diff --git a/.github/workflows/RunTests.yml b/.github/workflows/RunTests.yml index 81271cbf..7bda6611 100644 --- a/.github/workflows/RunTests.yml +++ b/.github/workflows/RunTests.yml @@ -17,7 +17,7 @@ jobs: strategy: fail-fast: false matrix: - julia-version: ['lts', '1', 'pre'] + julia-version: ['lts', '1', 'pre', '1.13-nightly', 'nightly'] os: [ubuntu-latest, macOS-latest, windows-latest] steps: diff --git a/CHANGELOG.md b/CHANGELOG.md index 9ecde669..e82ce9ac 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,27 @@ # Changelog +## Unreleased + +### Bugfixes +- Fix the reorg (passthrough) layer and the batchnorm read order for pre-0.2 darknet weight + headers, and apply softmax (not sigmoid) to region-layer class scores when the cfg enables + `softmax=1` (as the bundled v2 cfgs do; with `softmax=0` class scores are now left linear, + matching darknet). Together these fix `v2_COCO` (previously failed to load) and + `v2_tiny_COCO` (previously deviated from darknet); both now match AlexeyAB darknet output + and are enabled in the test suite. +- Fix `overridecfg!`/`cfgchanges` targeting any layer other than `:net` (e.g. + `(:yolo, 1, :classes, n)`), which previously errored or edited the wrong block. +- Fix soft-NMS (`nms_kind=soft`): decayed scores are now written back into the results, + duplicate keeps are avoided, and boxes whose decayed score falls below `detect_thresh` + are pruned from the returned detections. +- Fix the class-score maximum (CPU and CUDA) scanning one row past the last class into a + zero-filled scratch attribute; with non-positive class scores (e.g. region `softmax=0`) + the reported class index could point one past the last real class. +- Fix `prepare_image!` returning a bare 1-channel array (no padding tuple) for matching-size + 2D `Float32` inputs. +- CUDA: fix the last detection being dropped in `keepdetections`, an invalid class index + when all class scores are non-positive, and a `>`/`>=` threshold boundary mismatch vs CPU. + ## v0.2 ### Breaking changes diff --git a/Project.toml b/Project.toml index 2359bbc3..b185d6f5 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,10 @@ name = "ObjectDetector" uuid = "3dfc1049-5314-49cf-8447-288dfd02f9fb" -authors = ["Robert Luciani"] version = "1.1.2" +authors = ["Robert Luciani"] + +[workspace] +projects = ["test"] [deps] Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" @@ -10,6 +13,7 @@ BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" Cairo = "159f3aea-2a34-519c-b102-8c37f9878175" Colors = "5ae59095-9a9b-59fe-a467-6f913c188581" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196" ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534" ImageDraw = "4381153b-2b60-58ae-a1ba-fd683676385f" ImageFiltering = "6a3955dd-da59-5b1f-98d4-e7296123deb5" @@ -24,6 +28,9 @@ UnsafeArrays = "c4a57d5a-5b31-53a6-b365-19f8c011fbd6" CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" +[sources] +Functors = {rev = "master", url = "https://github.com/FluxML/Functors.jl"} + [extensions] CUDAExt = ["CUDA", "cuDNN"] @@ -34,33 +41,16 @@ BenchmarkTools = "0.4, 0.5, 0.6, 0.7, 1.0" CUDA = "4, 5, 6" Cairo = "1.1.1" Colors = "0.13.0" -Darknet = "0.5.0" -FileIO = "1" Flux = "0.12, 0.13, 0.14.1, 0.15, 0.16" +Functors = "0.5" ImageCore = "0.8, 0.9, 0.10" ImageDraw = "0.2" ImageFiltering = "0.6, 0.7" -ImageIO = "0.6" ImageTransformations = "0.8, 0.9, 0.10" LazyArtifacts = "1.3" PrecompileTools = "1" PrettyTables = "2.0, 3" -ReferenceTests = "0.10" TimerOutputs = "0.5.28, 1" UnsafeArrays = "1.0.6" cuDNN = "1, 2, 3, 4, 5, 6" julia = "1.10" - -[extras] -CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" -Darknet = "e2912957-7d06-5673-a7d6-96d153624877" -FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" -ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19" -OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" -ReferenceTests = "324d217c-45ce-50fc-942e-d289b448e8cf" -Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb" -Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" -cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" - -[targets] -test = ["CUDA", "cuDNN", "Darknet", "FileIO", "ImageIO", "OrderedCollections", "ReferenceTests", "Test", "Suppressor"] diff --git a/README.md b/README.md index 1b6c36b7..3b746ae9 100755 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ Supported YOLO models are: `v2`, `v2-tiny`, `v3`, `v3-spp`, `v3-tiny`, `v4`, `v4 Other less standard models may work also. -Note that v3+ models have result parity with [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet), and are directly tested against [Darknet.jl](https://github.com/IanButterworth/Darknet.jl) (see tests) +Note that all supported models have result parity with [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet), and are directly tested against [Darknet.jl](https://github.com/IanButterworth/Darknet.jl) (see tests) Training using ObjectDetector is currently unproven/untested. @@ -18,7 +18,7 @@ Requires julia v1.10+. From the Julia REPL, type `]` to enter the Pkg REPL mode pkg> add ObjectDetector ``` -As of ObjectDetector v0.3, if you want to use CUDA accelleration you will also need to +As of ObjectDetector v0.3, if you want to use CUDA acceleration you will also need to add `CUDA` and `cuDNN` to your project and load both packages. ## Usage @@ -29,7 +29,7 @@ add `CUDA` and `cuDNN` to your project and load both packages. ```julia using ObjectDetector, FileIO, ImageIO -yolomod = YOLO.v3_608_COCO(batch=1, silent=true) # Load the YOLOv3-tiny model pretrained on COCO, with a batch size of 1 +yolomod = YOLO.v3_608_COCO(batch=1, silent=true) # Load the 608x608 YOLOv3 model pretrained on COCO, with a batch size of 1 batch = emptybatch(yolomod) # Create a batch object. Automatically uses the GPU if available @@ -62,10 +62,10 @@ dimension is an integer multiple of the filter size of the first conv layer (typ On CPU an `AllocArrays` & `Adapt` - based allocator is used to reduce allocations. -To opt out of the allocator use `disable_bumper=true`. +To opt out of the allocator use `disallow_bumper=true`. i.e. ```julia -yolomod = YOLO.v3_608_COCO(batch=1, disable_bumper=true) +yolomod = YOLO.v3_608_COCO(batch=1, disallow_bumper=true) ``` ### Visualizing the result @@ -80,7 +80,7 @@ save("result.png", imgBoxes) The darknet YOLO models from https://pjreddie.com/darknet/yolo/ that are pretrained on the COCO dataset are available: ```julia -YOLO.v2_COCO() #Currently broken (weights seem bad, model may work with custom weights) +YOLO.v2_COCO() YOLO.v2_tiny_COCO() YOLO.v3_COCO() diff --git a/ext/CUDAExt.jl b/ext/CUDAExt.jl index fdaba783..8ab10c8c 100644 --- a/ext/CUDAExt.jl +++ b/ext/CUDAExt.jl @@ -23,7 +23,8 @@ function kern_clipdetect(input::CuDeviceArray, conf::Float32) idx = (blockIdx().x-1) * blockDim().x + threadIdx().x cols = gridDim().x if idx <= cols - @inbounds input[end-2, idx] = ifelse(input[end-2, idx] > conf, input[end-2, idx], Float32(0.0)) + # keep values >= conf, matching the CPU clipdetect! boundary behavior + @inbounds input[end-2, idx] = ifelse(input[end-2, idx] >= conf, input[end-2, idx], Float32(0.0)) end return end @@ -31,15 +32,19 @@ end function findmax!(input::CuArray) rows, cols = size(input) - idst, idend = 6, rows - 3 + # class scores live in rows 6:end-4; rows end-3:end are the appended + # scratch attributes and must not participate in the max + idst, idend = 6, rows - 4 @cuda blocks=cols threads=rows kern_findmax!(input, idst, idend) end function kern_findmax!(input::CuDeviceMatrix{T}, idst::Integer, idend::Integer) where {T} if threadIdx().x == idend j = blockIdx().x - val = zero(T) - idx = zero(T) - for i in idst:idend + # initialize with the first candidate so the first maximum wins, + # matching CPU findmax semantics even when all scores are <= 0 + val = input[idst, j] + idx = idst + for i in (idst+1):idend if input[i, j] > val val = input[i, j] idx = i @@ -80,7 +85,7 @@ end function kern_genbools(input::CuDeviceArray, output::CuDeviceArray) col = (blockIdx().x-1) * blockDim().x + threadIdx().x cols = gridDim().x - if col < cols && input[end-2, col] > Float32(0) + if col <= cols && input[end-2, col] > Float32(0) @inbounds output[col] = Int32(1) end return diff --git a/src/prepareimage.jl b/src/prepareimage.jl index e8f57f04..fdcdf02b 100644 --- a/src/prepareimage.jl +++ b/src/prepareimage.jl @@ -124,7 +124,7 @@ function prepare_image!(dest_arr::AbstractArray{Float32}, img::AbstractArray{Flo elseif ndims(img) == 3 && size(img)[[2,1,3]] == size(dest_arr) return (maybe_gpu(PermutedDimsArray(img, [2,1,3])), [0,0,0,0]) elseif ndims(img) == 2 && size(img)[[2,1]] == size(dest_arr)[1:2] - return (maybe_gpu(reshape(PermutedDimsArray(img, [2,1]), size(img,2), size(img, 1), 1))) + return (maybe_gpu(repeat(reshape(PermutedDimsArray(img, [2,1]), size(img,2), size(img,1), 1), outer=[1,1,size(dest_arr,3)])), [0,0,0,0]) elseif ndims(img) == 2 return prepare_image!(dest_arr, colorview(Gray, img), kern; use_gpu) elseif size(img, 1) == 1 diff --git a/src/yolo/nms.jl b/src/yolo/nms.jl index 41b0bc41..cc67c8f3 100644 --- a/src/yolo/nms.jl +++ b/src/yolo/nms.jl @@ -66,10 +66,11 @@ function bboxiou!(out::AbstractArray{T}, box1, box2; distance::Bool=false, beta end """ - nms(dets, iou_thresh; kind=:default, beta=0.6f0) + nms!(dets, iou_thresh; kind=:default, beta=0.6f0) Performs Non-Maximum Suppression (NMS) on a set of detection boxes `dets`, returning the indices -of boxes to keep. This function supports multiple NMS strategies: +of boxes to keep. For `kind = :soft`, `dets` is mutated: decayed scores are written back into +row `end-2`. This function supports multiple NMS strategies: Arguments: - `dets`: A matrix of shape (≥5, N), where each column represents a detection. @@ -82,7 +83,10 @@ Keyword Arguments: - `:default` (default): traditional hard-threshold NMS - `:greedynms` score-decay using IoU penalty (`score *= 1 - IoU`) with fixed beta of 0.6 - `:diounms`: score-decay using IoU penalty (`score *= 1 - IoU`) - - `:soft`: Soft-NMS using exponential decay (`score *= exp(-IoU^2 / beta)`) + - `:soft`: Soft-NMS using exponential decay (`score *= exp(-IoU^2 / beta)`). + Keeps all boxes; decayed scores are written back into row `end-2` of + `dets` (i.e. `dets` is mutated), and pruning is left to the caller's + score threshold. - `beta` (`Float32`): smoothing factor for soft-NMS (default `0.6`) Returns: @@ -90,7 +94,7 @@ Returns: See https://github.com/AlexeyAB/darknet/blob/9d40b619756be9521bc2ccd81808f502daaa3e9a/src/box.c#L195 """ -function nms(dets::AbstractArray{T}, iou_thresh; kind::Symbol = :default, beta::T = T(0.6)) where T +function nms!(dets::AbstractArray{T}, iou_thresh; kind::Symbol = :default, beta::T = T(0.6)) where T N = size(dets, 2) idxs = similar(dets, Int, N) @inbounds for j in 1:N @@ -128,21 +132,29 @@ function nms(dets::AbstractArray{T}, iou_thresh; kind::Symbol = :default, beta:: idxs[write_idx] = idxs[j+1] end end - elseif kind === :soft # untested + elseif kind === :soft + # Soft-NMS (Bodla et al. 2017), gaussian variant: no box is removed; + # overlapping boxes have their scores decayed (written back into + # `dets`) and final pruning is left to the caller's score threshold. @inbounds for j in 1:b2_len + col = idxs[j+1] decay = exp(-(ious[j]^2) / beta) - scores[idxs[j+1]] *= decay + scores[col] *= decay + dets[end-2, col] = scores[col] end - @inbounds for j in 2:idx_len - key = idxs[j] - k = j - 1 - while k >= 1 && scores[idxs[k]] < scores[key] - idxs[k + 1] = idxs[k] - k -= 1 + # compact survivors down one slot and swap the top decayed score to + # the front; only the per-round argmax matters for the keep order + best = 1 + @inbounds for j in 1:b2_len + idxs[j] = idxs[j+1] + if scores[idxs[j]] > scores[idxs[best]] + best = j end - idxs[k + 1] = key end - write_idx = idx_len - 1 + if best != 1 + @inbounds idxs[1], idxs[best] = idxs[best], idxs[1] + end + write_idx = b2_len else error("Unknown NMS kind: $kind") end @@ -152,12 +164,17 @@ function nms(dets::AbstractArray{T}, iou_thresh; kind::Symbol = :default, beta:: end """ - perform_detection_nms(batchout, overlap_thresh, batchsize) + perform_detection_nms(batchout, overlap_thresh, batchsize; kind, beta, detect_thresh) For each batch `b` in `1:batchsize`, extract the detections from `batchout`, group them by class, sort each group by the end-2 column (class confidence score) descending, and run NMS to remove duplicates using bboxiou and overlap_thresh. +`detect_thresh` re-applies the caller's score threshold to the kept boxes. +This only matters for `kind = :soft`, where scores are decayed during NMS and +boxes that fall below the original detection threshold must be pruned (the +other kinds only ever return boxes that already passed the threshold). + Returns a Vector of detection matrices, each of size (num_fields, kept_boxes). The input `batchout` is a 2D array of shape (num_fields, N), where `N` is the @@ -171,7 +188,7 @@ batchout rows: - end-1: the class index - The last row is the batch index """ -function perform_detection_nms(batchout, overlap_thresh, batchsize::Int; kind::Symbol=:default, beta::Float32=0.6f0) +function perform_detection_nms(batchout, overlap_thresh, batchsize::Int; kind::Symbol=:default, beta::Float32=0.6f0, detect_thresh::Float32=0f0) output = similar(batchout) i = 1 # index for writing into `output` @@ -204,9 +221,10 @@ function perform_detection_nms(batchout, overlap_thresh, batchsize::Int; kind::S # nms takes views of sorted_dets and copying here results in lower allocs and faster nms sorted_dets = dets[:, sorted_idx] - keep = nms(sorted_dets, overlap_thresh; kind, beta) + keep = nms!(sorted_dets, overlap_thresh; kind, beta) @inbounds for k in keep + sorted_dets[end-2, k] < detect_thresh && continue # soft-NMS may have decayed the score below threshold output[:, i] = sorted_dets[:, k] i += 1 end diff --git a/src/yolo/yolo.jl b/src/yolo/yolo.jl index b184884f..ba0c97ab 100644 --- a/src/yolo/yolo.jl +++ b/src/yolo/yolo.jl @@ -70,7 +70,7 @@ end Read the YOLO binary weights """ -function readweights(bytes::Union{IOBuffer,Nothing}, kern::Int, ch::Int, fl::Int, bn::Bool; old_darknet::Bool=false) +function readweights(bytes::Union{IOBuffer,Nothing}, kern::Int, ch::Int, fl::Int, bn::Bool) function read_array(io::IOBuffer, n::Int) expected = n * sizeof(Float32) data = read(io, expected) @@ -81,15 +81,10 @@ function readweights(bytes::Union{IOBuffer,Nothing}, kern::Int, ch::Int, fl::Int end dummy = isnothing(bytes) if bn - if old_darknet - # PJReddie Darknet: scales, biases, means, vars - bw = dummy ? ones(Float32, fl) : read_array(bytes, fl) # weights (scale) - bb = dummy ? ones(Float32, fl) : read_array(bytes, fl) # bias - else - # AlexeyAB fork: biases, scales, means, vars - bb = dummy ? ones(Float32, fl) : read_array(bytes, fl) # bias - bw = dummy ? ones(Float32, fl) : read_array(bytes, fl) # weights (scale) - end + # Both PJReddie darknet and the AlexeyAB fork write, for every + # darknet version: biases, scales, means, vars (load_convolutional_weights) + bb = dummy ? ones(Float32, fl) : read_array(bytes, fl) # bias + bw = dummy ? ones(Float32, fl) : read_array(bytes, fl) # weights (scale) bm = dummy ? ones(Float32, fl) : read_array(bytes, fl) # mean bv = dummy ? ones(Float32, fl) : read_array(bytes, fl) # variance cb = zeros(Float32, fl) # conv bias (zero when BN is used) @@ -248,14 +243,22 @@ end """ reorg(a, stride) -Reshapes feature map - decreases size and increases number of channels, without -changing elements. stride=2 mean that width and height will be decreased by 2 -times, and number of channels will be increased by 2x2 = 4 times, so the total -number of element will still the same: width_old*height_old*channels_old = width_new*height_new*channels_new +Reorg (passthrough) layer as used by YOLOv2: decreases width and height by a +factor of `stride` and increases channels by a factor of `stride^2`, keeping +the total element count. + +This replicates darknet's legacy `[reorg]` layer exactly (`reorg_cpu` with +`forward=0` as called by `forward_reorg_old_layer` for `reverse=0`), including +its idiosyncratic element ordering, since pretrained weights depend on it. See +https://github.com/AlexeyAB/darknet/blob/9d40b619756be9521bc2ccd81808f502daaa3e9a/src/blas.c#L10 """ -function reorg(a, stride) - w, h, c = size(a) - return reshape(a, (w // stride, h // stride, c*(stride^2))) +function reorg(a::AbstractArray{<:Any,4}, stride::Integer) + w, h, c, b = size(a) + @assert w % stride == 0 && h % stride == 0 && c % stride^2 == 0 "reorg with stride $stride requires width & height divisible by $stride and channels divisible by $(stride^2), got ($w, $h, $c)" + in_c = c ÷ (stride * stride) + x6 = reshape(a, stride, w, stride, h, in_c, b) + o6 = permutedims(x6, (2, 4, 5, 1, 3, 6)) + return reshape(o6, w ÷ stride, h ÷ stride, c * stride * stride, b) end """ @@ -269,7 +272,7 @@ i.e. function overridecfg!(cfgvec::Vector{Pair{Symbol,Dict{Symbol,T}}}, cfgchanges::Vector{Tuple{Symbol,Int,Symbol,U}}; silent::Bool = false) where {T,U} - layers = map(x->first(x), cfgchanges) + layers = map(first, cfgvec) for cfgchange in cfgchanges layer_idxs = findall(layers .== cfgchange[1]) length(layer_idxs) < cfgchange[2] && error("Number of $(cfgchange[1]) layers found ($(length(layer_idxs))) less than desired ($(cfgchange[2])).") @@ -302,91 +305,14 @@ _route(val, channels) = x -> val[:, :, channels, :] _add(val, act) = x -> broadcast!((a, b) -> act(a + b), x, x, val) _cat(arrays::AbstractArray...) = x -> cat(arrays...; dims=3) -flux_maxpool = true -@static if flux_maxpool - ## Flux maxpool approach - function _maxpool(siz, stride) - # For a 2x2 pool, use explicit padding to preserve dimensions. - pad = siz == 2 && stride == 1 ? (0, 1, 0, 1) : div(siz - 1, 2) - return x -> maxpool(x; siz, stride, pad) - end - function maxpool(x; siz, stride, pad) - return Flux.maxpool(x, Flux.PoolDims(x, (siz, siz); stride = (stride, stride), padding = pad)) - end -else - ## Direct copy of darknet maxpool approach - function _maxpool(siz, stride) - pad = if siz == 2 && stride == 1 - # For a 2×2 pool with stride=1, pad asymmetrically so that - # for an odd input (e.g. 13) the effective input becomes 14, - # producing an output of 13. - 1 - elseif siz == 2 && stride == 2 - 0 - else - div(siz, 2) - end - return x -> darknet_maxpool_layer(x, siz, (stride, stride), pad) - end - function maxpool(x::AbstractArray{Float32,4}, - siz::Int, - stride::Tuple{Int,Int}, - pad::Int; - return_indexes::Bool=false) - # x: input array with dimensions (H, W, C, N) - # siz: pooling window size (e.g., 2) - # stride: (stride_y, stride_x) - # pad: total padding (as in Darknet, where often for 2×2, stride=1, pad is set so that the - # effective input is increased asymetrically) - # return_indexes: if true, also return the indexes of the max values. - H, W, C, N = size(x) - stride_y, stride_x = stride - out_h = div(H + pad - siz, stride_y) + 1 - out_w = div(W + pad - siz, stride_x) + 1 - - # Allocate output; note we set the pool default to -Inf - y = fill(-Inf32, out_h, out_w, C, N) - idx = return_indexes ? similar(y, Int) : nothing - - # Compute offsets as in Darknet: - # h_offset = -l.pad/2, w_offset = -l.pad/2. - h_offset = -div(pad, 2) - w_offset = -div(pad, 2) - - # Loop over batch, channel, and output spatial locations. - # In Darknet, the loops are ordered as: batch, channel, out_h, out_w - for b in 1:N - for k in 1:C - for i in 1:out_h - for j in 1:out_w - max_val = -Inf32 - max_index = -1 # default (could be left as -1 if no valid element is found) - # Loop over the pooling window: - for n in 0:(siz-1) - for m in 0:(siz-1) - # Compute current position, adjusting for 1-indexed Julia arrays: - cur_h = h_offset + (i - 1) * stride_y + n + 1 - cur_w = w_offset + (j - 1) * stride_x + m + 1 - if cur_h >= 1 && cur_h <= H && cur_w >= 1 && cur_w <= W - val = x[cur_h, cur_w, k, b] - if val > max_val - max_val = val - # Save linear index (or you could choose to store a CartesianIndex) - max_index = LinearIndices(x)[CartesianIndex(cur_h, cur_w, k, b)] - end - end - end - end - y[i, j, k, b] = max_val - if return_indexes - idx[i, j, k, b] = max_index - end - end - end - end - end - return return_indexes ? (y, idx) : y - end +## Flux maxpool approach +function _maxpool(siz, stride) + # For a 2x2 pool, use explicit padding to preserve dimensions. + pad = siz == 2 && stride == 1 ? (0, 1, 0, 1) : div(siz - 1, 2) + return x -> maxpool(x; siz, stride, pad) +end +function maxpool(x; siz, stride, pad) + return Flux.maxpool(x, Flux.PoolDims(x, (siz, siz); stride = (stride, stride), padding = pad)) end ######################################################## @@ -470,7 +396,7 @@ mutable struct Yolo <: AbstractModel acts[cfg_idx] = block[:activation] bn = haskey(block, :batch_normalize) cw, cb, bb, bw, bm, bv = try - readweights(weightbytes, kern, ch[end], filters, bn; old_darknet) + readweights(weightbytes, kern, ch[end], filters, bn) catch !silent && println() @error "Error reading weights for layer $cfg_idx of type $blocktype. Check the weights file." kern ch[end] filters pad stride act bn @@ -498,8 +424,8 @@ mutable struct Yolo <: AbstractModel !silent && prettyprint(["($cfg_idx) ","upsample($stride)"," => "],[:blue,:magenta,:green]) elseif blocktype === :reorg stride = block[:stride] - push!(fn, _reorg(stride)) # reorg (reshape to (w/stride, h/stride, c*stride^2)) - push!(ch, ch[end]) + push!(fn, _reorg(stride)) # reorg to (w/stride, h/stride, c*stride^2) + push!(ch, ch[end] * stride^2) !silent && prettyprint(["($cfg_idx) ","reorg($stride)"," => "],[:blue,:magenta,:green]) elseif blocktype === :maxpool siz = block[:size] @@ -752,7 +678,9 @@ Findmax, get the class with highest confidence and class number out. """ function findmax!(input::AbstractArray{T}) where {T} @inbounds for i in axes(input, 2) - input[end-2, i], input[end-1, i] = findmax(@view input[6:end-3, i]) + # class scores live in rows 6:end-4; rows end-3:end are the appended + # scratch attributes and must not participate in the max + input[end-2, i], input[end-1, i] = findmax(@view input[6:end-4, i]) end end @@ -832,9 +760,20 @@ function (yolo::Yolo)(img::T; detect_thresh=nothing, overlap_thresh=nothing, sho weights[:, :, 3:4, :, :] = exp.(weights[:, :, 3:4, :, :]) .* out[:anchor] end - # Apply sigmoid to objectness (5) and class scores (6:a) ONLY if the - # preceding conv layer activation was NOT logistic (e.g., it was linear) - if out[:final_conv_activation] != "logistic" + if yolo.cfg[:laststage] === :region + # The region layer (yolov2) applies logistic to objectness, and + # softmax over the class scores only when the cfg sets softmax=1; + # with softmax=0 darknet leaves the class scores linear + # (forward_region_layer) + weights[:, :, 5, :, :] = σ.(weights[:, :, 5, :, :]) + if get(yolo.cfg[:output][outnr], :softmax, 0) != 0 + cls = weights[:, :, 6:end, :, :] # a view, via the enclosing @views + cls .= exp.(cls .- maximum(cls, dims=3)) + cls ./= sum(cls, dims=3) + end + elseif out[:final_conv_activation] != "logistic" + # Apply sigmoid to objectness (5) and class scores (6:a) ONLY if the + # preceding conv layer activation was NOT logistic (e.g., it was linear) weights[:, :, 5:end, :, :] = σ.(weights[:, :, 5:end, :, :]) end @@ -861,10 +800,11 @@ function (yolo::Yolo)(img::T; detect_thresh=nothing, overlap_thresh=nothing, sho weights[:, :, 3, :, :] = weights[:, :, 1, :, :] .+ weights[:, :, 3, :, :] #x2 weights[:, :, 4, :, :] = weights[:, :, 2, :, :] .+ weights[:, :, 4, :, :] #y2 - # add additional attributes for post-inference analysis: confidence, classnr, outnr, batchnr + # add 4 additional attributes for post-inference analysis. After findmax! + # below they hold: (unused), best class confidence (end-2), + # best class index (end-1), batch number (end) weights = extend_for_attributes(weights, w, h, bo, ba) - weights[:, :, a+3, outnr, :] .= outnr # write output number to attribute a+3 for batch in 1:ba weights[:, :, a+4, :, batch] .= batch end # write batchnumber to attribute a+4 weights = permutedims(weights, [3, 1, 2, 4, 5]) # place attributes first weights = reshape(weights, a+4, :) # reshape to attr, data @@ -887,7 +827,7 @@ function (yolo::Yolo)(img::T; detect_thresh=nothing, overlap_thresh=nothing, sho overlap_thresh = Float32(@something overlap_thresh yolo.out[1][:ignore_thresh]) nms_kind = yolo.out[1][:nms_kind] beta_nms = yolo.out[1][:beta_nms] - ret = perform_detection_nms(batchout, overlap_thresh, batchsize; kind=nms_kind, beta=beta_nms) + ret = perform_detection_nms(batchout, overlap_thresh, batchsize; kind=nms_kind, beta=beta_nms, detect_thresh=Float32(detect_thresh)) end end end diff --git a/test/Project.toml b/test/Project.toml new file mode 100644 index 00000000..fd0eafcc --- /dev/null +++ b/test/Project.toml @@ -0,0 +1,20 @@ +[deps] +CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba" +Darknet = "e2912957-7d06-5673-a7d6-96d153624877" +FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534" +ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19" +ObjectDetector = "3dfc1049-5314-49cf-8447-288dfd02f9fb" +OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" +PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" +ReferenceTests = "324d217c-45ce-50fc-942e-d289b448e8cf" +Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb" +Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" +cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" + +[compat] +Darknet = "0.5.0" +FileIO = "1" +ImageIO = "0.6" +ReferenceTests = "0.10" diff --git a/test/maintests.jl b/test/maintests.jl index 88e4373c..3a85e3ad 100644 --- a/test/maintests.jl +++ b/test/maintests.jl @@ -4,31 +4,18 @@ oThresh = 0.5 #Overlap Threshold (maximum acceptable IoU) psnr_thresh = 35.0 +# Resolve artifacts through the package itself: `artifact""` cannot be used in +# test files because the search for Artifacts.toml stops at test/Project.toml @testset "Download all artifacts" begin - # artifact"yolov2-COCO" # broken, see below - # artifact"yolov2-tiny-COCO" # broken, see below - artifact"yolov3-COCO" - artifact"yolov3-spp-COCO" - artifact"yolov3-tiny-COCO" - artifact"yolov4-COCO" - artifact"yolov4-tiny-COCO" - artifact"yolov7-COCO" - artifact"yolov7-tiny-COCO" + for (name, files) in sort(collect(YOLO.YOLO_MODELS), by = first) + cfgfile, weightsfile = files() + @test isfile(weightsfile) + end end Darknet.download_defaults() -const skip_models = ( - "v2_COCO", # TODO: Not all weights are read during load: Read 196856372 bytes. Filesize 203934260 bytes - "v2_tiny_COCO", # TODO: Figure out why results differ - # "v3_COCO", - # "v3_tiny_COCO", - # "v3_spp_COCO", - # "v4_COCO", - # "v4_tiny_COCO", - # "v7_COCO", - # "v7_tiny_COCO", -) +const skip_models = () const testimages = ["dog-cycle-car", "dog-cycle-car_nonsquare"] const namesfile = joinpath(ObjectDetector.YOLO.models_dir(), "coco.names") @@ -163,7 +150,30 @@ end end end +@testset "NMS" begin + # two heavily-overlapping boxes and one distinct box, all one class + # rows: x1, y1, x2, y2, objectness, class score, class id, batch id + dets = Float32[0.1 0.12 0.6; 0.1 0.12 0.6; 0.3 0.32 0.8; 0.3 0.32 0.8; + 1.0 1.0 1.0; 0.9 0.8 0.7; 2.0 2.0 2.0; 1.0 1.0 1.0] + keep = ObjectDetector.YOLO.nms!(copy(dets), 0.5f0; kind=:soft, beta=0.6f0) + @test sort(keep) == [1, 2, 3] # soft-NMS keeps all boxes, only decays scores + out = ObjectDetector.YOLO.perform_detection_nms(copy(dets), 0.5f0, 1; kind=:soft, beta=0.6f0, detect_thresh=0.5f0) + @test size(out, 2) == 2 # the overlapped box decayed below detect_thresh and is pruned + @test out[end-2, :] ≈ Float32[0.9, 0.7] + out_def = ObjectDetector.YOLO.perform_detection_nms(copy(dets), 0.5f0, 1; kind=:default) + @test size(out_def, 2) == 2 # hard NMS suppresses the overlapped box outright +end + @testset "Custom cfg's" begin + @testset "overridecfg! non-net layers" begin + cfgvec = ObjectDetector.YOLO.cfgread(joinpath(ObjectDetector.YOLO.models_dir(), "yolov3.cfg")) + ObjectDetector.YOLO.overridecfg!(cfgvec, [(:yolo, 3, :classes, 2), (:net, 1, :width, 512)]) + yolos = [last(p) for p in cfgvec if first(p) === :yolo] + @test length(yolos) == 3 + @test yolos[3][:classes] == 2 + @test yolos[1][:classes] == 80 + @test cfgvec[1][2][:width] == 512 + end @testset "Valid non-square dimensions (512x384)" begin img = load(joinpath(@__DIR__,"images","dog-cycle-car.png")) yolomod = YOLO.v3_COCO(silent=true, cfgchanges=[(:net, 1, :width, 512), (:net, 1, :height, 384)]) diff --git a/test/prepare_image.jl b/test/prepare_image.jl index 3b171bf3..17200271 100644 --- a/test/prepare_image.jl +++ b/test/prepare_image.jl @@ -29,6 +29,12 @@ batch[:,:,:,1], padding = prepare_image(rand(Gray, 500, 200), yolomod) @test true end + @testset "direct prepare_image! with matching-size 2D Float32" begin + dest = zeros(Float32, 416, 416, 3) + arr, padding = prepare_image!(dest, rand(Float32, 416, 416), nothing; use_gpu=false) + @test size(arr) == (416, 416, 3) + @test padding == [0, 0, 0, 0] + end @testset "2D RGB" begin batch[:,:,:,1], padding = prepare_image(rand(RGB, 416, 416), yolomod) batch[:,:,:,1], padding = prepare_image(rand(RGB, 500, 500), yolomod) diff --git a/test/resrefs.jl b/test/resrefs.jl index febb016f..6cd7e83b 100644 --- a/test/resrefs.jl +++ b/test/resrefs.jl @@ -1,2204 +1,2738 @@ const RES_REFS = OrderedDict{String, Matrix{Float32}}( - "dn_v2_tiny_COCO_dog-cycle-car" => [ - 0.11871345 0.19095461 0.8690704; - 0.24898383 0.105363406 0.05126566; - 0.7444068 1.1171391 1.0070908; - 1.0466365 0.7709561 0.2240009; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.62716407 0.69729006 0.68624246; - 17.0 2.0 3.0; + "dn_v2_COCO_dog-cycle-car" => Float32[ + 0.18849395 0.24314514 0.8879252 + 0.16923633 0.315379 0.05479352 + 0.98268044 0.59957844 1.0059307 + 0.6888683 0.8909184 0.21899809 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.78942966 0.70303994 0.7141917 + 2.0 17.0 3.0 + 0.0 0.0 0.0 + ], + "dn_v2_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.16893783 0.24524125 + 0.15931235 0.23013915 + 0.98755896 0.59691036 + 0.5386595 0.7868471 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.6410263 0.83778614 + 2.0 17.0 + 0.0 0.0 + ], + "dn_v2_tiny_COCO_dog-cycle-car" => Float32[ + 0.11871345 0.19095461 0.8690704 + 0.24898383 0.105363406 0.05126566 + 0.7444068 1.1171391 1.0070908 + 1.0466365 0.7709561 0.2240009 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.62716407 0.69729006 0.68624246 + 17.0 2.0 3.0 + 0.0 0.0 0.0 + ], + "dn_v2_tiny_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.15290764 0.20614062 + 0.13900016 0.1033171 + 0.71046585 1.101412 + 0.86544985 0.6217896 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.87685555 0.56319904 + 17.0 2.0 + 0.0 0.0 + ], + "dn_v3_COCO_dog-cycle-car" => Float32[ + 0.19417045 0.25129163 0.87945247 + 0.11786421 0.34023315 0.057231095 + 1.0063193 0.5869943 1.0014776 + 0.7645824 0.9384274 0.21131793 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.9988289 0.99910194 0.94670224 + 2.0 17.0 3.0 + 0.0 0.0 0.0 + ], + "dn_v3_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.17121516 0.2476446 + 0.119167514 0.2321015 + 1.0063298 0.5886883 + 0.6026426 0.79089874 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.9335599 0.9995055 + 2.0 17.0 + 0.0 0.0 + ], + "dn_v3_spp_COCO_dog-cycle-car" => Float32[ + 0.19689927 0.24459465 0.8887503 + 0.14884625 0.36018226 0.06057044 + 1.047396 0.5927578 1.0014724 + 0.7096074 0.93162817 0.23094009 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.9104581 0.98495084 0.71383667 + 2.0 17.0 3.0 + 0.0 0.0 0.0 + ], + "dn_v3_spp_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.18823436 0.24413626 + 0.16247772 0.2306887 + 1.0094358 0.59887177 + 0.6820583 0.8014147 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.74724454 0.98902136 + 2.0 17.0 + 0.0 0.0 + ], + "dn_v3_tiny_COCO_dog-cycle-car" => Float32[ + 0.2507026 0.86342007 0.9034787 0.90618366 + 0.31197226 0.048579667 0.093155935 0.06912032 + 0.63642 0.9952956 0.98370415 0.9592237 + 0.87765443 0.22791807 0.17728068 0.20277499 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.0 0.0 0.0 0.0 + 0.71402943 0.5302138 0.6647044 0.6010517 + 17.0 3.0 3.0 3.0 + 0.0 0.0 0.0 0.0 + ], + "dn_v3_tiny_COCO_dog-cycle-car_nonsquare" => Float32[0.2381582; 0.22545269; 0.6233993; 0.7690958; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.7681297; 17.0; 0.0;;], + "dn_v4_COCO_dog-cycle-car" => Float32[ + 0.24185833 0.24869768 0.884411 + 0.14170478 0.3591396 0.057404205 + 1.0096805 0.5886977 0.9998628 + 0.7194481 0.9311235 0.22066805 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.92888606 0.98138404 0.95234597 + 2.0 17.0 3.0 + 0.0 0.0 0.0 + ], + "dn_v4_COCO_dog-cycle-car_nonsquare" => Float32[0.24385601; 0.21881355; 0.59973025; 0.8213513; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.74570936; 17.0; 0.0;;], + "dn_v4_tiny_COCO_dog-cycle-car" => Float32[ + 0.25264323 0.88405293 + 0.33418566 0.06083686 + 0.60824335 1.0000688 + 0.8501826 0.2110069 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.80551463 0.8853552 + 17.0 3.0 + 0.0 0.0 ], - "dn_v3_COCO_dog-cycle-car" => [ - 0.19417045 0.25129163 0.87945247; - 0.11786421 0.34023315 0.057231095; - 1.0063193 0.5869943 1.0014776; - 0.7645824 0.9384274 0.21131793; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.9988289 0.99910194 0.94670224; - 2.0 17.0 3.0; + "dn_v4_tiny_COCO_dog-cycle-car_nonsquare" => Float32[0.25392982; 0.19908307; 0.61074865; 0.7779487; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.872691; 17.0; 0.0;;], + "dn_v7_COCO_dog-cycle-car" => Float32[ + 0.23815057 0.24645366 0.88601756 + 0.16355096 0.3362234 0.057299808 + 0.99730533 0.58837694 0.9996812 + 0.7102827 0.9375947 0.2177062 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.9809589 0.97152036 0.72744626 + 2.0 17.0 3.0 0.0 0.0 0.0 ], - "dn_v3_COCO_dog-cycle-car_nonsquare" => [ - 0.17121516 0.2476446; - 0.119167514 0.2321015; - 1.0063298 0.5886883; - 0.6026426 0.79089874; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.9335599 0.9995055; - 2.0 17.0; + "dn_v7_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.23688027 0.24578272 + 0.16262104 0.21957606 + 0.9971996 0.5866954 + 0.59032714 0.8174101 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 0.0 0.0 - ], - "dn_v3_spp_COCO_dog-cycle-car" => [ - 0.19689927 0.24459465 0.8887503; - 0.14884625 0.36018226 0.06057044; - 1.047396 0.5927578 1.0014724; - 0.7096074 0.93162817 0.23094009; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.9104581 0.98495084 0.71383667; - 2.0 17.0 3.0; - 0.0 0.0 0.0 - ], - "dn_v3_spp_COCO_dog-cycle-car_nonsquare" => [ - 0.18823436 0.24413626; - 0.16247772 0.2306887; - 1.0094358 0.59887177; - 0.6820583 0.8014147; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.74724454 0.98902136; - 2.0 17.0; + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.9743059 0.9687549 + 2.0 17.0 0.0 0.0 ], - "dn_v3_tiny_COCO_dog-cycle-car" => [ - 0.2507026 0.86342007 0.9034787 0.90618366; - 0.31197226 0.048579667 0.093155935 0.06912032; - 0.63642 0.9952956 0.98370415 0.9592237; - 0.87765443 0.22791807 0.17728068 0.20277499; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.0 0.0 0.0 0.0; - 0.71402943 0.5302138 0.6647044 0.6010517; - 17.0 3.0 3.0 3.0; - 0.0 0.0 0.0 0.0 - ], - "dn_v3_tiny_COCO_dog-cycle-car_nonsquare" => [ - 0.2381582; 0.22545269; 0.6233993; 0.7690958; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.7681297; 17.0; 0.0;; - ], - "dn_v4_COCO_dog-cycle-car" => [ - 0.24185833 0.24869768 0.884411; - 0.14170478 0.3591396 0.057404205; - 1.0096805 0.5886977 0.9998628; - 0.7194481 0.9311235 0.22066805; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.92888606 0.98138404 0.95234597; - 2.0 17.0 3.0; + "dn_v7_tiny_COCO_dog-cycle-car" => Float32[ + 0.23326866 0.24692088 0.87750936 + 0.16659053 0.32880515 0.057245042 + 0.99945194 0.5826077 0.99936235 + 0.695994 0.9460747 0.21617691 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.0 0.0 0.0 + 0.8781291 0.87099695 0.8488881 + 2.0 17.0 3.0 0.0 0.0 0.0 ], - "dn_v4_COCO_dog-cycle-car_nonsquare" => [ - 0.24385601; 0.21881355; 0.59973025; 0.8213513; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.74570936; 17.0; 0.0;; - ], - "dn_v4_tiny_COCO_dog-cycle-car" => [ - 0.25264323 0.88405293; - 0.33418566 0.06083686; - 0.60824335 1.0000688; - 0.8501826 0.2110069; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.80551463 0.8853552; - 17.0 3.0; + "dn_v7_tiny_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.21098383 0.24594077 + 0.1621059 0.21256444 + 0.9982124 0.5849901 + 0.57099396 0.8199513 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.7663366 0.90416116 + 2.0 17.0 0.0 0.0 ], - "dn_v4_tiny_COCO_dog-cycle-car_nonsquare" => [ - 0.25392982; 0.19908307; 0.61074865; 0.7779487; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.872691; 17.0; 0.0;; - ], - "dn_v7_COCO_dog-cycle-car" => [ - 0.23815057 0.24645366 0.88601756; - 0.16355096 0.3362234 0.057299808; - 0.99730533 0.58837694 0.9996812; - 0.7102827 0.9375947 0.2177062; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.9809589 0.97152036 0.72744626; - 2.0 17.0 3.0; + "od_v2_COCO_dog-cycle-car" => Float32[ + 0.18849403 0.24314502 0.8879252 + 0.16923633 0.31537876 0.05479352 + 0.98268026 0.5995785 1.0059307 + 0.6888683 0.8909186 0.21899806 + 0.79056317 0.75247735 0.7722787 + 1.893246e-5 4.6755395e-6 7.069486e-5 + 0.78942835 6.9823276e-5 3.8901762e-6 + 2.3752762e-5 4.2468133e-7 0.7141928 + 0.0007935507 2.3317257e-6 3.4653753e-5 + 4.0631956e-8 1.5494922e-7 1.6234786e-6 + 2.4805672e-6 1.6882954e-8 0.000264244 + 3.5351536e-7 8.789901e-8 6.726476e-6 + 5.6710187e-6 1.1657928e-7 0.057616036 + 2.8577811e-5 2.4836602e-6 6.667571e-6 + 9.036832e-9 5.767049e-8 1.0552276e-6 + 6.750502e-7 3.8758776e-6 1.0358257e-6 + 4.276634e-9 1.4145633e-7 1.1881611e-6 + 4.4794668e-8 4.7125542e-7 3.9412193e-6 + 0.00022821047 1.4311355e-5 1.0216371e-5 + 2.7570192e-7 6.465868e-5 2.41993e-7 + 2.4529634e-6 0.04906877 7.4602883e-7 + 3.3852575e-6 0.7030404 8.929461e-7 + 5.556965e-7 3.7183345e-6 1.9639072e-6 + 1.682536e-7 2.0264095e-5 5.4808106e-7 + 1.7127187e-8 6.235964e-6 1.1602776e-6 + 1.6963687e-8 8.615953e-8 4.665787e-7 + 1.412269e-8 1.0605225e-5 1.348163e-6 + 6.427144e-9 2.4380586e-7 8.173163e-7 + 1.17880035e-8 2.5853383e-6 3.229629e-7 + 2.3863765e-7 2.766442e-5 2.0296172e-6 + 4.359651e-6 1.1163129e-5 4.324758e-6 + 1.3736481e-7 9.847435e-6 2.5732263e-6 + 2.8372806e-9 9.631257e-7 1.2263035e-7 + 2.1858479e-7 2.9496498e-6 1.7869424e-6 + 1.36952e-7 1.7835507e-6 4.863121e-7 + 5.933732e-7 6.624745e-7 1.7593028e-7 + 3.143066e-8 1.1101509e-7 2.3633473e-7 + 7.586576e-8 3.5249013e-7 6.5414804e-7 + 1.783775e-7 5.905835e-7 4.9143966e-7 + 7.624205e-8 2.0849384e-7 2.5758376e-7 + 2.5234153e-8 4.6760678e-7 1.6125514e-7 + 1.0165617e-6 2.010384e-6 4.577127e-7 + 6.4351417e-7 7.099646e-7 9.3138954e-7 + 8.298905e-7 7.41345e-7 3.1396485e-7 + 6.3612895e-8 2.4984295e-6 5.7886194e-7 + 2.985152e-8 1.7607813e-7 1.4156687e-7 + 3.168092e-8 3.4657577e-7 6.41637e-7 + 1.2207072e-8 1.00421644e-7 1.17458235e-7 + 7.478762e-9 9.054672e-8 9.1597116e-8 + 1.2781243e-8 1.5222273e-7 7.235564e-8 + 9.750051e-9 2.6144977e-7 1.1669626e-6 + 1.217304e-7 3.616973e-6 5.20076e-7 + 1.2768111e-8 5.201764e-8 2.84177e-7 + 2.6672244e-9 9.2578824e-7 8.819847e-8 + 1.1545499e-7 6.2721796e-8 4.243834e-7 + 1.5369764e-8 1.505944e-7 1.9841306e-7 + 2.7063518e-8 4.8290755e-8 2.4411463e-7 + 3.9321475e-9 1.9112503e-7 1.14984005e-7 + 3.0580477e-8 4.82117e-6 9.338662e-8 + 2.2022753e-9 5.3431386e-8 1.409437e-7 + 3.3074259e-9 9.451089e-7 3.738786e-7 + 1.0433772e-5 6.760473e-5 9.6766325e-6 + 2.5747761e-8 1.6592732e-6 2.4620915e-6 + 3.9562156e-6 4.246295e-7 1.1445722e-5 + 4.543625e-8 1.4435316e-6 7.903168e-7 + 1.0092194e-6 1.9891236e-6 6.5939423e-7 + 7.76582e-8 2.222393e-6 6.28766e-7 + 1.2372239e-7 1.3727438e-7 1.1477929e-6 + 2.407644e-8 7.993003e-7 4.1844632e-7 + 8.843334e-9 1.4812893e-7 4.2442588e-7 + 1.5827373e-8 2.1477146e-7 4.1050706e-7 + 9.2732826e-8 9.367395e-7 1.1438651e-7 + 7.9451565e-9 4.969516e-8 1.1246204e-6 + 1.433404e-8 1.3811665e-7 9.476624e-7 + 5.941303e-7 6.890314e-7 3.6959892e-7 + 2.0489452e-8 1.03566094e-7 7.689774e-7 + 2.438743e-7 8.728375e-7 3.2116546e-7 + 6.4884865e-8 1.1810116e-7 9.901164e-7 + 6.232637e-8 2.4237013e-7 2.9436828e-7 + 9.734271e-8 3.0696128e-7 7.313043e-7 + 7.798535e-8 2.633466e-7 8.326499e-7 + 1.6775836e-7 5.7168654e-7 9.513002e-8 + 4.423723e-9 4.407137e-6 3.6187964e-7 + 3.3216903e-8 2.7631856e-7 1.4946868e-7 + 2.5989824e-8 3.9617322e-7 8.582249e-8 0.0 0.0 0.0 + 0.78942835 0.7030404 0.7141928 + 2.0 17.0 3.0 + 1.0 1.0 1.0 ], - "dn_v7_COCO_dog-cycle-car_nonsquare" => [ - 0.23688027 0.24578272; - 0.16262104 0.21957606; - 0.9971996 0.5866954; - 0.59032714 0.8174101; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.9743059 0.9687549; - 2.0 17.0; + "od_v2_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.1689375 0.24524122 + 0.15931238 0.23013908 + 0.98755926 0.5969102 + 0.5386596 0.7868472 + 0.6878193 0.8527449 + 0.0014332853 4.396705e-6 + 0.64102656 5.3872946e-6 + 0.0060599004 4.0688573e-7 + 0.0024225458 7.607443e-7 + 1.7761304e-5 8.673216e-8 + 0.0006776177 1.7987515e-8 + 0.000120816374 3.386518e-8 + 0.0010805506 8.724334e-8 + 0.005627892 6.950457e-7 + 1.7700328e-5 4.6372076e-8 + 7.046585e-5 1.2090294e-6 + 2.2624108e-5 1.626169e-7 + 2.6060461e-5 5.038605e-7 + 0.0062638945 1.6548623e-6 + 1.2425572e-5 1.6572465e-5 + 6.571216e-5 0.014827092 + 0.0012634731 0.8377859 + 3.5815166e-5 7.4370478e-6 + 1.19410015e-5 1.1660953e-5 + 3.5453127e-6 1.25046145e-5 + 2.6037692e-6 1.3392088e-7 + 5.4990533e-6 1.5284606e-5 + 1.2719855e-6 2.420354e-7 + 3.398975e-6 3.3787794e-6 + 0.00018888581 5.8969063e-6 + 0.0045596864 1.7622068e-6 + 6.0368708e-5 2.1348199e-6 + 7.4400336e-6 1.0997288e-6 + 0.00076672004 1.2506588e-6 + 0.00043368686 1.3450357e-6 + 0.00034617097 2.2797897e-7 + 2.2009097e-5 6.893031e-8 + 4.0364233e-5 2.3033681e-7 + 8.248804e-5 2.9155174e-7 + 0.000118863005 7.362702e-8 + 1.2024847e-5 2.4801108e-7 + 0.00046726465 7.1867873e-7 + 0.005421301 2.453146e-7 + 0.00031920828 2.7776744e-7 + 6.260917e-5 8.699862e-7 + 6.362409e-6 1.7616598e-7 + 1.4068126e-5 3.111447e-7 + 3.533276e-6 6.756988e-8 + 1.8470005e-6 4.9082477e-8 + 3.9278752e-6 8.00932e-8 + 4.7997864e-6 1.8186647e-7 + 8.21764e-5 1.9756608e-6 + 4.4231624e-6 4.3608757e-8 + 1.603579e-6 7.951953e-7 + 2.8911903e-5 1.8605816e-8 + 1.0714042e-6 9.894294e-8 + 7.0113892e-6 1.6968533e-8 + 4.1778376e-6 1.4870798e-7 + 8.644932e-6 3.1213394e-6 + 5.7137794e-7 7.067594e-8 + 5.3218246e-6 6.4239185e-7 + 0.0062826565 1.1603401e-5 + 6.0879975e-5 1.806975e-6 + 0.00037452966 8.006189e-8 + 0.0005841755 1.9151134e-6 + 0.00022291364 4.7457436e-7 + 6.0444698e-5 1.6462882e-6 + 0.00016428424 7.070911e-8 + 3.911437e-5 9.514102e-7 + 6.562258e-6 1.6149627e-7 + 9.296868e-6 2.3509111e-7 + 0.00011312842 6.310973e-7 + 1.07225405e-5 5.250507e-8 + 2.0314128e-5 1.1093042e-7 + 4.2707103e-5 1.8774836e-7 + 1.1679116e-5 1.0411035e-7 + 0.00019495696 5.3225784e-7 + 4.3185682e-5 7.3667614e-8 + 1.52942e-5 1.240683e-7 + 2.6563079e-5 1.5554765e-7 + 4.535674e-6 1.5102385e-7 + 5.816375e-5 3.1913444e-7 + 3.7140526e-6 3.0416163e-6 + 3.3908695e-5 1.6172054e-7 + 7.506096e-5 2.9147586e-7 0.0 0.0 + 0.64102656 0.8377859 + 2.0 17.0 + 1.0 1.0 ], - "dn_v7_tiny_COCO_dog-cycle-car" => [ - 0.23326866 0.24692088 0.87750936; - 0.16659053 0.32880515 0.057245042; - 0.99945194 0.5826077 0.99936235; - 0.695994 0.9460747 0.21617691; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.0 0.0 0.0; - 0.8781291 0.87099695 0.8488881; - 2.0 17.0 3.0; + "od_v2_tiny_COCO_dog-cycle-car" => Float32[ + 0.11871338 0.19095433 0.8690703 + 0.24898377 0.10536334 0.05126568 + 0.7444068 1.1171392 1.0070908 + 1.0466365 0.77095616 0.2240009 + 0.69387424 0.69902545 0.8112938 + 0.0008987789 0.00022354074 0.00022385716 + 0.00034189943 0.69729036 1.9661743e-6 + 0.0057066996 2.0611782e-5 0.68624234 + 0.001541932 0.00044406828 3.920957e-5 + 2.743121e-5 7.706883e-7 2.7320987e-6 + 1.5977339e-5 4.9926202e-6 0.0009291807 + 1.6140764e-5 3.5874945e-7 2.4774772e-5 + 0.00024954346 1.2203536e-6 0.12362775 + 0.0020453637 1.3014524e-5 4.556519e-6 + 8.508562e-6 2.5346164e-8 3.712489e-7 + 1.4502485e-5 4.051449e-8 2.3241614e-6 + 1.9091856e-6 7.308769e-8 7.9534544e-7 + 2.3174283e-5 3.3019063e-8 4.208304e-6 + 0.008147207 4.4822267e-5 3.877463e-6 + 0.0008246361 1.3122478e-7 1.7189066e-6 + 0.019679056 3.4399718e-6 3.1751829e-6 + 0.62716407 1.6497801e-6 7.586225e-6 + 0.00021912254 8.5184176e-5 2.9016496e-6 + 9.682777e-5 4.01874e-8 8.5607115e-7 + 0.00028123643 9.682689e-7 4.822609e-6 + 9.2236644e-7 1.1083416e-7 1.4079159e-5 + 6.171252e-5 3.95596e-8 1.1392298e-6 + 9.585823e-6 1.3060166e-7 1.3974757e-6 + 0.00012201938 1.4584e-7 8.2183135e-7 + 0.00016306507 2.461954e-6 2.1240454e-5 + 0.00015110725 4.0089617e-5 2.7996733e-5 + 5.6428693e-5 9.167696e-6 2.2302835e-5 + 4.1387493e-6 3.0854796e-8 1.5446034e-6 + 0.00014508578 2.1942685e-6 2.3407432e-5 + 0.0001536566 3.558782e-7 5.271552e-7 + 7.7928205e-5 4.0250343e-6 4.920886e-7 + 2.1783288e-5 1.2495694e-7 3.291361e-7 + 2.475414e-5 2.387964e-7 2.266852e-6 + 1.1833976e-5 1.8240113e-6 3.0859292e-7 + 2.4355446e-5 2.9575034e-7 5.4607767e-8 + 3.1701453e-5 1.3382432e-7 3.2310726e-7 + 7.80129e-5 1.1934698e-6 1.599028e-6 + 9.2786555e-5 2.2244933e-6 2.317738e-6 + 5.600521e-5 3.72768e-5 7.6918394e-7 + 5.3881436e-6 1.0576999e-7 1.0762142e-6 + 4.5800202e-6 5.5717685e-8 4.0481268e-7 + 5.2277196e-6 1.2196498e-6 9.828692e-7 + 1.3805374e-5 3.839451e-8 7.52782e-8 + 3.352416e-6 2.3817999e-9 6.3286826e-8 + 1.4988306e-5 3.71283e-8 5.0390394e-8 + 8.525672e-5 2.2223486e-7 8.9748943e-7 + 1.240048e-5 1.1553569e-8 3.920497e-7 + 1.7118531e-6 3.0151547e-8 3.5612408e-7 + 1.2526813e-6 3.1178543e-10 5.6218323e-7 + 2.0490602e-6 1.4238779e-8 9.370811e-8 + 1.9705412e-6 2.105635e-9 2.6405297e-7 + 1.3291553e-6 3.4250003e-9 1.4144928e-7 + 1.3091118e-6 7.5898404e-10 4.7056506e-7 + 8.727e-6 7.49477e-9 1.3184071e-7 + 1.9082115e-6 6.452558e-9 2.5148097e-7 + 3.7832157e-5 6.385104e-8 1.161801e-6 + 0.008944842 0.0007635413 9.192342e-6 + 0.00038597427 2.1326014e-6 2.6465032e-6 + 2.7870714e-5 6.2252134e-6 3.2873818e-6 + 0.005463749 1.6651251e-6 4.572942e-7 + 0.0015627501 5.8675823e-6 1.0650066e-6 + 0.004339739 6.700967e-7 4.3220572e-7 + 0.00010307234 1.8767606e-8 8.787206e-7 + 0.00022018015 1.176118e-6 1.8503696e-6 + 7.496523e-6 2.7023653e-8 2.1755263e-6 + 4.6247656e-5 2.0927025e-9 3.8503953e-7 + 0.00013838809 8.214469e-8 2.0715814e-7 + 8.732678e-6 7.5222095e-9 4.1723065e-6 + 2.0204317e-5 5.975637e-9 3.961017e-7 + 2.6865802e-5 8.107722e-9 3.6265553e-7 + 1.8737706e-5 1.933666e-7 4.7235864e-7 + 0.0027915202 6.1677883e-7 3.3697862e-7 + 0.00017572215 8.264972e-8 5.829258e-7 + 0.00066275685 1.17613304e-7 1.1388387e-6 + 3.3390006e-5 2.6619612e-6 6.536876e-7 + 2.3445846e-5 3.4354395e-7 6.8269e-7 + 2.1123096e-5 1.246628e-7 5.0701306e-8 + 2.4404062e-5 9.366713e-7 2.6591401e-6 + 1.8189116e-5 5.050002e-8 1.3977522e-7 + 1.477255e-5 9.570004e-8 1.7925959e-7 0.0 0.0 0.0 + 0.62716407 0.69729036 0.68624234 + 17.0 2.0 3.0 + 1.0 1.0 1.0 ], - "dn_v7_tiny_COCO_dog-cycle-car_nonsquare" => [ - 0.21098383 0.24594077; - 0.1621059 0.21256444; - 0.9982124 0.5849901; - 0.57099396 0.8199513; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.0 0.0; - 0.7663366 0.90416116; - 2.0 17.0; + "od_v2_tiny_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.15290752 0.20614076 + 0.13900006 0.10331702 + 0.710466 1.1014122 + 0.8654499 0.62178963 + 0.9003332 0.5633622 + 0.00040936618 3.5456327e-5 + 0.00033080188 0.5631998 + 0.0014215447 3.410984e-6 + 0.0007985512 4.0927065e-5 + 1.8044623e-6 8.995422e-8 + 2.1364987e-5 1.1399276e-6 + 8.58304e-6 7.813778e-8 + 0.00023298136 2.7114336e-7 + 0.00048686995 2.2037968e-6 + 9.2448835e-7 4.5575734e-9 + 2.6462078e-6 5.7063794e-9 + 5.5444366e-7 8.533511e-9 + 6.624052e-6 4.713381e-9 + 0.0015917888 4.696356e-6 + 5.5563625e-5 2.3786423e-8 + 0.010716682 1.1121039e-7 + 0.87685555 4.4682503e-7 + 0.0004763933 1.734899e-5 + 5.1406383e-5 3.4723746e-9 + 0.00016268411 2.736638e-7 + 1.0041257e-6 4.5017368e-9 + 2.7991124e-5 1.1442358e-9 + 2.98183e-6 9.781835e-9 + 1.6724563e-5 6.096858e-9 + 0.00012748805 5.3296395e-7 + 1.6251408e-5 2.62274e-6 + 2.4177425e-5 7.6047303e-7 + 3.6366346e-6 5.1840074e-9 + 9.82215e-5 2.3279185e-7 + 3.1749754e-5 4.076457e-8 + 8.637687e-6 4.8530205e-7 + 2.071697e-6 1.5481607e-8 + 1.1167736e-5 1.6112544e-8 + 2.0102125e-6 1.9675655e-7 + 4.9686955e-6 1.100043e-7 + 1.24622175e-5 7.953869e-8 + 1.6040553e-5 2.459487e-7 + 2.3985156e-5 2.0257737e-7 + 1.6184238e-5 1.626855e-5 + 1.4380474e-6 1.2503833e-8 + 6.340804e-7 9.202987e-9 + 1.4132327e-6 1.332866e-7 + 7.1001307e-7 5.0585043e-9 + 5.268204e-7 4.101237e-10 + 1.3877976e-6 7.8461895e-9 + 1.2363362e-5 2.8956402e-8 + 6.531171e-6 6.7395765e-9 + 7.313702e-7 2.3124607e-9 + 6.6870246e-7 1.7168742e-11 + 4.8551715e-7 9.1281777e-10 + 2.3039277e-7 4.005636e-10 + 5.1361e-7 8.125182e-10 + 6.316065e-7 2.1202679e-10 + 3.9833385e-6 1.1421316e-9 + 3.4271673e-7 3.0998115e-10 + 7.560931e-6 3.3444303e-9 + 0.0032854793 3.104846e-5 + 0.000497859 6.907311e-8 + 9.50898e-6 5.887177e-7 + 0.0011020884 2.956244e-7 + 0.00017125822 8.3126133e-7 + 0.00029824843 1.0894535e-7 + 5.3595828e-5 3.4336944e-9 + 4.290796e-5 6.3955156e-8 + 1.6131669e-6 1.5754308e-9 + 7.1954787e-6 4.0608308e-10 + 1.3436616e-5 6.8405406e-9 + 3.2460628e-6 3.3414078e-9 + 3.4638556e-6 3.1740435e-9 + 3.005767e-6 9.738181e-9 + 3.5589944e-6 1.1355549e-8 + 0.0003717706 3.6072166e-8 + 4.044418e-5 3.3360523e-9 + 0.0002645297 9.146272e-9 + 1.1119669e-5 7.93612e-7 + 4.0071827e-6 2.0698035e-8 + 3.7556433e-6 4.9773377e-8 + 1.2374048e-5 4.4568257e-8 + 4.506299e-6 1.0428729e-8 + 3.7186173e-6 1.4824447e-8 0.0 0.0 + 0.87685555 0.5631998 + 17.0 2.0 + 1.0 1.0 ], - "od_v2_tiny_COCO_dog-cycle-car" => [ - 0.11871338 0.19095433 0.8690703; - 0.24898377 0.10536334 0.05126568; - 0.7444068 1.1171392 1.0070908; - 1.0466365 0.77095616 0.2240009; - 0.69387424 0.69902545 0.8112938; - 0.6525434 0.6980044 0.8055601; - 0.59483343 0.6990251 0.44813687; - 0.6870209 0.68810916 0.81129193; - 0.66916907 0.6985111 0.77961326; - 0.22563104 0.49079219 0.5124429; - 0.15206617 0.6560575 0.80990493; - 0.153278 0.36570147 0.7622702; - 0.56498706 0.5513059 0.81128335; - 0.6750852 0.68189305 0.60110027; - 0.09022437 0.05028647 0.15331127; - 0.14087902 0.077062584 0.48132285; - 0.022515688 0.12770139 0.27013257; - 0.20074658 0.06411322 0.58848447; - 0.68905956 0.6939628 0.5750103; - 0.6490673 0.20018986 0.42102626; - 0.69187284 0.63834715 0.54021174; - 0.6938113 0.58339596 0.6704745; - 0.5507835 0.6963525 0.52371556; - 0.43697083 0.07650845 0.28354907; - 0.5770664 0.52255636 0.60981786; - 0.011063345 0.17695765 0.72881436; - 0.36093175 0.07544233 0.33823618; - 0.10000126 0.19951059 0.37908307; - 0.47313705 0.21560901 0.27606723; - 0.5143217 0.61706865 0.7546821; - 0.5040011 0.69337004 0.7676083; - 0.34541228 0.6749517 0.757199; - 0.0470276 0.060273148 0.39934802; - 0.49834144 0.6083672 0.7595891; - 0.50629973 0.36429986 0.20168865; - 0.40096644 0.6465044 0.1914382; - 0.1920319 0.19327171 0.13889776; - 0.21028452 0.29503268 0.4764241; - 0.119418055 0.592762 0.13163501; - 0.20791256 0.3319807 0.0268839; - 0.24819176 0.20300382 0.13678251; - 0.40115026 0.54869395 0.4063711; - 0.43003628 0.60944116 0.48078105; - 0.3441057 0.69294703 0.26413995; - 0.059996694 0.17084835 0.3270694; - 0.0516683 0.101770446 0.16436379; - 0.058360588 0.55123854 0.30952415; - 0.13542892 0.07345389 0.03660104; - 0.03858961 0.005054929 0.030993413; - 0.14461483 0.07127847 0.024871264; - 0.41606367 0.28285044 0.2923247; - 0.1241122 0.02385598 0.16020502; - 0.020256417 0.059015367 0.14820631; - 0.014940005 0.0006658917 0.21159501; - 0.024107952 0.029169096 0.045064095; - 0.023215072 0.0044725738 0.11533688; - 0.015831262 0.0072459737 0.066150814; - 0.015597892 0.001618778 0.18497385; - 0.09223277 0.015663099 0.062000584; - 0.022504576 0.01352718 0.11059402; - 0.27702644 0.11419935 0.34211153; - 0.6894862 0.69872624 0.6914443; - 0.6046893 0.6060941 0.50640965; - 0.22805798 0.6641406 0.5464434; - 0.6867193 0.5842866 0.18092056; - 0.6694866 0.6621266 0.32502788; - 0.68489015 0.46978438 0.17311278; - 0.4469974 0.037943155 0.28838447; - 0.5513297 0.5469585 0.43592468; - 0.08074152 0.053360507 0.46830952; - 0.31102696 0.004445272 0.1578979; - 0.4916363 0.14034882 0.0933448; - 0.09228483 0.015719177 0.5870934; - 0.18175852 0.012545296 0.16153115; - 0.2224716 0.0169131 0.1504205; - 0.17183214 0.2597619 0.18551764; - 0.68000704 0.45683348 0.14162965; - 0.5240901 0.1410376 0.21731095; - 0.63898885 0.18491791 0.3381684; - 0.25652412 0.6225529 0.23601638; - 0.20241281 0.3581426 0.2433469; - 0.18778697 0.1929423 0.025020001; - 0.208203 0.51814383 0.5073155; - 0.1680196 0.09351513 0.06543106; - 0.14296189 0.1582653 0.082045116; - 0.0 0.0 0.0; - 0.6938113 0.6990251 0.81129193; - 17.0 2.0 3.0; - 1.0 1.0 1.0 - ], - "od_v3_COCO_dog-cycle-car" => [ - 0.19417053 0.2512917 0.87945247; - 0.11786431 0.34023294 0.057231084; - 1.0063193 0.5869944 1.0014776; - 0.7645823 0.93842757 0.21131791; - 0.9995278 0.99931896 0.9600554; - 6.84662f-6 6.2348863f-7 1.6280905f-5; - 0.9988289 5.294633f-6 5.550454f-7; - 4.27632f-5 9.19633f-8 0.946702; - 3.2615677f-5 2.64196f-7 3.0475092f-6; - 1.8063157f-7 3.6772418f-9 1.1102389f-6; - 4.0890085f-5 3.820776f-8 9.648094f-5; - 2.7271067f-6 2.2622001f-10 1.0501994f-5; - 2.545097f-6 1.1437643f-7 0.018053256; - 0.020164147 5.759063f-6 6.181232f-6; - 2.9495768f-8 2.0654307f-9 6.420875f-7; - 1.1428097f-6 4.079646f-7 4.5086986f-6; - 4.975904f-8 9.188227f-9 8.931493f-7; - 5.969592f-8 1.3812065f-8 8.503025f-6; - 2.5420855f-5 3.8266327f-7 1.1355838f-6; - 7.799988f-7 1.3804632f-6 5.8046897f-8; - 1.0408487f-5 3.2867178f-5 8.7770076f-8; - 9.1399095f-5 0.99910194 1.6726394f-6; - 1.5299801f-6 5.816472f-7 3.6334913f-7; - 6.009606f-7 1.5624817f-5 3.9317732f-7; - 4.978009f-9 9.5225255f-7 1.6664426f-6; - 1.7671343f-8 1.2564334f-7 8.141259f-7; - 3.2334203f-8 4.184731f-6 8.884014f-7; - 8.9079606f-8 5.913905f-9 2.3963162f-6; - 1.5608841f-6 1.7362653f-6 3.1455565f-7; - 5.4962896f-7 4.998196f-7 4.4759377f-6; - 2.2777238f-5 1.2160163f-7 5.175549f-6; - 2.6876964f-7 4.2334097f-8 1.8724705f-6; - 1.637287f-8 5.994242f-8 5.1642046f-9; - 5.942889f-6 2.1478573f-7 2.6486967f-5; - 2.6822113f-6 2.2142935f-6 9.1189236f-7; - 1.9530742f-5 2.3365789f-7 7.9996845f-8; - 4.993036f-8 9.816027f-9 1.4691967f-7; - 1.0366178f-6 1.551963f-7 1.3894616f-5; - 3.5528956f-6 2.8034427f-7 1.7753558f-7; - 3.3154294f-7 1.3180645f-8 1.3592762f-6; - 2.1425446f-7 4.485047f-8 3.2649086f-7; - 3.3085068f-6 3.4664217f-6 7.1472545f-7; - 1.6941731f-6 4.860799f-7 1.6491342f-6; - 7.426256f-6 6.0219165f-8 5.7161304f-7; - 2.9683253f-8 1.3244656f-8 1.547523f-6; - 1.808048f-7 2.497413f-9 4.2342577f-8; - 2.0912125f-7 5.7168315f-9 7.733236f-7; - 4.2861507f-6 1.4723198f-10 3.2460542f-7; - 3.8937234f-8 4.9218762f-9 8.517017f-8; - 3.4270985f-7 4.4869637f-8 1.1743746f-7; - 1.6266173f-7 1.4175867f-9 4.069451f-6; - 5.110102f-7 8.3066716f-8 2.20357f-7; - 1.7076962f-8 6.233689f-9 2.3011767f-6; - 5.758063f-7 2.41846f-6 6.928843f-7; - 6.50027f-7 4.101483f-10 2.2433555f-6; - 7.137244f-8 6.062344f-8 6.5940185f-8; - 3.5710553f-7 1.0576748f-9 3.881537f-7; - 1.409395f-7 2.073629f-7 3.1922057f-6; - 1.7877139f-5 4.6618108f-5 1.7439051f-8; - 1.410579f-8 1.6517694f-7 5.051711f-6; - 4.2492276f-9 4.5027726f-8 1.0907437f-6; - 6.966393f-6 1.8237789f-6 5.4271237f-7; - 5.655553f-8 7.6212626f-7 5.051316f-6; - 9.859648f-6 9.616377f-7 5.134419f-7; - 7.774909f-8 2.1033622f-6 3.0389676f-6; - 1.5853434f-6 5.616037f-7 1.8223498f-7; - 5.571813f-8 2.2141384f-7 2.235846f-6; - 6.8149375f-9 1.1808227f-8 4.6534433f-6; - 3.5756296f-9 4.5045077f-8 8.8821956f-8; - 8.270272f-8 7.91901f-9 5.386807f-6; - 2.0769495f-8 1.7812335f-8 2.1228143f-6; - 1.2398779f-6 1.5088506f-7 5.5540127f-8; - 9.287962f-10 9.728456f-10 3.4257125f-6; - 2.1378566f-9 1.2017165f-7 6.2084524f-7; - 1.8430035f-5 1.10666456f-7 4.3369857f-7; - 5.419496f-7 2.6854636f-8 6.569533f-6; - 1.1853178f-6 3.867695f-7 1.0727575f-6; - 1.8275367f-7 7.216945f-9 9.2320215f-7; - 1.8561883f-8 3.6991687f-9 1.2073359f-7; - 1.801753f-6 8.5372506f-8 2.7632424f-7; - 5.683641f-7 1.912974f-10 2.7875774f-7; - 4.2577892f-7 3.4787813f-9 1.0288681f-7; - 1.262967f-8 1.9039522f-6 1.43556464f-8; - 4.018171f-7 4.0304435f-8 6.696728f-7; - 2.8551287f-7 1.030376f-8 3.9475594f-6; - 0.0 0.0 0.0; - 0.9988289 0.99910194 0.946702; - 2.0 17.0 3.0; + "od_v3_COCO_dog-cycle-car" => Float32[ + 0.19417053 0.2512917 0.87945247 + 0.11786431 0.34023294 0.057231084 + 1.0063193 0.5869944 1.0014776 + 0.7645823 0.93842757 0.21131791 + 0.9995278 0.99931896 0.9600554 + 6.84662e-6 6.2348863e-7 1.6280905e-5 + 0.9988289 5.294633e-6 5.550454e-7 + 4.27632e-5 9.19633e-8 0.946702 + 3.2615677e-5 2.64196e-7 3.0475092e-6 + 1.8063157e-7 3.6772418e-9 1.1102389e-6 + 4.0890085e-5 3.820776e-8 9.648094e-5 + 2.7271067e-6 2.2622001e-10 1.0501994e-5 + 2.545097e-6 1.1437643e-7 0.018053256 + 0.020164147 5.759063e-6 6.181232e-6 + 2.9495768e-8 2.0654307e-9 6.420875e-7 + 1.1428097e-6 4.079646e-7 4.5086986e-6 + 4.975904e-8 9.188227e-9 8.931493e-7 + 5.969592e-8 1.3812065e-8 8.503025e-6 + 2.5420855e-5 3.8266327e-7 1.1355838e-6 + 7.799988e-7 1.3804632e-6 5.8046897e-8 + 1.0408487e-5 3.2867178e-5 8.7770076e-8 + 9.1399095e-5 0.99910194 1.6726394e-6 + 1.5299801e-6 5.816472e-7 3.6334913e-7 + 6.009606e-7 1.5624817e-5 3.9317732e-7 + 4.978009e-9 9.5225255e-7 1.6664426e-6 + 1.7671343e-8 1.2564334e-7 8.141259e-7 + 3.2334203e-8 4.184731e-6 8.884014e-7 + 8.9079606e-8 5.913905e-9 2.3963162e-6 + 1.5608841e-6 1.7362653e-6 3.1455565e-7 + 5.4962896e-7 4.998196e-7 4.4759377e-6 + 2.2777238e-5 1.2160163e-7 5.175549e-6 + 2.6876964e-7 4.2334097e-8 1.8724705e-6 + 1.637287e-8 5.994242e-8 5.1642046e-9 + 5.942889e-6 2.1478573e-7 2.6486967e-5 + 2.6822113e-6 2.2142935e-6 9.1189236e-7 + 1.9530742e-5 2.3365789e-7 7.9996845e-8 + 4.993036e-8 9.816027e-9 1.4691967e-7 + 1.0366178e-6 1.551963e-7 1.3894616e-5 + 3.5528956e-6 2.8034427e-7 1.7753558e-7 + 3.3154294e-7 1.3180645e-8 1.3592762e-6 + 2.1425446e-7 4.485047e-8 3.2649086e-7 + 3.3085068e-6 3.4664217e-6 7.1472545e-7 + 1.6941731e-6 4.860799e-7 1.6491342e-6 + 7.426256e-6 6.0219165e-8 5.7161304e-7 + 2.9683253e-8 1.3244656e-8 1.547523e-6 + 1.808048e-7 2.497413e-9 4.2342577e-8 + 2.0912125e-7 5.7168315e-9 7.733236e-7 + 4.2861507e-6 1.4723198e-10 3.2460542e-7 + 3.8937234e-8 4.9218762e-9 8.517017e-8 + 3.4270985e-7 4.4869637e-8 1.1743746e-7 + 1.6266173e-7 1.4175867e-9 4.069451e-6 + 5.110102e-7 8.3066716e-8 2.20357e-7 + 1.7076962e-8 6.233689e-9 2.3011767e-6 + 5.758063e-7 2.41846e-6 6.928843e-7 + 6.50027e-7 4.101483e-10 2.2433555e-6 + 7.137244e-8 6.062344e-8 6.5940185e-8 + 3.5710553e-7 1.0576748e-9 3.881537e-7 + 1.409395e-7 2.073629e-7 3.1922057e-6 + 1.7877139e-5 4.6618108e-5 1.7439051e-8 + 1.410579e-8 1.6517694e-7 5.051711e-6 + 4.2492276e-9 4.5027726e-8 1.0907437e-6 + 6.966393e-6 1.8237789e-6 5.4271237e-7 + 5.655553e-8 7.6212626e-7 5.051316e-6 + 9.859648e-6 9.616377e-7 5.134419e-7 + 7.774909e-8 2.1033622e-6 3.0389676e-6 + 1.5853434e-6 5.616037e-7 1.8223498e-7 + 5.571813e-8 2.2141384e-7 2.235846e-6 + 6.8149375e-9 1.1808227e-8 4.6534433e-6 + 3.5756296e-9 4.5045077e-8 8.8821956e-8 + 8.270272e-8 7.91901e-9 5.386807e-6 + 2.0769495e-8 1.7812335e-8 2.1228143e-6 + 1.2398779e-6 1.5088506e-7 5.5540127e-8 + 9.287962e-10 9.728456e-10 3.4257125e-6 + 2.1378566e-9 1.2017165e-7 6.2084524e-7 + 1.8430035e-5 1.10666456e-7 4.3369857e-7 + 5.419496e-7 2.6854636e-8 6.569533e-6 + 1.1853178e-6 3.867695e-7 1.0727575e-6 + 1.8275367e-7 7.216945e-9 9.2320215e-7 + 1.8561883e-8 3.6991687e-9 1.2073359e-7 + 1.801753e-6 8.5372506e-8 2.7632424e-7 + 5.683641e-7 1.912974e-10 2.7875774e-7 + 4.2577892e-7 3.4787813e-9 1.0288681e-7 + 1.262967e-8 1.9039522e-6 1.43556464e-8 + 4.018171e-7 4.0304435e-8 6.696728e-7 + 2.8551287e-7 1.030376e-8 3.9475594e-6 + 0.0 0.0 0.0 + 0.9988289 0.99910194 0.946702 + 2.0 17.0 3.0 1.0 1.0 1.0 ], - "od_v3_COCO_dog-cycle-car_nonsquare" => [ - 0.17121503 0.24764465; - 0.11916754 0.23210159; - 1.0063299 0.5886883; - 0.60264254 0.7908988; - 0.96526116 0.9996107; - 0.002565164 1.344385f-6; - 0.93356025 6.014301f-6; - 0.009201254 6.5052745f-8; - 0.0011039398 8.937377f-8; - 1.7233942f-5 3.0969913f-9; - 0.0059791473 5.6808563f-8; - 3.5643283f-5 5.2488697f-10; - 0.0011556066 3.7859454f-7; - 0.02808627 3.5937956f-6; - 2.703705f-6 2.5947946f-9; - 3.591901f-6 2.0877812f-7; - 9.2930105f-7 8.649291f-9; - 2.4762867f-6 1.2554747f-8; - 0.0029889038 1.5205252f-7; - 1.3076526f-6 4.5949906f-7; - 1.3718492f-5 1.6043863f-5; - 0.0004540762 0.9995055; - 3.505712f-5 5.182399f-7; - 1.3236182f-6 2.4833603f-6; - 7.151738f-8 2.3592074f-7; - 2.792458f-7 1.5565746f-7; - 3.7076205f-7 2.2332788f-6; - 2.4559972f-7 3.6460515f-9; - 3.085004f-6 1.2927914f-6; - 6.556131f-5 3.178391f-6; - 0.00012970317 8.87495f-8; - 8.338017f-6 3.1979912f-8; - 2.7538357f-7 1.3214043f-7; - 1.8825625f-5 1.9694028f-7; - 3.0075173f-5 1.2415154f-6; - 0.00022678082 2.59952f-7; - 2.7918556f-6 1.0719265f-8; - 1.118119f-5 1.054437f-7; - 5.1928688f-5 1.9821005f-7; - 5.816411f-6 7.2664212f-9; - 2.3697614f-6 4.034297f-8; - 0.00012925906 3.287544f-6; - 0.00012566976 4.4923505f-7; - 6.729925f-5 6.236868f-8; - 7.3601393f-7 1.6529649f-8; - 1.1414452f-6 5.628552f-10; - 4.571938f-6 7.4678965f-9; - 3.3770707f-6 7.588211f-11; - 2.353473f-7 3.9986574f-9; - 1.2715815f-6 3.6615244f-8; - 8.2617186f-7 4.6227733f-10; - 3.276057f-7 5.1317368f-8; - 9.7749485f-8 4.7395017f-9; - 1.8449747f-6 1.2309777f-6; - 1.4437114f-6 2.1525136f-10; - 5.7466264f-7 1.3562656f-7; - 8.536632f-7 1.2932608f-9; - 1.348736f-6 2.091033f-7; - 1.4382687f-5 3.4014527f-5; - 5.0613593f-8 8.15563f-8; - 3.1995718f-8 1.865923f-8; - 0.0014240663 6.156549f-6; - 3.3745564f-6 1.6961308f-6; - 1.2984689f-5 2.7287564f-7; - 9.253867f-7 1.529172f-6; - 0.0001401631 1.8962172f-7; - 7.479724f-7 8.160205f-8; - 2.7151775f-6 1.7313344f-8; - 2.9168457f-6 5.3161187f-8; - 7.100827f-7 1.11784635f-8; - 2.682445f-7 2.1929155f-8; - 1.9420666f-5 2.0462112f-7; - 3.0610298f-7 1.0554462f-9; - 3.780523f-7 1.0908444f-7; - 5.468832f-5 1.00757774f-7; - 4.848841f-6 2.5995217f-8; - 6.21967f-6 6.090171f-8; - 8.95331f-6 9.451061f-9; - 4.7400877f-6 6.6012777f-9; - 4.866254f-6 9.996718f-8; - 1.1450923f-6 1.3172742f-10; - 2.2613997f-6 3.4270176f-9; - 3.030548f-7 1.0446566f-6; - 4.2615693f-6 3.520475f-8; - 9.931302f-7 9.3225845f-9; - 0.0 0.0; - 0.93356025 0.9995055; - 2.0 17.0; + "od_v3_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.17121503 0.24764465 + 0.11916754 0.23210159 + 1.0063299 0.5886883 + 0.60264254 0.7908988 + 0.96526116 0.9996107 + 0.002565164 1.344385e-6 + 0.93356025 6.014301e-6 + 0.009201254 6.5052745e-8 + 0.0011039398 8.937377e-8 + 1.7233942e-5 3.0969913e-9 + 0.0059791473 5.6808563e-8 + 3.5643283e-5 5.2488697e-10 + 0.0011556066 3.7859454e-7 + 0.02808627 3.5937956e-6 + 2.703705e-6 2.5947946e-9 + 3.591901e-6 2.0877812e-7 + 9.2930105e-7 8.649291e-9 + 2.4762867e-6 1.2554747e-8 + 0.0029889038 1.5205252e-7 + 1.3076526e-6 4.5949906e-7 + 1.3718492e-5 1.6043863e-5 + 0.0004540762 0.9995055 + 3.505712e-5 5.182399e-7 + 1.3236182e-6 2.4833603e-6 + 7.151738e-8 2.3592074e-7 + 2.792458e-7 1.5565746e-7 + 3.7076205e-7 2.2332788e-6 + 2.4559972e-7 3.6460515e-9 + 3.085004e-6 1.2927914e-6 + 6.556131e-5 3.178391e-6 + 0.00012970317 8.87495e-8 + 8.338017e-6 3.1979912e-8 + 2.7538357e-7 1.3214043e-7 + 1.8825625e-5 1.9694028e-7 + 3.0075173e-5 1.2415154e-6 + 0.00022678082 2.59952e-7 + 2.7918556e-6 1.0719265e-8 + 1.118119e-5 1.054437e-7 + 5.1928688e-5 1.9821005e-7 + 5.816411e-6 7.2664212e-9 + 2.3697614e-6 4.034297e-8 + 0.00012925906 3.287544e-6 + 0.00012566976 4.4923505e-7 + 6.729925e-5 6.236868e-8 + 7.3601393e-7 1.6529649e-8 + 1.1414452e-6 5.628552e-10 + 4.571938e-6 7.4678965e-9 + 3.3770707e-6 7.588211e-11 + 2.353473e-7 3.9986574e-9 + 1.2715815e-6 3.6615244e-8 + 8.2617186e-7 4.6227733e-10 + 3.276057e-7 5.1317368e-8 + 9.7749485e-8 4.7395017e-9 + 1.8449747e-6 1.2309777e-6 + 1.4437114e-6 2.1525136e-10 + 5.7466264e-7 1.3562656e-7 + 8.536632e-7 1.2932608e-9 + 1.348736e-6 2.091033e-7 + 1.4382687e-5 3.4014527e-5 + 5.0613593e-8 8.15563e-8 + 3.1995718e-8 1.865923e-8 + 0.0014240663 6.156549e-6 + 3.3745564e-6 1.6961308e-6 + 1.2984689e-5 2.7287564e-7 + 9.253867e-7 1.529172e-6 + 0.0001401631 1.8962172e-7 + 7.479724e-7 8.160205e-8 + 2.7151775e-6 1.7313344e-8 + 2.9168457e-6 5.3161187e-8 + 7.100827e-7 1.11784635e-8 + 2.682445e-7 2.1929155e-8 + 1.9420666e-5 2.0462112e-7 + 3.0610298e-7 1.0554462e-9 + 3.780523e-7 1.0908444e-7 + 5.468832e-5 1.00757774e-7 + 4.848841e-6 2.5995217e-8 + 6.21967e-6 6.090171e-8 + 8.95331e-6 9.451061e-9 + 4.7400877e-6 6.6012777e-9 + 4.866254e-6 9.996718e-8 + 1.1450923e-6 1.3172742e-10 + 2.2613997e-6 3.4270176e-9 + 3.030548e-7 1.0446566e-6 + 4.2615693e-6 3.520475e-8 + 9.931302e-7 9.3225845e-9 + 0.0 0.0 + 0.93356025 0.9995055 + 2.0 17.0 1.0 1.0 ], - "od_v3_spp_COCO_dog-cycle-car" => [ - 0.19689906 0.24459462 0.8887503; - 0.1488463 0.36018223 0.06057042; - 1.0473961 0.59275776 1.0014724; - 0.70960736 0.9316283 0.2309401; - 0.910512 0.9881821 0.8952051; - 1.930166f-5 8.159738f-6 4.7085752f-5; - 0.9104578 1.691023f-6 1.9126207f-6; - 1.3790263f-6 6.911531f-8 0.7138366; - 2.6012879f-6 1.2904401f-7 5.0861778f-5; - 1.1098012f-8 2.2086285f-8 6.1016376f-6; - 9.057754f-6 9.5129586f-8 0.00038733156; - 1.1161652f-6 8.367672f-9 2.2170327f-5; - 9.058946f-7 4.1705272f-7 0.29135424; - 4.5512534f-5 2.4128855f-7 5.8548985f-6; - 2.1115416f-7 1.15923955f-8 1.1526488f-6; - 8.1184663f-7 2.2253998f-7 1.5866458f-6; - 1.1245807f-8 1.8470355f-7 5.4033757f-7; - 1.08634204f-7 7.9246384f-8 4.4991725f-6; - 1.1923254f-6 5.1360627f-7 9.995203f-5; - 1.3927402f-6 1.7355974f-6 3.1599143f-8; - 3.81957f-6 0.0010601201 7.618114f-8; - 1.6381528f-6 0.98495096 1.6210147f-6; - 2.8651685f-8 1.2533204f-6 2.2698643f-5; - 2.608681f-8 2.8966385f-6 4.714277f-6; - 4.9097255f-9 1.2341462f-6 5.096642f-6; - 2.9978867f-8 8.6187976f-7 5.9160384f-6; - 4.2343538f-9 1.2508925f-5 4.3054132f-7; - 1.2049973f-8 9.0271364f-8 3.6232873f-6; - 9.647422f-8 3.205269f-7 2.1099694f-8; - 9.756159f-7 1.899152f-5 5.5800174f-6; - 2.1720164f-6 9.4082644f-8 5.9462027f-6; - 1.3667454f-6 2.1821234f-7 3.2749144f-6; - 3.8992642f-8 1.924836f-7 1.5601264f-8; - 9.319571f-6 9.669151f-7 3.1558318f-6; - 1.5815185f-7 1.7082317f-6 7.281923f-7; - 6.4362152f-6 1.120655f-6 4.391029f-7; - 4.4480068f-8 8.593095f-8 1.7877613f-7; - 3.2706f-7 3.6466574f-7 7.1183126f-7; - 2.4603432f-7 4.2563258f-7 3.8344479f-7; - 7.552904f-8 1.4771066f-8 6.0315065f-6; - 3.821396f-8 1.2727838f-7 1.4470459f-6; - 4.979707f-7 2.7458195f-6 2.3616168f-7; - 3.5113857f-7 5.0932704f-7 1.4618166f-7; - 5.279122f-6 4.51986f-7 5.927365f-7; - 1.202448f-7 1.0310528f-8 3.596275f-7; - 7.913564f-8 2.5259968f-8 4.3275762f-7; - 5.2092705f-8 4.315123f-8 3.3435597f-6; - 3.1152084f-7 4.4646398f-9 1.2317258f-6; - 3.043047f-8 6.802467f-9 3.9936265f-7; - 4.609466f-8 2.4053584f-8 2.109222f-6; - 6.650988f-8 1.1567878f-8 6.710031f-6; - 2.667538f-6 5.652369f-8 7.274405f-7; - 3.316609f-7 7.0341075f-9 8.953999f-8; - 3.0939333f-9 4.22922f-7 6.491611f-7; - 1.8770496f-8 1.5821362f-9 2.7103135f-6; - 4.937021f-7 1.8987889f-8 1.0990202f-6; - 3.6307281f-7 3.0497155f-10 7.155243f-7; - 4.9156004f-9 1.8683498f-7 4.2371803f-6; - 1.7441934f-6 9.320106f-6 7.78749f-8; - 3.4816299f-9 7.210677f-8 1.737384f-6; - 2.9242882f-8 6.208634f-8 1.0987446f-6; - 2.5622048f-5 1.7635933f-6 2.5303048f-5; - 1.26850415f-8 2.8108548f-7 1.6335434f-5; - 0.00053333153 1.0198573f-6 5.6205663f-5; - 1.1474223f-7 3.75714f-6 1.8207937f-6; - 3.6199378f-7 2.5305013f-7 1.2585926f-6; - 1.6564526f-8 2.175242f-7 5.672272f-7; - 1.1230781f-8 5.6625986f-8 6.422501f-7; - 2.0197302f-9 2.1163782f-8 7.294656f-7; - 2.6033277f-8 9.256203f-8 6.953829f-7; - 4.1351388f-8 2.883101f-8 1.4163764f-6; - 2.410564f-7 3.2959353f-8 3.149001f-7; - 1.1975986f-8 6.8810646f-9 2.3321163f-6; - 8.117884f-9 1.2723822f-7 5.457541f-7; - 2.5321859f-5 3.9635648f-8 6.0792405f-7; - 8.8945035f-7 3.1633346f-7 6.974225f-7; - 8.896321f-8 4.030092f-8 5.001396f-7; - 2.599902f-7 9.474226f-8 5.060795f-7; - 1.8978075f-7 3.539335f-8 1.1813174f-6; - 1.4317889f-5 8.917272f-8 6.3750626f-8; - 4.917164f-7 1.867334f-8 2.5667001f-8; - 9.358809f-8 2.2406724f-8 2.3201217f-7; - 1.6544444f-8 2.7522745f-7 1.5681597f-7; - 2.0142437f-7 6.664031f-8 1.3572262f-7; - 3.7246448f-8 4.2742393f-8 1.6831775f-7; - 0.0 0.0 0.0; - 0.9104578 0.98495096 0.7138366; - 2.0 17.0 3.0; + "od_v3_spp_COCO_dog-cycle-car" => Float32[ + 0.19689906 0.24459462 0.8887503 + 0.1488463 0.36018223 0.06057042 + 1.0473961 0.59275776 1.0014724 + 0.70960736 0.9316283 0.2309401 + 0.910512 0.9881821 0.8952051 + 1.930166e-5 8.159738e-6 4.7085752e-5 + 0.9104578 1.691023e-6 1.9126207e-6 + 1.3790263e-6 6.911531e-8 0.7138366 + 2.6012879e-6 1.2904401e-7 5.0861778e-5 + 1.1098012e-8 2.2086285e-8 6.1016376e-6 + 9.057754e-6 9.5129586e-8 0.00038733156 + 1.1161652e-6 8.367672e-9 2.2170327e-5 + 9.058946e-7 4.1705272e-7 0.29135424 + 4.5512534e-5 2.4128855e-7 5.8548985e-6 + 2.1115416e-7 1.15923955e-8 1.1526488e-6 + 8.1184663e-7 2.2253998e-7 1.5866458e-6 + 1.1245807e-8 1.8470355e-7 5.4033757e-7 + 1.08634204e-7 7.9246384e-8 4.4991725e-6 + 1.1923254e-6 5.1360627e-7 9.995203e-5 + 1.3927402e-6 1.7355974e-6 3.1599143e-8 + 3.81957e-6 0.0010601201 7.618114e-8 + 1.6381528e-6 0.98495096 1.6210147e-6 + 2.8651685e-8 1.2533204e-6 2.2698643e-5 + 2.608681e-8 2.8966385e-6 4.714277e-6 + 4.9097255e-9 1.2341462e-6 5.096642e-6 + 2.9978867e-8 8.6187976e-7 5.9160384e-6 + 4.2343538e-9 1.2508925e-5 4.3054132e-7 + 1.2049973e-8 9.0271364e-8 3.6232873e-6 + 9.647422e-8 3.205269e-7 2.1099694e-8 + 9.756159e-7 1.899152e-5 5.5800174e-6 + 2.1720164e-6 9.4082644e-8 5.9462027e-6 + 1.3667454e-6 2.1821234e-7 3.2749144e-6 + 3.8992642e-8 1.924836e-7 1.5601264e-8 + 9.319571e-6 9.669151e-7 3.1558318e-6 + 1.5815185e-7 1.7082317e-6 7.281923e-7 + 6.4362152e-6 1.120655e-6 4.391029e-7 + 4.4480068e-8 8.593095e-8 1.7877613e-7 + 3.2706e-7 3.6466574e-7 7.1183126e-7 + 2.4603432e-7 4.2563258e-7 3.8344479e-7 + 7.552904e-8 1.4771066e-8 6.0315065e-6 + 3.821396e-8 1.2727838e-7 1.4470459e-6 + 4.979707e-7 2.7458195e-6 2.3616168e-7 + 3.5113857e-7 5.0932704e-7 1.4618166e-7 + 5.279122e-6 4.51986e-7 5.927365e-7 + 1.202448e-7 1.0310528e-8 3.596275e-7 + 7.913564e-8 2.5259968e-8 4.3275762e-7 + 5.2092705e-8 4.315123e-8 3.3435597e-6 + 3.1152084e-7 4.4646398e-9 1.2317258e-6 + 3.043047e-8 6.802467e-9 3.9936265e-7 + 4.609466e-8 2.4053584e-8 2.109222e-6 + 6.650988e-8 1.1567878e-8 6.710031e-6 + 2.667538e-6 5.652369e-8 7.274405e-7 + 3.316609e-7 7.0341075e-9 8.953999e-8 + 3.0939333e-9 4.22922e-7 6.491611e-7 + 1.8770496e-8 1.5821362e-9 2.7103135e-6 + 4.937021e-7 1.8987889e-8 1.0990202e-6 + 3.6307281e-7 3.0497155e-10 7.155243e-7 + 4.9156004e-9 1.8683498e-7 4.2371803e-6 + 1.7441934e-6 9.320106e-6 7.78749e-8 + 3.4816299e-9 7.210677e-8 1.737384e-6 + 2.9242882e-8 6.208634e-8 1.0987446e-6 + 2.5622048e-5 1.7635933e-6 2.5303048e-5 + 1.26850415e-8 2.8108548e-7 1.6335434e-5 + 0.00053333153 1.0198573e-6 5.6205663e-5 + 1.1474223e-7 3.75714e-6 1.8207937e-6 + 3.6199378e-7 2.5305013e-7 1.2585926e-6 + 1.6564526e-8 2.175242e-7 5.672272e-7 + 1.1230781e-8 5.6625986e-8 6.422501e-7 + 2.0197302e-9 2.1163782e-8 7.294656e-7 + 2.6033277e-8 9.256203e-8 6.953829e-7 + 4.1351388e-8 2.883101e-8 1.4163764e-6 + 2.410564e-7 3.2959353e-8 3.149001e-7 + 1.1975986e-8 6.8810646e-9 2.3321163e-6 + 8.117884e-9 1.2723822e-7 5.457541e-7 + 2.5321859e-5 3.9635648e-8 6.0792405e-7 + 8.8945035e-7 3.1633346e-7 6.974225e-7 + 8.896321e-8 4.030092e-8 5.001396e-7 + 2.599902e-7 9.474226e-8 5.060795e-7 + 1.8978075e-7 3.539335e-8 1.1813174e-6 + 1.4317889e-5 8.917272e-8 6.3750626e-8 + 4.917164e-7 1.867334e-8 2.5667001e-8 + 9.358809e-8 2.2406724e-8 2.3201217e-7 + 1.6544444e-8 2.7522745e-7 1.5681597e-7 + 2.0142437e-7 6.664031e-8 1.3572262e-7 + 3.7246448e-8 4.2742393e-8 1.6831775e-7 + 0.0 0.0 0.0 + 0.9104578 0.98495096 0.7138366 + 2.0 17.0 3.0 1.0 1.0 1.0 ], - "od_v3_spp_COCO_dog-cycle-car_nonsquare" => [ - 0.18823412 0.24413626; - 0.16247767 0.23068884; - 1.0094361 0.59887177; - 0.6820583 0.8014146; - 0.7510926 0.99008596; - 4.335929f-5 1.6426953f-5; - 0.74724674 8.728225f-6; - 9.110433f-6 1.35974f-7; - 6.3066786f-5 3.8487784f-7; - 6.8189115f-7 9.753309f-9; - 0.0006248606 1.5477048f-7; - 9.461998f-8 3.2249248f-9; - 5.0184743f-5 1.7826544f-6; - 0.0024103988 5.3096016f-7; - 4.209413f-7 6.641051f-9; - 4.915594f-7 9.6615516f-8; - 6.525769f-8 7.9806114f-8; - 3.0564559f-7 9.304281f-8; - 8.599426f-5 7.380485f-7; - 3.935025f-6 4.741456f-7; - 4.854384f-5 0.00020515107; - 0.0033578007 0.9890212; - 4.035398f-7 3.886322f-7; - 1.2757583f-6 4.0162304f-6; - 5.491968f-8 2.6222924f-7; - 5.1529616f-7 1.7435381f-7; - 5.6900422f-8 2.8929812f-6; - 8.380851f-8 1.0693155f-8; - 9.401072f-7 1.4686363f-7; - 1.0757298f-5 1.6504633f-5; - 5.078025f-6 7.951742f-8; - 1.2194449f-6 1.2846276f-7; - 1.7270757f-7 1.2877776f-7; - 2.787614f-5 2.1802323f-6; - 2.5788927f-6 1.7254697f-6; - 8.407031f-5 6.470587f-7; - 1.7831688f-7 1.08143794f-7; - 7.7173524f-7 2.4746166f-7; - 4.008371f-6 3.214947f-7; - 8.749327f-7 1.4925758f-8; - 2.190018f-7 7.895748f-8; - 1.46510965f-5 5.8568307f-6; - 6.4549295f-6 4.1100742f-7; - 1.9430503f-5 1.0773457f-6; - 1.276726f-7 1.2156122f-8; - 1.4922196f-7 4.4826816f-8; - 1.3640096f-7 3.2519807f-8; - 1.2974688f-7 1.9066382f-9; - 6.658731f-8 7.702652f-9; - 7.825696f-8 3.0914418f-8; - 1.615733f-7 2.0027077f-8; - 1.1472288f-6 6.104772f-8; - 5.676568f-8 3.789612f-9; - 3.519764f-8 4.361378f-7; - 2.3792758f-8 1.1139133f-9; - 4.278863f-7 1.9611607f-8; - 3.3247733f-7 1.89739f-10; - 8.3198344f-8 1.7747227f-7; - 5.5232804f-6 1.6174346f-5; - 1.2334602f-8 6.1301144f-8; - 8.8537746f-8 7.903613f-8; - 0.00019718622 1.3628296f-5; - 1.3749595f-7 1.1342502f-6; - 6.0158265f-5 3.1322057f-7; - 2.4109768f-6 6.138057f-6; - 4.8796032f-6 2.3079085f-6; - 3.143719f-8 1.2942402f-7; - 2.4232412f-7 4.6344436f-8; - 2.8124752f-8 2.9049772f-8; - 6.460292f-8 5.9817864f-8; - 2.8136718f-8 2.833065f-8; - 3.3243103f-7 4.281453f-8; - 1.3251085f-7 8.896884f-9; - 1.1413348f-7 6.0445224f-8; - 1.2418358f-5 1.654085f-7; - 1.3847947f-6 1.9322852f-7; - 1.1979328f-7 4.0835296f-8; - 2.2538548f-7 9.469079f-8; - 1.01028f-6 5.9878595f-8; - 1.3725897f-5 4.6758192f-8; - 2.7727646f-7 9.608841f-9; - 2.8459021f-7 2.4791584f-8; - 1.8543525f-7 2.0675367f-7; - 6.061894f-7 5.223392f-8; - 5.394565f-8 2.1659144f-8; - 0.0 0.0; - 0.74724674 0.9890212; - 2.0 17.0; + "od_v3_spp_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.18823412 0.24413626 + 0.16247767 0.23068884 + 1.0094361 0.59887177 + 0.6820583 0.8014146 + 0.7510926 0.99008596 + 4.335929e-5 1.6426953e-5 + 0.74724674 8.728225e-6 + 9.110433e-6 1.35974e-7 + 6.3066786e-5 3.8487784e-7 + 6.8189115e-7 9.753309e-9 + 0.0006248606 1.5477048e-7 + 9.461998e-8 3.2249248e-9 + 5.0184743e-5 1.7826544e-6 + 0.0024103988 5.3096016e-7 + 4.209413e-7 6.641051e-9 + 4.915594e-7 9.6615516e-8 + 6.525769e-8 7.9806114e-8 + 3.0564559e-7 9.304281e-8 + 8.599426e-5 7.380485e-7 + 3.935025e-6 4.741456e-7 + 4.854384e-5 0.00020515107 + 0.0033578007 0.9890212 + 4.035398e-7 3.886322e-7 + 1.2757583e-6 4.0162304e-6 + 5.491968e-8 2.6222924e-7 + 5.1529616e-7 1.7435381e-7 + 5.6900422e-8 2.8929812e-6 + 8.380851e-8 1.0693155e-8 + 9.401072e-7 1.4686363e-7 + 1.0757298e-5 1.6504633e-5 + 5.078025e-6 7.951742e-8 + 1.2194449e-6 1.2846276e-7 + 1.7270757e-7 1.2877776e-7 + 2.787614e-5 2.1802323e-6 + 2.5788927e-6 1.7254697e-6 + 8.407031e-5 6.470587e-7 + 1.7831688e-7 1.08143794e-7 + 7.7173524e-7 2.4746166e-7 + 4.008371e-6 3.214947e-7 + 8.749327e-7 1.4925758e-8 + 2.190018e-7 7.895748e-8 + 1.46510965e-5 5.8568307e-6 + 6.4549295e-6 4.1100742e-7 + 1.9430503e-5 1.0773457e-6 + 1.276726e-7 1.2156122e-8 + 1.4922196e-7 4.4826816e-8 + 1.3640096e-7 3.2519807e-8 + 1.2974688e-7 1.9066382e-9 + 6.658731e-8 7.702652e-9 + 7.825696e-8 3.0914418e-8 + 1.615733e-7 2.0027077e-8 + 1.1472288e-6 6.104772e-8 + 5.676568e-8 3.789612e-9 + 3.519764e-8 4.361378e-7 + 2.3792758e-8 1.1139133e-9 + 4.278863e-7 1.9611607e-8 + 3.3247733e-7 1.89739e-10 + 8.3198344e-8 1.7747227e-7 + 5.5232804e-6 1.6174346e-5 + 1.2334602e-8 6.1301144e-8 + 8.8537746e-8 7.903613e-8 + 0.00019718622 1.3628296e-5 + 1.3749595e-7 1.1342502e-6 + 6.0158265e-5 3.1322057e-7 + 2.4109768e-6 6.138057e-6 + 4.8796032e-6 2.3079085e-6 + 3.143719e-8 1.2942402e-7 + 2.4232412e-7 4.6344436e-8 + 2.8124752e-8 2.9049772e-8 + 6.460292e-8 5.9817864e-8 + 2.8136718e-8 2.833065e-8 + 3.3243103e-7 4.281453e-8 + 1.3251085e-7 8.896884e-9 + 1.1413348e-7 6.0445224e-8 + 1.2418358e-5 1.654085e-7 + 1.3847947e-6 1.9322852e-7 + 1.1979328e-7 4.0835296e-8 + 2.2538548e-7 9.469079e-8 + 1.01028e-6 5.9878595e-8 + 1.3725897e-5 4.6758192e-8 + 2.7727646e-7 9.608841e-9 + 2.8459021e-7 2.4791584e-8 + 1.8543525e-7 2.0675367e-7 + 6.061894e-7 5.223392e-8 + 5.394565e-8 2.1659144e-8 + 0.0 0.0 + 0.74724674 0.9890212 + 2.0 17.0 1.0 1.0 ], - "od_v3_tiny_COCO_dog-cycle-car" => [ - 0.25070268 0.86342007 0.90347874 0.9061836; - 0.31197208 0.048579663 0.09315592 0.06912026; - 0.63642 0.9952955 0.98370415 0.9592236; - 0.8776546 0.22791806 0.17728066 0.20277503; - 0.7199427 0.67497426 0.75653523 0.7579072; - 3.6946796f-5 6.3520447f-6 2.77061f-5 1.8442268f-5; - 0.0017374302 1.543739f-6 7.3751386f-7 3.8403897f-8; - 9.999582f-5 0.530212 0.6647045 0.60105085; - 0.0001341944 1.3482474f-5 2.0091425f-6 9.763955f-7; - 6.089481f-7 1.560517f-6 9.422702f-7 2.992451f-7; - 4.092186f-8 0.00027738052 0.00038814504 0.000281254; - 8.798341f-8 1.073583f-5 1.962159f-6 8.7326947f-7; - 2.2165596f-6 0.1921335 0.14133048 0.25427753; - 1.2438655f-5 7.998351f-6 1.3629677f-6 5.486417f-7; - 1.3224232f-7 2.8583747f-6 3.3069423f-6 3.287688f-6; - 4.0329373f-6 1.8347928f-5 4.6438683f-7 1.0856575f-6; - 5.6914807f-8 4.007585f-6 8.498192f-7 1.0553737f-7; - 1.2362788f-7 1.9625051f-5 3.0365749f-5 4.8831403f-6; - 6.8263085f-5 1.2201207f-6 2.3172277f-6 2.0796793f-7; - 2.0136313f-5 1.2377592f-7 3.435223f-8 3.0009484f-9; - 0.024718968 1.6535286f-7 3.9040572f-7 9.2202086f-8; - 0.71403086 2.853256f-7 1.1416093f-5 1.3384434f-6; - 2.2500724f-5 2.830204f-7 5.285307f-7 5.0497334f-8; - 1.4299682f-5 1.1013805f-6 1.0987314f-7 2.176558f-8; - 7.317742f-5 4.9694854f-6 4.424348f-7 5.3142975f-7; - 6.472217f-7 1.0862758f-6 1.1030551f-6 3.826297f-7; - 1.12473035f-5 3.1961074f-6 4.2890312f-7 1.01167565f-7; - 8.9796536f-8 8.349069f-5 2.3314674f-6 3.150383f-6; - 7.4816044f-6 1.7608488f-6 1.3021292f-6 2.8035467f-7; - 0.00014708508 3.878815f-6 1.21547555f-5 5.9044523f-6; - 5.4220372f-6 2.0368886f-6 5.436459f-7 1.4044745f-7; - 1.0828776f-5 2.6254506f-6 3.0320205f-5 3.5790258f-6; - 3.311594f-6 3.1348063f-7 3.9318414f-7 1.6938445f-8; - 4.938679f-5 1.1169496f-5 5.6549666f-6 1.279068f-6; - 1.766856f-6 5.3935554f-7 2.140484f-7 4.679406f-8; - 9.845927f-7 1.6050532f-7 1.6736452f-7 4.4654193f-8; - 2.1260563f-7 2.4880703f-7 5.5792987f-7 4.1866322f-8; - 6.34402f-7 1.5998732f-5 1.6410854f-6 6.5423967f-7; - 2.548826f-6 4.7061158f-7 3.8200665f-7 8.0720376f-8; - 1.8909933f-6 1.5942737f-7 5.07034f-8 6.3764007f-9; - 8.989167f-7 1.1639756f-5 1.2524241f-8 1.3407076f-7; - 2.5302238f-6 9.590806f-7 2.8609256f-7 5.7947403f-8; - 5.2059977f-7 3.9850502f-7 5.7413604f-7 4.8860308f-8; - 8.630877f-6 1.5090544f-6 3.192704f-7 2.2891154f-8; - 1.7869452f-6 6.3255203f-7 2.1100962f-7 7.995285f-8; - 2.2984419f-5 3.15873f-7 2.0042906f-8 1.5291464f-8; - 3.684746f-6 5.4192685f-7 4.8221054f-6 5.631381f-7; - 3.7174527f-8 5.9328494f-7 6.3027656f-8 7.775973f-9; - 1.2797616f-7 3.65732f-7 1.8444963f-7 2.688885f-8; - 1.4480156f-6 3.0307558f-7 1.2392829f-7 1.0401096f-8; - 7.5166565f-7 2.2061834f-6 9.0823323f-7 1.4701315f-7; - 2.2699686f-7 3.330352f-6 4.6841043f-7 5.9536617f-8; - 1.839243f-7 2.0931277f-6 7.1956784f-8 2.613853f-8; - 7.5725706f-7 5.333149f-7 9.849815f-9 9.31662f-9; - 1.4810817f-7 6.1284973f-6 7.359311f-8 1.9711214f-8; - 9.698992f-8 4.332167f-7 1.1827153f-8 2.6504969f-8; - 1.1141073f-7 3.7851232f-6 1.715555f-7 4.213284f-8; - 2.9080587f-7 1.808514f-7 3.584823f-8 2.3250428f-9; - 3.243794f-6 2.8045693f-7 1.4469876f-7 3.243043f-8; - 1.6736615f-7 3.5666588f-5 1.8501713f-6 2.0994391f-6; - 2.8674137f-7 2.845521f-6 3.1248235f-6 5.5119663f-6; - 0.000615658 4.460225f-6 2.40385f-6 2.8206918f-7; - 0.00015491586 2.7028967f-5 5.266183f-6 3.177688f-6; - 3.192292f-6 4.6788273f-6 2.8147184f-7 3.1650586f-7; - 0.000114420225 2.3589587f-6 4.9840486f-7 1.4900549f-7; - 6.811624f-6 5.3425325f-8 1.2958571f-6 1.0018415f-6; - 7.771735f-5 1.8801267f-5 7.700046f-7 7.4855376f-7; - 7.645848f-7 1.9413735f-6 1.359818f-6 1.3699238f-6; - 8.147689f-6 5.669804f-7 1.9727338f-6 2.4961932f-7; - 1.3650114f-6 9.047882f-6 3.6931405f-7 6.4860814f-7; - 1.2456113f-6 2.1199246f-6 1.4806617f-6 6.134846f-7; - 2.3017496f-5 7.165744f-7 9.253388f-7 9.943693f-7; - 3.696221f-7 2.186955f-5 7.92138f-6 2.9906275f-6; - 6.470255f-7 2.537061f-6 1.5725794f-6 5.8302436f-7; - 8.8042947f-7 4.054257f-6 6.4628985f-8 3.7774947f-7; - 1.7851941f-6 4.4717322f-5 3.467298f-6 2.9588991f-6; - 9.274161f-5 5.9935237f-6 6.019442f-6 9.4694104f-7; - 6.2037014f-7 6.836238f-6 2.1693494f-7 3.5901806f-8; - 1.3763499f-6 1.805932f-7 6.03484f-7 1.7707647f-8; - 8.780863f-8 8.958647f-6 2.6856544f-6 1.6682166f-6; - 1.3076234f-6 7.253918f-7 1.7543325f-8 7.475385f-9; - 1.1790255f-6 3.699489f-7 6.2015246f-8 2.0027922f-8; - 2.20716f-6 9.079005f-8 8.219807f-8 1.4729659f-8; - 4.862006f-6 5.4297443f-6 1.7366732f-7 1.9869209f-7; - 6.911949f-7 7.6199946f-8 4.5365874f-7 5.191371f-8; - 0.0 0.0 0.0 0.0; - 0.71403086 0.530212 0.6647045 0.60105085; - 17.0 3.0 3.0 3.0; + "od_v3_tiny_COCO_dog-cycle-car" => Float32[ + 0.25070268 0.86342007 0.90347874 0.9061836 + 0.31197208 0.048579663 0.09315592 0.06912026 + 0.63642 0.9952955 0.98370415 0.9592236 + 0.8776546 0.22791806 0.17728066 0.20277503 + 0.7199427 0.67497426 0.75653523 0.7579072 + 3.6946796e-5 6.3520447e-6 2.77061e-5 1.8442268e-5 + 0.0017374302 1.543739e-6 7.3751386e-7 3.8403897e-8 + 9.999582e-5 0.530212 0.6647045 0.60105085 + 0.0001341944 1.3482474e-5 2.0091425e-6 9.763955e-7 + 6.089481e-7 1.560517e-6 9.422702e-7 2.992451e-7 + 4.092186e-8 0.00027738052 0.00038814504 0.000281254 + 8.798341e-8 1.073583e-5 1.962159e-6 8.7326947e-7 + 2.2165596e-6 0.1921335 0.14133048 0.25427753 + 1.2438655e-5 7.998351e-6 1.3629677e-6 5.486417e-7 + 1.3224232e-7 2.8583747e-6 3.3069423e-6 3.287688e-6 + 4.0329373e-6 1.8347928e-5 4.6438683e-7 1.0856575e-6 + 5.6914807e-8 4.007585e-6 8.498192e-7 1.0553737e-7 + 1.2362788e-7 1.9625051e-5 3.0365749e-5 4.8831403e-6 + 6.8263085e-5 1.2201207e-6 2.3172277e-6 2.0796793e-7 + 2.0136313e-5 1.2377592e-7 3.435223e-8 3.0009484e-9 + 0.024718968 1.6535286e-7 3.9040572e-7 9.2202086e-8 + 0.71403086 2.853256e-7 1.1416093e-5 1.3384434e-6 + 2.2500724e-5 2.830204e-7 5.285307e-7 5.0497334e-8 + 1.4299682e-5 1.1013805e-6 1.0987314e-7 2.176558e-8 + 7.317742e-5 4.9694854e-6 4.424348e-7 5.3142975e-7 + 6.472217e-7 1.0862758e-6 1.1030551e-6 3.826297e-7 + 1.12473035e-5 3.1961074e-6 4.2890312e-7 1.01167565e-7 + 8.9796536e-8 8.349069e-5 2.3314674e-6 3.150383e-6 + 7.4816044e-6 1.7608488e-6 1.3021292e-6 2.8035467e-7 + 0.00014708508 3.878815e-6 1.21547555e-5 5.9044523e-6 + 5.4220372e-6 2.0368886e-6 5.436459e-7 1.4044745e-7 + 1.0828776e-5 2.6254506e-6 3.0320205e-5 3.5790258e-6 + 3.311594e-6 3.1348063e-7 3.9318414e-7 1.6938445e-8 + 4.938679e-5 1.1169496e-5 5.6549666e-6 1.279068e-6 + 1.766856e-6 5.3935554e-7 2.140484e-7 4.679406e-8 + 9.845927e-7 1.6050532e-7 1.6736452e-7 4.4654193e-8 + 2.1260563e-7 2.4880703e-7 5.5792987e-7 4.1866322e-8 + 6.34402e-7 1.5998732e-5 1.6410854e-6 6.5423967e-7 + 2.548826e-6 4.7061158e-7 3.8200665e-7 8.0720376e-8 + 1.8909933e-6 1.5942737e-7 5.07034e-8 6.3764007e-9 + 8.989167e-7 1.1639756e-5 1.2524241e-8 1.3407076e-7 + 2.5302238e-6 9.590806e-7 2.8609256e-7 5.7947403e-8 + 5.2059977e-7 3.9850502e-7 5.7413604e-7 4.8860308e-8 + 8.630877e-6 1.5090544e-6 3.192704e-7 2.2891154e-8 + 1.7869452e-6 6.3255203e-7 2.1100962e-7 7.995285e-8 + 2.2984419e-5 3.15873e-7 2.0042906e-8 1.5291464e-8 + 3.684746e-6 5.4192685e-7 4.8221054e-6 5.631381e-7 + 3.7174527e-8 5.9328494e-7 6.3027656e-8 7.775973e-9 + 1.2797616e-7 3.65732e-7 1.8444963e-7 2.688885e-8 + 1.4480156e-6 3.0307558e-7 1.2392829e-7 1.0401096e-8 + 7.5166565e-7 2.2061834e-6 9.0823323e-7 1.4701315e-7 + 2.2699686e-7 3.330352e-6 4.6841043e-7 5.9536617e-8 + 1.839243e-7 2.0931277e-6 7.1956784e-8 2.613853e-8 + 7.5725706e-7 5.333149e-7 9.849815e-9 9.31662e-9 + 1.4810817e-7 6.1284973e-6 7.359311e-8 1.9711214e-8 + 9.698992e-8 4.332167e-7 1.1827153e-8 2.6504969e-8 + 1.1141073e-7 3.7851232e-6 1.715555e-7 4.213284e-8 + 2.9080587e-7 1.808514e-7 3.584823e-8 2.3250428e-9 + 3.243794e-6 2.8045693e-7 1.4469876e-7 3.243043e-8 + 1.6736615e-7 3.5666588e-5 1.8501713e-6 2.0994391e-6 + 2.8674137e-7 2.845521e-6 3.1248235e-6 5.5119663e-6 + 0.000615658 4.460225e-6 2.40385e-6 2.8206918e-7 + 0.00015491586 2.7028967e-5 5.266183e-6 3.177688e-6 + 3.192292e-6 4.6788273e-6 2.8147184e-7 3.1650586e-7 + 0.000114420225 2.3589587e-6 4.9840486e-7 1.4900549e-7 + 6.811624e-6 5.3425325e-8 1.2958571e-6 1.0018415e-6 + 7.771735e-5 1.8801267e-5 7.700046e-7 7.4855376e-7 + 7.645848e-7 1.9413735e-6 1.359818e-6 1.3699238e-6 + 8.147689e-6 5.669804e-7 1.9727338e-6 2.4961932e-7 + 1.3650114e-6 9.047882e-6 3.6931405e-7 6.4860814e-7 + 1.2456113e-6 2.1199246e-6 1.4806617e-6 6.134846e-7 + 2.3017496e-5 7.165744e-7 9.253388e-7 9.943693e-7 + 3.696221e-7 2.186955e-5 7.92138e-6 2.9906275e-6 + 6.470255e-7 2.537061e-6 1.5725794e-6 5.8302436e-7 + 8.8042947e-7 4.054257e-6 6.4628985e-8 3.7774947e-7 + 1.7851941e-6 4.4717322e-5 3.467298e-6 2.9588991e-6 + 9.274161e-5 5.9935237e-6 6.019442e-6 9.4694104e-7 + 6.2037014e-7 6.836238e-6 2.1693494e-7 3.5901806e-8 + 1.3763499e-6 1.805932e-7 6.03484e-7 1.7707647e-8 + 8.780863e-8 8.958647e-6 2.6856544e-6 1.6682166e-6 + 1.3076234e-6 7.253918e-7 1.7543325e-8 7.475385e-9 + 1.1790255e-6 3.699489e-7 6.2015246e-8 2.0027922e-8 + 2.20716e-6 9.079005e-8 8.219807e-8 1.4729659e-8 + 4.862006e-6 5.4297443e-6 1.7366732e-7 1.9869209e-7 + 6.911949e-7 7.6199946e-8 4.5365874e-7 5.191371e-8 + 0.0 0.0 0.0 0.0 + 0.71403086 0.530212 0.6647045 0.60105085 + 17.0 3.0 3.0 3.0 1.0 1.0 1.0 1.0 ], - "od_v3_tiny_COCO_dog-cycle-car_nonsquare" => [ - 0.2381582; 0.22545278; 0.6233994; 0.7690957; 0.783968; 8.106858f-5; 0.00037950932; 0.0002556421; 3.9762228f-5; 7.792718f-7; 6.961448f-8; 1.1884973f-7; 5.4418606f-6; 2.0008072f-5; 2.9955157f-7; 3.4479403f-6; 1.5571422f-7; 2.640694f-7; 7.4997435f-5; 3.879428f-5; 0.079218246; 0.7681299; 1.932912f-5; 2.6602856f-5; 5.34549f-5; 4.2219085f-7; 0.00012267525; 7.90364f-8; 6.540139f-6; 9.053799f-5; 4.723675f-6; 1.5373751f-5; 6.2462136f-6; 5.5804565f-5; 5.4916122f-6; 8.598918f-7; 4.7022098f-7; 1.8363446f-6; 1.4790954f-6; 1.725651f-6; 5.5688815f-7; 1.9748222f-6; 1.2294502f-6; 3.8115845f-6; 3.8086266f-6; 1.436193f-5; 4.018075f-6; 3.5998468f-8; 3.0277752f-7; 2.1069518f-6; 4.048033f-6; 1.6428647f-6; 6.310958f-7; 2.7941394f-6; 6.507534f-7; 1.7333957f-7; 2.2756676f-7; 7.119433f-7; 5.1212673f-6; 1.521049f-6; 2.465717f-6; 0.00029336117; 0.0003004471; 5.5749115f-6; 0.00039306172; 1.5655347f-5; 0.00024058059; 1.8381494f-6; 1.367301f-5; 4.9805035f-6; 7.1206077f-6; 2.5164965f-5; 5.495248f-7; 6.667494f-7; 2.2684387f-6; 2.3121381f-6; 0.00054974126; 3.1236632f-6; 2.9696969f-6; 1.4562076f-7; 2.4266587f-6; 1.7900652f-6; 6.967112f-6; 5.986008f-6; 1.2340632f-6; 0.0; 0.7681299; 17.0; 1.0;; - ], - "od_v4_COCO_dog-cycle-car" => [ - 0.2418581 0.24869773 0.884411; - 0.14170471 0.35913965 0.057404235; - 1.0096806 0.58869755 0.99986273; - 0.7194482 0.9311234 0.220668; - 0.9289863 0.9818553 0.97336596; - 8.269788f-6 3.2998294f-5 3.064497f-5; - 0.9288855 2.010352f-5 5.582407f-6; - 2.0190743f-7 8.422477f-8 0.95234585; - 1.572774f-5 2.1410828f-7 2.22413f-5; - 1.1283716f-8 3.2488426f-10 1.3379597f-5; - 7.667304f-6 8.637037f-9 0.00021753744; - 4.035973f-6 2.0763036f-9 7.2779308f-6; - 6.37036f-7 5.685189f-8 0.07093087; - 6.3546763f-6 5.123186f-8 1.9594641f-5; - 3.0632915f-8 4.4431343f-9 4.337505f-6; - 2.7353164f-7 2.3037383f-6 2.22388f-6; - 1.1050809f-6 4.3179092f-8 2.865916f-6; - 4.6950515f-7 1.6420857f-7 2.2753924f-5; - 3.9537994f-5 9.0995735f-7 1.47904775f-5; - 4.5572665f-7 9.674271f-6 1.6566472f-7; - 8.952011f-7 0.00041836334 6.273153f-7; - 2.3969762f-6 0.98138404 9.3955947f-7; - 1.0623897f-7 3.6085366f-6 1.2653354f-6; - 8.492932f-6 6.6312154f-5 5.927627f-6; - 2.5604768f-8 5.53853f-6 9.107111f-6; - 6.2399397f-9 5.832744f-7 1.8502425f-6; - 5.516336f-9 1.0963395f-5 2.018962f-6; - 3.4016434f-8 5.914142f-8 6.1852425f-6; - 7.1584424f-8 9.987565f-8 9.525132f-7; - 1.044242f-6 1.2314568f-5 4.7199183f-6; - 4.410769f-6 2.1847942f-8 3.85706f-6; - 2.6470215f-7 4.2619874f-7 2.1188544f-6; - 7.046015f-7 7.914585f-8 2.744543f-7; - 2.8539343f-6 4.6700512f-7 2.9201988f-6; - 2.5410168f-7 3.5116152f-6 2.1054916f-6; - 7.077694f-5 1.1200679f-7 1.3047734f-6; - 1.8601649f-7 1.9138836f-8 1.992968f-6; - 8.0183196f-7 1.03693516f-7 3.6498457f-6; - 1.8413211f-6 4.258928f-7 2.2897689f-6; - 4.889145f-7 9.223586f-9 3.001902f-6; - 8.515384f-8 4.534755f-7 9.4013666f-7; - 4.6156273f-7 1.7745375f-6 2.2646168f-6; - 1.4737138f-6 3.4056126f-7 1.3167712f-6; - 2.4761473f-5 7.756722f-8 1.9953986f-6; - 3.8276144f-7 8.399104f-8 9.589883f-7; - 1.381643f-7 2.2086134f-8 4.744514f-7; - 1.9104833f-8 3.647359f-8 1.0533954f-5; - 1.620776f-5 1.1173533f-8 1.3686259f-6; - 4.0187942f-8 9.281024f-10 3.1105228f-7; - 1.7343092f-7 8.634994f-9 1.2631363f-6; - 6.350072f-10 1.6143336f-9 5.8324845f-6; - 3.1991815f-6 5.47453f-7 3.4980786f-7; - 4.898775f-9 7.808536f-10 8.43164f-7; - 1.7400217f-9 6.0826926f-7 6.329406f-7; - 1.3659424f-7 2.4478652f-9 1.5293307f-6; - 2.7727424f-7 2.4627832f-7 2.8428544f-6; - 1.8752455f-6 1.2142151f-9 4.9534094f-7; - 1.9032156f-9 5.744223f-8 7.6738894f-7; - 8.02633f-7 2.0463535f-6 1.9989743f-7; - 5.4599806f-8 1.700761f-7 1.0251144f-6; - 9.836272f-10 2.8809817f-8 2.8874146f-7; - 4.088106f-5 2.7215451f-6 6.300429f-6; - 2.1911661f-7 4.642387f-7 2.8254642f-6; - 1.412526f-6 2.1279136f-6 4.186861f-6; - 6.931229f-7 7.9598635f-7 7.0697826f-7; - 3.5194876f-6 2.3448615f-7 8.852207f-7; - 8.375659f-9 8.590857f-8 3.458849f-6; - 2.7782953f-8 2.747892f-9 6.311392f-6; - 4.983452f-9 1.4717647f-8 1.7548044f-6; - 1.808658f-7 2.1996673f-8 5.1257175f-6; - 9.9261236f-8 1.6132754f-8 2.175897f-6; - 9.0969746f-7 1.8566597f-8 2.9697285f-6; - 4.0833488f-9 1.598691f-9 5.712893f-6; - 2.4843806f-7 9.988365f-8 4.587525f-6; - 2.075809f-5 1.8578465f-8 9.741626f-7; - 4.1169f-7 3.8719072f-8 3.7708428f-6; - 5.6319443f-8 5.1995887f-8 4.777787f-6; - 8.155214f-5 1.5662401f-8 1.6980013f-6; - 1.6335029f-5 1.2212001f-7 6.3248626f-7; - 4.6740265f-6 1.16199f-8 9.11628f-7; - 2.7912665f-7 8.719696f-10 1.6525745f-7; - 3.6819808f-6 5.8901356f-10 5.4415256f-7; - 5.9977103f-9 6.337361f-6 2.4323998f-7; - 7.987737f-7 4.8706717f-8 1.6061184f-6; - 2.348542f-6 2.2639755f-8 4.112084f-6; - 0.0 0.0 0.0; - 0.9288855 0.98138404 0.95234585; - 2.0 17.0 3.0; + "od_v3_tiny_COCO_dog-cycle-car_nonsquare" => Float32[0.2381582; 0.22545278; 0.6233994; 0.7690957; 0.783968; 8.106858f-5; 0.00037950932; 0.0002556421; 3.9762228f-5; 7.792718f-7; 6.961448f-8; 1.1884973f-7; 5.4418606f-6; 2.0008072f-5; 2.9955157f-7; 3.4479403f-6; 1.5571422f-7; 2.640694f-7; 7.4997435f-5; 3.879428f-5; 0.079218246; 0.7681299; 1.932912f-5; 2.6602856f-5; 5.34549f-5; 4.2219085f-7; 0.00012267525; 7.90364f-8; 6.540139f-6; 9.053799f-5; 4.723675f-6; 1.5373751f-5; 6.2462136f-6; 5.5804565f-5; 5.4916122f-6; 8.598918f-7; 4.7022098f-7; 1.8363446f-6; 1.4790954f-6; 1.725651f-6; 5.5688815f-7; 1.9748222f-6; 1.2294502f-6; 3.8115845f-6; 3.8086266f-6; 1.436193f-5; 4.018075f-6; 3.5998468f-8; 3.0277752f-7; 2.1069518f-6; 4.048033f-6; 1.6428647f-6; 6.310958f-7; 2.7941394f-6; 6.507534f-7; 1.7333957f-7; 2.2756676f-7; 7.119433f-7; 5.1212673f-6; 1.521049f-6; 2.465717f-6; 0.00029336117; 0.0003004471; 5.5749115f-6; 0.00039306172; 1.5655347f-5; 0.00024058059; 1.8381494f-6; 1.367301f-5; 4.9805035f-6; 7.1206077f-6; 2.5164965f-5; 5.495248f-7; 6.667494f-7; 2.2684387f-6; 2.3121381f-6; 0.00054974126; 3.1236632f-6; 2.9696969f-6; 1.4562076f-7; 2.4266587f-6; 1.7900652f-6; 6.967112f-6; 5.986008f-6; 1.2340632f-6; 0.0; 0.7681299; 17.0; 1.0;;], + "od_v4_COCO_dog-cycle-car" => Float32[ + 0.2418581 0.24869773 0.884411 + 0.14170471 0.35913965 0.057404235 + 1.0096806 0.58869755 0.99986273 + 0.7194482 0.9311234 0.220668 + 0.9289863 0.9818553 0.97336596 + 8.269788e-6 3.2998294e-5 3.064497e-5 + 0.9288855 2.010352e-5 5.582407e-6 + 2.0190743e-7 8.422477e-8 0.95234585 + 1.572774e-5 2.1410828e-7 2.22413e-5 + 1.1283716e-8 3.2488426e-10 1.3379597e-5 + 7.667304e-6 8.637037e-9 0.00021753744 + 4.035973e-6 2.0763036e-9 7.2779308e-6 + 6.37036e-7 5.685189e-8 0.07093087 + 6.3546763e-6 5.123186e-8 1.9594641e-5 + 3.0632915e-8 4.4431343e-9 4.337505e-6 + 2.7353164e-7 2.3037383e-6 2.22388e-6 + 1.1050809e-6 4.3179092e-8 2.865916e-6 + 4.6950515e-7 1.6420857e-7 2.2753924e-5 + 3.9537994e-5 9.0995735e-7 1.47904775e-5 + 4.5572665e-7 9.674271e-6 1.6566472e-7 + 8.952011e-7 0.00041836334 6.273153e-7 + 2.3969762e-6 0.98138404 9.3955947e-7 + 1.0623897e-7 3.6085366e-6 1.2653354e-6 + 8.492932e-6 6.6312154e-5 5.927627e-6 + 2.5604768e-8 5.53853e-6 9.107111e-6 + 6.2399397e-9 5.832744e-7 1.8502425e-6 + 5.516336e-9 1.0963395e-5 2.018962e-6 + 3.4016434e-8 5.914142e-8 6.1852425e-6 + 7.1584424e-8 9.987565e-8 9.525132e-7 + 1.044242e-6 1.2314568e-5 4.7199183e-6 + 4.410769e-6 2.1847942e-8 3.85706e-6 + 2.6470215e-7 4.2619874e-7 2.1188544e-6 + 7.046015e-7 7.914585e-8 2.744543e-7 + 2.8539343e-6 4.6700512e-7 2.9201988e-6 + 2.5410168e-7 3.5116152e-6 2.1054916e-6 + 7.077694e-5 1.1200679e-7 1.3047734e-6 + 1.8601649e-7 1.9138836e-8 1.992968e-6 + 8.0183196e-7 1.03693516e-7 3.6498457e-6 + 1.8413211e-6 4.258928e-7 2.2897689e-6 + 4.889145e-7 9.223586e-9 3.001902e-6 + 8.515384e-8 4.534755e-7 9.4013666e-7 + 4.6156273e-7 1.7745375e-6 2.2646168e-6 + 1.4737138e-6 3.4056126e-7 1.3167712e-6 + 2.4761473e-5 7.756722e-8 1.9953986e-6 + 3.8276144e-7 8.399104e-8 9.589883e-7 + 1.381643e-7 2.2086134e-8 4.744514e-7 + 1.9104833e-8 3.647359e-8 1.0533954e-5 + 1.620776e-5 1.1173533e-8 1.3686259e-6 + 4.0187942e-8 9.281024e-10 3.1105228e-7 + 1.7343092e-7 8.634994e-9 1.2631363e-6 + 6.350072e-10 1.6143336e-9 5.8324845e-6 + 3.1991815e-6 5.47453e-7 3.4980786e-7 + 4.898775e-9 7.808536e-10 8.43164e-7 + 1.7400217e-9 6.0826926e-7 6.329406e-7 + 1.3659424e-7 2.4478652e-9 1.5293307e-6 + 2.7727424e-7 2.4627832e-7 2.8428544e-6 + 1.8752455e-6 1.2142151e-9 4.9534094e-7 + 1.9032156e-9 5.744223e-8 7.6738894e-7 + 8.02633e-7 2.0463535e-6 1.9989743e-7 + 5.4599806e-8 1.700761e-7 1.0251144e-6 + 9.836272e-10 2.8809817e-8 2.8874146e-7 + 4.088106e-5 2.7215451e-6 6.300429e-6 + 2.1911661e-7 4.642387e-7 2.8254642e-6 + 1.412526e-6 2.1279136e-6 4.186861e-6 + 6.931229e-7 7.9598635e-7 7.0697826e-7 + 3.5194876e-6 2.3448615e-7 8.852207e-7 + 8.375659e-9 8.590857e-8 3.458849e-6 + 2.7782953e-8 2.747892e-9 6.311392e-6 + 4.983452e-9 1.4717647e-8 1.7548044e-6 + 1.808658e-7 2.1996673e-8 5.1257175e-6 + 9.9261236e-8 1.6132754e-8 2.175897e-6 + 9.0969746e-7 1.8566597e-8 2.9697285e-6 + 4.0833488e-9 1.598691e-9 5.712893e-6 + 2.4843806e-7 9.988365e-8 4.587525e-6 + 2.075809e-5 1.8578465e-8 9.741626e-7 + 4.1169e-7 3.8719072e-8 3.7708428e-6 + 5.6319443e-8 5.1995887e-8 4.777787e-6 + 8.155214e-5 1.5662401e-8 1.6980013e-6 + 1.6335029e-5 1.2212001e-7 6.3248626e-7 + 4.6740265e-6 1.16199e-8 9.11628e-7 + 2.7912665e-7 8.719696e-10 1.6525745e-7 + 3.6819808e-6 5.8901356e-10 5.4415256e-7 + 5.9977103e-9 6.337361e-6 2.4323998e-7 + 7.987737e-7 4.8706717e-8 1.6061184e-6 + 2.348542e-6 2.2639755e-8 4.112084e-6 + 0.0 0.0 0.0 + 0.9288855 0.98138404 0.95234585 + 2.0 17.0 3.0 1.0 1.0 1.0 ], - "od_v4_COCO_dog-cycle-car_nonsquare" => [ - 0.24385607; 0.2188136; 0.5997302; 0.82135123; 0.7500973; 0.00023252431; 0.0007803494; 6.0792013f-7; 3.4914128f-6; 6.412752f-9; 1.8743056f-7; 4.3772996f-8; 7.4116383f-7; 1.2322782f-6; 2.6664713f-8; 8.044653f-6; 2.1694233f-7; 1.8679924f-6; 2.1710104f-5; 0.000103658145; 0.0047235927; 0.7457102; 1.8176132f-5; 0.00053209177; 8.312888f-6; 7.035149f-7; 1.8633238f-5; 4.5999613f-7; 6.300148f-7; 2.9088998f-5; 2.8472394f-7; 2.7194733f-6; 6.0125495f-7; 1.2981325f-6; 9.952989f-6; 1.1205373f-6; 1.323928f-7; 5.919096f-7; 8.532816f-7; 1.08071205f-7; 1.1888196f-6; 5.0686217f-6; 1.9517968f-6; 1.1891767f-6; 9.3203937f-7; 3.6832168f-7; 3.7349594f-7; 2.229868f-7; 1.2003035f-8; 1.6744892f-7; 2.6590511f-8; 3.495714f-6; 1.11521175f-8; 1.4375114f-6; 2.672403f-8; 2.1336512f-6; 1.5169727f-8; 1.2949327f-7; 1.6064594f-5; 1.0558648f-6; 3.75138f-7; 3.0361789f-5; 2.1351434f-6; 1.4093226f-5; 2.7482984f-6; 4.40722f-6; 8.228406f-7; 3.4522238f-8; 1.14341404f-7; 2.2691232f-7; 1.3334162f-7; 3.6704287f-7; 1.3577349f-8; 5.2699335f-7; 7.38859f-7; 2.894171f-7; 4.3963263f-7; 3.789401f-7; 2.4876786f-6; 2.1359523f-7; 6.746143f-8; 2.248321f-8; 1.8635088f-5; 4.953993f-7; 4.323774f-7; 0.0; 0.7457102; 17.0; 1.0;; - ], - "od_v4_tiny_COCO_dog-cycle-car" => [ - 0.25264323 0.88405305; - 0.33418563 0.06083685; - 0.60824335 1.0000688; - 0.8501828 0.21100688; - 0.8116536 0.98294824; - 1.0290358f-5 6.8941365f-5; - 1.028921f-5 3.1989812f-6; - 3.7023812f-5 0.88535506; - 1.8356855f-6 8.1438935f-5; - 2.3669368f-7 8.762663f-6; - 1.5906524f-7 0.00025538504; - 4.1937323f-8 3.7717284f-6; - 6.1683627f-6 0.13692191; - 8.384943f-6 7.774879f-6; - 1.6155244f-7 1.3341888f-6; - 1.5220728f-7 1.42909485f-5; - 1.0434254f-6 1.973386f-6; - 1.5227679f-6 2.219546f-5; - 0.00031010024 3.6171627f-6; - 1.362012f-5 1.1509221f-8; - 0.003598087 6.387791f-8; - 0.80551404 4.140792f-6; - 1.2294284f-7 1.8664535f-6; - 4.0039163f-6 1.9627709f-7; - 1.7741618f-6 1.3418464f-6; - 2.4750105f-7 6.9187087f-7; - 8.410755f-6 3.628118f-6; - 5.3287954f-7 2.7049066f-7; - 3.3056537f-7 8.175751f-7; - 0.00064245495 5.7913593f-7; - 6.7649135f-6 7.591073f-7; - 7.278294f-5 1.4066734f-6; - 9.2135735f-7 5.7760315f-7; - 0.0009110165 1.0901628f-6; - 1.2088527f-5 1.1644975f-6; - 1.4032729f-8 3.1706963f-7; - 6.131138f-8 2.7496574f-7; - 1.7794315f-6 3.8195964f-7; - 5.962632f-8 1.929273f-6; - 2.4432074f-8 2.0219305f-7; - 5.3673745f-7 4.7247065f-7; - 2.3576094f-6 7.4766206f-7; - 3.5011522f-7 1.4861581f-6; - 1.1618213f-6 1.9533408f-7; - 4.614778f-6 4.1072886f-7; - 6.502815f-7 4.0691626f-7; - 5.6749197f-8 5.0866793f-6; - 4.4068376f-8 8.731642f-8; - 1.0844112f-7 1.9776351f-7; - 3.0200038f-8 6.450121f-8; - 8.704141f-7 2.5114787f-6; - 8.797941f-8 7.547485f-9; - 1.6552997f-7 5.5896155f-8; - 3.1622073f-7 1.6056913f-6; - 7.693585f-9 2.021953f-6; - 4.8839985f-7 6.783226f-7; - 5.215422f-7 2.380504f-7; - 1.6065849f-8 1.0821341f-6; - 1.7648129f-6 2.1054043f-7; - 9.131048f-8 2.7812789f-7; - 6.2122064f-7 2.6551525f-6; - 0.00043765508 1.7732987f-7; - 7.081019f-5 9.782313f-6; - 5.933345f-7 1.311903f-5; - 4.304351f-5 3.817954f-7; - 3.4779558f-5 4.9966565f-7; - 9.87594f-6 8.904085f-6; - 3.605005f-7 2.2993759f-6; - 3.771773f-5 7.668917f-7; - 4.9538903f-7 2.104519f-7; - 2.0333518f-8 2.8599166f-7; - 9.425045f-7 5.4813146f-8; - 3.9426893f-9 1.4356274f-6; - 1.3196572f-7 5.276659f-7; - 1.6584235f-7 6.3444793f-7; - 1.8343466f-6 2.8500088f-7; - 2.1991063f-5 1.554987f-6; - 7.1276014f-7 4.4540295f-7; - 4.1344388f-6 4.0607853f-7; - 9.966061f-9 2.5623245f-7; - 3.0985493f-6 4.8840656f-7; - 2.1387075f-8 6.9697755f-8; - 3.6422435f-8 1.0493426f-6; - 5.745379f-7 7.059537f-8; - 5.2699174f-9 5.6219594f-8; - 0.0 0.0; - 0.80551404 0.88535506; - 17.0 3.0; + "od_v4_COCO_dog-cycle-car_nonsquare" => Float32[0.24385607; 0.2188136; 0.5997302; 0.82135123; 0.7500973; 0.00023252431; 0.0007803494; 6.0792013f-7; 3.4914128f-6; 6.412752f-9; 1.8743056f-7; 4.3772996f-8; 7.4116383f-7; 1.2322782f-6; 2.6664713f-8; 8.044653f-6; 2.1694233f-7; 1.8679924f-6; 2.1710104f-5; 0.000103658145; 0.0047235927; 0.7457102; 1.8176132f-5; 0.00053209177; 8.312888f-6; 7.035149f-7; 1.8633238f-5; 4.5999613f-7; 6.300148f-7; 2.9088998f-5; 2.8472394f-7; 2.7194733f-6; 6.0125495f-7; 1.2981325f-6; 9.952989f-6; 1.1205373f-6; 1.323928f-7; 5.919096f-7; 8.532816f-7; 1.08071205f-7; 1.1888196f-6; 5.0686217f-6; 1.9517968f-6; 1.1891767f-6; 9.3203937f-7; 3.6832168f-7; 3.7349594f-7; 2.229868f-7; 1.2003035f-8; 1.6744892f-7; 2.6590511f-8; 3.495714f-6; 1.11521175f-8; 1.4375114f-6; 2.672403f-8; 2.1336512f-6; 1.5169727f-8; 1.2949327f-7; 1.6064594f-5; 1.0558648f-6; 3.75138f-7; 3.0361789f-5; 2.1351434f-6; 1.4093226f-5; 2.7482984f-6; 4.40722f-6; 8.228406f-7; 3.4522238f-8; 1.14341404f-7; 2.2691232f-7; 1.3334162f-7; 3.6704287f-7; 1.3577349f-8; 5.2699335f-7; 7.38859f-7; 2.894171f-7; 4.3963263f-7; 3.789401f-7; 2.4876786f-6; 2.1359523f-7; 6.746143f-8; 2.248321f-8; 1.8635088f-5; 4.953993f-7; 4.323774f-7; 0.0; 0.7457102; 17.0; 1.0;;], + "od_v4_tiny_COCO_dog-cycle-car" => Float32[ + 0.25264323 0.88405305 + 0.33418563 0.06083685 + 0.60824335 1.0000688 + 0.8501828 0.21100688 + 0.8116536 0.98294824 + 1.0290358e-5 6.8941365e-5 + 1.028921e-5 3.1989812e-6 + 3.7023812e-5 0.88535506 + 1.8356855e-6 8.1438935e-5 + 2.3669368e-7 8.762663e-6 + 1.5906524e-7 0.00025538504 + 4.1937323e-8 3.7717284e-6 + 6.1683627e-6 0.13692191 + 8.384943e-6 7.774879e-6 + 1.6155244e-7 1.3341888e-6 + 1.5220728e-7 1.42909485e-5 + 1.0434254e-6 1.973386e-6 + 1.5227679e-6 2.219546e-5 + 0.00031010024 3.6171627e-6 + 1.362012e-5 1.1509221e-8 + 0.003598087 6.387791e-8 + 0.80551404 4.140792e-6 + 1.2294284e-7 1.8664535e-6 + 4.0039163e-6 1.9627709e-7 + 1.7741618e-6 1.3418464e-6 + 2.4750105e-7 6.9187087e-7 + 8.410755e-6 3.628118e-6 + 5.3287954e-7 2.7049066e-7 + 3.3056537e-7 8.175751e-7 + 0.00064245495 5.7913593e-7 + 6.7649135e-6 7.591073e-7 + 7.278294e-5 1.4066734e-6 + 9.2135735e-7 5.7760315e-7 + 0.0009110165 1.0901628e-6 + 1.2088527e-5 1.1644975e-6 + 1.4032729e-8 3.1706963e-7 + 6.131138e-8 2.7496574e-7 + 1.7794315e-6 3.8195964e-7 + 5.962632e-8 1.929273e-6 + 2.4432074e-8 2.0219305e-7 + 5.3673745e-7 4.7247065e-7 + 2.3576094e-6 7.4766206e-7 + 3.5011522e-7 1.4861581e-6 + 1.1618213e-6 1.9533408e-7 + 4.614778e-6 4.1072886e-7 + 6.502815e-7 4.0691626e-7 + 5.6749197e-8 5.0866793e-6 + 4.4068376e-8 8.731642e-8 + 1.0844112e-7 1.9776351e-7 + 3.0200038e-8 6.450121e-8 + 8.704141e-7 2.5114787e-6 + 8.797941e-8 7.547485e-9 + 1.6552997e-7 5.5896155e-8 + 3.1622073e-7 1.6056913e-6 + 7.693585e-9 2.021953e-6 + 4.8839985e-7 6.783226e-7 + 5.215422e-7 2.380504e-7 + 1.6065849e-8 1.0821341e-6 + 1.7648129e-6 2.1054043e-7 + 9.131048e-8 2.7812789e-7 + 6.2122064e-7 2.6551525e-6 + 0.00043765508 1.7732987e-7 + 7.081019e-5 9.782313e-6 + 5.933345e-7 1.311903e-5 + 4.304351e-5 3.817954e-7 + 3.4779558e-5 4.9966565e-7 + 9.87594e-6 8.904085e-6 + 3.605005e-7 2.2993759e-6 + 3.771773e-5 7.668917e-7 + 4.9538903e-7 2.104519e-7 + 2.0333518e-8 2.8599166e-7 + 9.425045e-7 5.4813146e-8 + 3.9426893e-9 1.4356274e-6 + 1.3196572e-7 5.276659e-7 + 1.6584235e-7 6.3444793e-7 + 1.8343466e-6 2.8500088e-7 + 2.1991063e-5 1.554987e-6 + 7.1276014e-7 4.4540295e-7 + 4.1344388e-6 4.0607853e-7 + 9.966061e-9 2.5623245e-7 + 3.0985493e-6 4.8840656e-7 + 2.1387075e-8 6.9697755e-8 + 3.6422435e-8 1.0493426e-6 + 5.745379e-7 7.059537e-8 + 5.2699174e-9 5.6219594e-8 + 0.0 0.0 + 0.80551404 0.88535506 + 17.0 3.0 1.0 1.0 ], - "od_v4_tiny_COCO_dog-cycle-car_nonsquare" => [ - 0.2539299; 0.19908333; 0.61074865; 0.7779485; 0.885034; 8.018394f-6; 0.00012812295; 0.0008370466; 6.5234335f-6; 2.3423543f-6; 1.5642835f-6; 1.8780679f-7; 6.12936f-5; 0.0002469004; 5.508887f-7; 2.6336352f-7; 6.303524f-6; 3.7316563f-6; 0.00019138154; 9.534192f-6; 0.004068276; 0.87269115; 9.03528f-6; 6.5147356f-6; 1.0823026f-5; 3.5724503f-7; 1.3758284f-5; 1.967107f-6; 7.409454f-7; 0.0023256599; 2.7227557f-5; 0.0003673656; 2.642379f-6; 0.0047195167; 1.5202914f-5; 2.7782043f-8; 7.660662f-8; 3.525224f-6; 2.9226783f-7; 4.660468f-8; 6.115468f-7; 3.920959f-6; 2.237545f-6; 6.584367f-6; 8.322705f-6; 3.3556702f-7; 6.3410596f-8; 4.8931266f-8; 5.374632f-7; 3.6472862f-8; 3.4451386f-6; 2.4351243f-7; 6.808317f-7; 1.2721725f-6; 1.9497437f-8; 1.2986773f-7; 6.6764795f-7; 1.1044616f-7; 3.8099515f-6; 4.5612828f-7; 6.6059437f-7; 0.00053603936; 0.00013236573; 1.6922503f-6; 0.00016953715; 5.751094f-5; 2.705995f-5; 3.4457137f-6; 3.0019759f-5; 2.7318265f-6; 9.110978f-8; 3.637514f-6; 6.505787f-8; 1.549414f-6; 9.1509355f-7; 1.04915325f-5; 5.2181367f-5; 2.1398773f-6; 4.3863997f-6; 4.939826f-8; 5.304777f-7; 6.0424576f-8; 2.1450337f-7; 1.2874827f-6; 6.3505095f-9; 0.0; 0.87269115; 17.0; 1.0;; - ], - "od_v7_COCO_dog-cycle-car" => [ - 0.23815063 0.24645361 0.8860177; - 0.16355091 0.33622342 0.057299823; - 0.9973053 0.588377 0.9996812; - 0.7102828 0.9375947 0.21770617; - 0.98193926 0.97203165 0.7320531; - 1.7976697f-6 6.2492504f-6 8.6512614f-7; - 0.98095894 4.403119f-5 1.0029323f-6; - 4.372776f-6 6.5727797f-9 0.72744566; - 4.2918127f-5 2.5776708f-7 3.6603058f-6; - 7.9137203f-7 1.4385056f-7 8.729315f-7; - 7.1363024f-6 6.1945498f-9 0.00027033224; - 3.2591308f-6 2.4984898f-8 8.119923f-8; - 2.2369423f-7 1.4840158f-7 0.0058117644; - 3.218778f-6 7.267238f-8 2.5482393f-7; - 9.54758f-7 2.178337f-7 2.77832f-6; - 8.421701f-5 0.00012422209 6.4283563f-7; - 1.0419087f-6 2.5902089f-6 3.3597905f-6; - 4.779215f-6 2.4839035f-6 3.3635927f-6; - 1.1762072f-5 2.8768275f-6 4.2990192f-7; - 5.963289f-7 1.7837545f-5 8.122549f-10; - 1.1004106f-5 8.4084f-5 1.4938864f-8; - 0.00907491 0.97152025 1.7191042f-9; - 1.1587575f-5 1.9319223f-5 2.9292593f-7; - 3.5602403f-5 4.6567242f-5 2.1636317f-6; - 8.579619f-7 1.214295f-5 3.5215535f-7; - 4.147618f-6 7.948674f-6 7.0206543f-9; - 2.0516417f-7 1.6422526f-5 4.144618f-8; - 2.7922939f-5 5.1868083f-6 1.7928953f-6; - 4.2349654f-8 6.4661626f-6 3.1637386f-9; - 1.2926707f-5 9.621897f-6 4.306613f-9; - 1.7509233f-6 1.1196531f-8 4.4370537f-7; - 3.6393672f-6 4.848704f-7 1.427095f-6; - 1.9388567f-6 5.3300873f-6 4.995329f-9; - 4.301117f-6 1.364441f-6 5.0403514f-7; - 1.0401697f-5 1.5473493f-5 1.2681767f-7; - 1.2120468f-5 6.8532f-7 3.1809728f-7; - 7.452113f-7 1.2121109f-6 6.264496f-7; - 5.664459f-7 9.808097f-7 3.0339559f-6; - 2.4178958f-5 2.5849142f-5 9.584112f-7; - 2.8547252f-6 1.620321f-6 3.0887907f-7; - 6.0593443f-6 6.104965f-6 2.3764052f-7; - 2.408509f-5 2.9606865f-5 7.545183f-7; - 1.104113f-5 3.2925088f-6 3.0091152f-7; - 2.5208954f-5 1.6969775f-6 9.737609f-8; - 3.970217f-6 3.4524974f-6 3.179383f-8; - 6.9098332f-6 2.0231514f-6 1.5536154f-8; - 1.2588231f-6 4.0681858f-8 1.6999917f-7; - 4.17788f-6 3.051015f-6 1.3918115f-6; - 8.252845f-8 5.1825083f-8 7.138027f-10; - 9.82085f-7 2.008168f-7 9.101209f-7; - 4.813209f-7 4.937284f-9 2.5846333f-7; - 1.6222037f-5 1.4827979f-6 8.351329f-10; - 1.9211765f-7 1.8995638f-6 3.997992f-7; - 1.261036f-9 1.4610689f-5 6.4059136f-9; - 2.8033144f-6 3.0735384f-7 3.856958f-7; - 1.2583346f-6 5.060041f-6 4.346336f-7; - 9.867529f-7 1.4447544f-6 8.687343f-9; - 2.3000123f-6 8.957199f-6 2.1583976f-7; - 3.9436498f-5 1.8005288f-5 2.3852666f-8; - 5.201025f-7 1.1350874f-6 7.157745f-7; - 2.6482135f-7 4.4439134f-6 5.3565903f-9; - 3.1865343f-6 9.084356f-7 8.666778f-7; - 1.2553061f-8 2.5589114f-7 4.366165f-8; - 1.2007594f-5 1.2240149f-5 1.4250107f-7; - 3.6738054f-8 2.149799f-7 4.5886452f-8; - 7.414613f-8 1.46066785f-8 5.640038f-9; - 8.885151f-9 7.8896613f-7 6.047938f-8; - 1.7629132f-8 3.8172687f-9 2.9288822f-7; - 6.440085f-8 6.7793935f-6 4.235829f-8; - 3.5249744f-7 2.948251f-7 5.2710215f-7; - 8.4702793f-7 1.5126291f-6 1.5741315f-7; - 6.6191537f-6 7.362936f-8 2.2796047f-7; - 6.308443f-8 5.0100656f-8 3.1613047f-6; - 1.6461069f-6 4.0537174f-7 2.1894073f-6; - 2.4674985f-6 2.5355038f-7 5.1386166f-7; - 6.55973f-7 2.3428618f-6 2.7922122f-6; - 1.0076072f-6 1.8410525f-7 6.4598584f-7; - 2.161139f-7 8.09069f-7 2.127518f-7; - 3.8941923f-7 2.5079032f-7 1.0986423f-7; - 1.127125f-5 4.2768858f-7 2.595848f-8; - 3.30429f-7 1.6697399f-7 3.602047f-7; - 1.1248517f-5 2.8598404f-6 3.9626222f-8; - 7.038784f-7 4.2287716f-5 4.903233f-10; - 2.2290447f-5 1.5659867f-6 1.3394194f-7; - 1.115863f-6 3.0305216f-6 5.691326f-7; - 0.0 0.0 0.0; - 0.98095894 0.97152025 0.72744566; - 2.0 17.0 3.0; + "od_v4_tiny_COCO_dog-cycle-car_nonsquare" => Float32[0.2539299; 0.19908333; 0.61074865; 0.7779485; 0.885034; 8.018394f-6; 0.00012812295; 0.0008370466; 6.5234335f-6; 2.3423543f-6; 1.5642835f-6; 1.8780679f-7; 6.12936f-5; 0.0002469004; 5.508887f-7; 2.6336352f-7; 6.303524f-6; 3.7316563f-6; 0.00019138154; 9.534192f-6; 0.004068276; 0.87269115; 9.03528f-6; 6.5147356f-6; 1.0823026f-5; 3.5724503f-7; 1.3758284f-5; 1.967107f-6; 7.409454f-7; 0.0023256599; 2.7227557f-5; 0.0003673656; 2.642379f-6; 0.0047195167; 1.5202914f-5; 2.7782043f-8; 7.660662f-8; 3.525224f-6; 2.9226783f-7; 4.660468f-8; 6.115468f-7; 3.920959f-6; 2.237545f-6; 6.584367f-6; 8.322705f-6; 3.3556702f-7; 6.3410596f-8; 4.8931266f-8; 5.374632f-7; 3.6472862f-8; 3.4451386f-6; 2.4351243f-7; 6.808317f-7; 1.2721725f-6; 1.9497437f-8; 1.2986773f-7; 6.6764795f-7; 1.1044616f-7; 3.8099515f-6; 4.5612828f-7; 6.6059437f-7; 0.00053603936; 0.00013236573; 1.6922503f-6; 0.00016953715; 5.751094f-5; 2.705995f-5; 3.4457137f-6; 3.0019759f-5; 2.7318265f-6; 9.110978f-8; 3.637514f-6; 6.505787f-8; 1.549414f-6; 9.1509355f-7; 1.04915325f-5; 5.2181367f-5; 2.1398773f-6; 4.3863997f-6; 4.939826f-8; 5.304777f-7; 6.0424576f-8; 2.1450337f-7; 1.2874827f-6; 6.3505095f-9; 0.0; 0.87269115; 17.0; 1.0;;], + "od_v7_COCO_dog-cycle-car" => Float32[ + 0.23815063 0.24645361 0.8860177 + 0.16355091 0.33622342 0.057299823 + 0.9973053 0.588377 0.9996812 + 0.7102828 0.9375947 0.21770617 + 0.98193926 0.97203165 0.7320531 + 1.7976697e-6 6.2492504e-6 8.6512614e-7 + 0.98095894 4.403119e-5 1.0029323e-6 + 4.372776e-6 6.5727797e-9 0.72744566 + 4.2918127e-5 2.5776708e-7 3.6603058e-6 + 7.9137203e-7 1.4385056e-7 8.729315e-7 + 7.1363024e-6 6.1945498e-9 0.00027033224 + 3.2591308e-6 2.4984898e-8 8.119923e-8 + 2.2369423e-7 1.4840158e-7 0.0058117644 + 3.218778e-6 7.267238e-8 2.5482393e-7 + 9.54758e-7 2.178337e-7 2.77832e-6 + 8.421701e-5 0.00012422209 6.4283563e-7 + 1.0419087e-6 2.5902089e-6 3.3597905e-6 + 4.779215e-6 2.4839035e-6 3.3635927e-6 + 1.1762072e-5 2.8768275e-6 4.2990192e-7 + 5.963289e-7 1.7837545e-5 8.122549e-10 + 1.1004106e-5 8.4084e-5 1.4938864e-8 + 0.00907491 0.97152025 1.7191042e-9 + 1.1587575e-5 1.9319223e-5 2.9292593e-7 + 3.5602403e-5 4.6567242e-5 2.1636317e-6 + 8.579619e-7 1.214295e-5 3.5215535e-7 + 4.147618e-6 7.948674e-6 7.0206543e-9 + 2.0516417e-7 1.6422526e-5 4.144618e-8 + 2.7922939e-5 5.1868083e-6 1.7928953e-6 + 4.2349654e-8 6.4661626e-6 3.1637386e-9 + 1.2926707e-5 9.621897e-6 4.306613e-9 + 1.7509233e-6 1.1196531e-8 4.4370537e-7 + 3.6393672e-6 4.848704e-7 1.427095e-6 + 1.9388567e-6 5.3300873e-6 4.995329e-9 + 4.301117e-6 1.364441e-6 5.0403514e-7 + 1.0401697e-5 1.5473493e-5 1.2681767e-7 + 1.2120468e-5 6.8532e-7 3.1809728e-7 + 7.452113e-7 1.2121109e-6 6.264496e-7 + 5.664459e-7 9.808097e-7 3.0339559e-6 + 2.4178958e-5 2.5849142e-5 9.584112e-7 + 2.8547252e-6 1.620321e-6 3.0887907e-7 + 6.0593443e-6 6.104965e-6 2.3764052e-7 + 2.408509e-5 2.9606865e-5 7.545183e-7 + 1.104113e-5 3.2925088e-6 3.0091152e-7 + 2.5208954e-5 1.6969775e-6 9.737609e-8 + 3.970217e-6 3.4524974e-6 3.179383e-8 + 6.9098332e-6 2.0231514e-6 1.5536154e-8 + 1.2588231e-6 4.0681858e-8 1.6999917e-7 + 4.17788e-6 3.051015e-6 1.3918115e-6 + 8.252845e-8 5.1825083e-8 7.138027e-10 + 9.82085e-7 2.008168e-7 9.101209e-7 + 4.813209e-7 4.937284e-9 2.5846333e-7 + 1.6222037e-5 1.4827979e-6 8.351329e-10 + 1.9211765e-7 1.8995638e-6 3.997992e-7 + 1.261036e-9 1.4610689e-5 6.4059136e-9 + 2.8033144e-6 3.0735384e-7 3.856958e-7 + 1.2583346e-6 5.060041e-6 4.346336e-7 + 9.867529e-7 1.4447544e-6 8.687343e-9 + 2.3000123e-6 8.957199e-6 2.1583976e-7 + 3.9436498e-5 1.8005288e-5 2.3852666e-8 + 5.201025e-7 1.1350874e-6 7.157745e-7 + 2.6482135e-7 4.4439134e-6 5.3565903e-9 + 3.1865343e-6 9.084356e-7 8.666778e-7 + 1.2553061e-8 2.5589114e-7 4.366165e-8 + 1.2007594e-5 1.2240149e-5 1.4250107e-7 + 3.6738054e-8 2.149799e-7 4.5886452e-8 + 7.414613e-8 1.46066785e-8 5.640038e-9 + 8.885151e-9 7.8896613e-7 6.047938e-8 + 1.7629132e-8 3.8172687e-9 2.9288822e-7 + 6.440085e-8 6.7793935e-6 4.235829e-8 + 3.5249744e-7 2.948251e-7 5.2710215e-7 + 8.4702793e-7 1.5126291e-6 1.5741315e-7 + 6.6191537e-6 7.362936e-8 2.2796047e-7 + 6.308443e-8 5.0100656e-8 3.1613047e-6 + 1.6461069e-6 4.0537174e-7 2.1894073e-6 + 2.4674985e-6 2.5355038e-7 5.1386166e-7 + 6.55973e-7 2.3428618e-6 2.7922122e-6 + 1.0076072e-6 1.8410525e-7 6.4598584e-7 + 2.161139e-7 8.09069e-7 2.127518e-7 + 3.8941923e-7 2.5079032e-7 1.0986423e-7 + 1.127125e-5 4.2768858e-7 2.595848e-8 + 3.30429e-7 1.6697399e-7 3.602047e-7 + 1.1248517e-5 2.8598404e-6 3.9626222e-8 + 7.038784e-7 4.2287716e-5 4.903233e-10 + 2.2290447e-5 1.5659867e-6 1.3394194e-7 + 1.115863e-6 3.0305216e-6 5.691326e-7 + 0.0 0.0 0.0 + 0.98095894 0.97152025 0.72744566 + 2.0 17.0 3.0 1.0 1.0 1.0 ], - "od_v7_COCO_dog-cycle-car_nonsquare" => [ - 0.23688027 0.24578273; - 0.16262105 0.21957612; - 0.99719954 0.5866954; - 0.59032714 0.8174101; - 0.975068 0.9692562; - 1.184655f-6 9.994274f-6; - 0.97430605 1.3336885f-5; - 3.3300023f-6 1.2308988f-9; - 2.0224497f-5 1.344517f-7; - 3.824636f-7 5.2542095f-8; - 1.6512942f-5 2.1155229f-9; - 4.381695f-6 1.4103183f-8; - 9.394181f-8 4.1502425f-8; - 2.0519294f-6 7.3913125f-9; - 6.3093853f-7 1.8169033f-7; - 3.6916106f-5 5.3863365f-5; - 8.170963f-7 6.3364195f-7; - 4.230873f-6 1.0640626f-6; - 3.5408018f-6 3.927086f-7; - 1.6933086f-7 5.160184f-6; - 2.355327f-6 6.455358f-5; - 0.0037281094 0.9687549; - 2.663422f-6 5.2636256f-6; - 7.4775767f-6 3.5310884f-5; - 1.1007835f-7 3.18651f-6; - 1.0181171f-6 2.4107114f-6; - 5.984942f-8 4.8451975f-6; - 1.2937715f-5 2.6636415f-6; - 1.5681627f-8 2.44238f-6; - 7.482063f-6 4.471455f-6; - 1.1072179f-6 1.8545914f-9; - 1.7524779f-6 1.0813944f-6; - 1.4294272f-6 2.4388048f-6; - 1.9486658f-6 1.3560023f-6; - 8.539374f-6 9.001304f-6; - 7.986357f-6 3.465628f-7; - 4.6180662f-7 6.69567f-7; - 2.763405f-7 6.8244947f-7; - 7.712415f-6 2.8762082f-5; - 2.2013126f-6 8.0002417f-7; - 2.7930155f-6 1.3775925f-6; - 1.711175f-5 1.2333333f-5; - 5.4268044f-6 7.120057f-7; - 2.020603f-5 8.0180473f-7; - 1.725241f-6 4.178968f-6; - 2.9914193f-6 1.10822f-6; - 4.3899166f-7 4.8788237f-8; - 3.3638169f-6 1.3639395f-6; - 4.2970832f-8 2.9306003f-8; - 4.671291f-7 3.3810397f-7; - 2.8756222f-7 2.1598827f-9; - 5.331467f-6 7.0894725f-7; - 5.122982f-8 8.970353f-7; - 3.864593f-10 4.070058f-6; - 9.603966f-7 8.690696f-8; - 7.0223615f-7 2.0698567f-6; - 5.5102964f-7 5.9723834f-7; - 7.936989f-7 5.063694f-6; - 3.123006f-5 2.3562222f-6; - 1.9848675f-7 2.5853538f-7; - 9.306757f-8 1.4023057f-6; - 1.0383552f-6 2.5612252f-7; - 3.0626088f-9 8.156728f-8; - 7.091949f-6 1.0260906f-5; - 8.727424f-9 6.486597f-8; - 4.3564604f-8 5.07503f-9; - 2.609664f-9 7.754648f-7; - 1.470107f-8 1.0804373f-9; - 3.5059063f-8 2.5944753f-6; - 2.0716814f-7 1.5563327f-7; - 3.838229f-7 1.6772532f-6; - 8.156206f-6 2.2776225f-8; - 3.978885f-8 1.7183316f-8; - 2.0486934f-6 2.8718358f-7; - 4.2104375f-6 7.814725f-8; - 5.88018f-7 5.567012f-7; - 6.102524f-7 5.818898f-8; - 1.2426872f-7 2.0988692f-7; - 3.96326f-7 1.18481f-7; - 8.999482f-6 1.2901135f-7; - 1.2870642f-7 7.9409844f-8; - 7.386114f-6 1.405275f-6; - 1.7699445f-7 2.7791904f-5; - 1.985172f-5 3.3442266f-6; - 5.905588f-7 2.522737f-6; - 0.0 0.0; - 0.97430605 0.9687549; - 2.0 17.0; + "od_v7_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.23688027 0.24578273 + 0.16262105 0.21957612 + 0.99719954 0.5866954 + 0.59032714 0.8174101 + 0.975068 0.9692562 + 1.184655e-6 9.994274e-6 + 0.97430605 1.3336885e-5 + 3.3300023e-6 1.2308988e-9 + 2.0224497e-5 1.344517e-7 + 3.824636e-7 5.2542095e-8 + 1.6512942e-5 2.1155229e-9 + 4.381695e-6 1.4103183e-8 + 9.394181e-8 4.1502425e-8 + 2.0519294e-6 7.3913125e-9 + 6.3093853e-7 1.8169033e-7 + 3.6916106e-5 5.3863365e-5 + 8.170963e-7 6.3364195e-7 + 4.230873e-6 1.0640626e-6 + 3.5408018e-6 3.927086e-7 + 1.6933086e-7 5.160184e-6 + 2.355327e-6 6.455358e-5 + 0.0037281094 0.9687549 + 2.663422e-6 5.2636256e-6 + 7.4775767e-6 3.5310884e-5 + 1.1007835e-7 3.18651e-6 + 1.0181171e-6 2.4107114e-6 + 5.984942e-8 4.8451975e-6 + 1.2937715e-5 2.6636415e-6 + 1.5681627e-8 2.44238e-6 + 7.482063e-6 4.471455e-6 + 1.1072179e-6 1.8545914e-9 + 1.7524779e-6 1.0813944e-6 + 1.4294272e-6 2.4388048e-6 + 1.9486658e-6 1.3560023e-6 + 8.539374e-6 9.001304e-6 + 7.986357e-6 3.465628e-7 + 4.6180662e-7 6.69567e-7 + 2.763405e-7 6.8244947e-7 + 7.712415e-6 2.8762082e-5 + 2.2013126e-6 8.0002417e-7 + 2.7930155e-6 1.3775925e-6 + 1.711175e-5 1.2333333e-5 + 5.4268044e-6 7.120057e-7 + 2.020603e-5 8.0180473e-7 + 1.725241e-6 4.178968e-6 + 2.9914193e-6 1.10822e-6 + 4.3899166e-7 4.8788237e-8 + 3.3638169e-6 1.3639395e-6 + 4.2970832e-8 2.9306003e-8 + 4.671291e-7 3.3810397e-7 + 2.8756222e-7 2.1598827e-9 + 5.331467e-6 7.0894725e-7 + 5.122982e-8 8.970353e-7 + 3.864593e-10 4.070058e-6 + 9.603966e-7 8.690696e-8 + 7.0223615e-7 2.0698567e-6 + 5.5102964e-7 5.9723834e-7 + 7.936989e-7 5.063694e-6 + 3.123006e-5 2.3562222e-6 + 1.9848675e-7 2.5853538e-7 + 9.306757e-8 1.4023057e-6 + 1.0383552e-6 2.5612252e-7 + 3.0626088e-9 8.156728e-8 + 7.091949e-6 1.0260906e-5 + 8.727424e-9 6.486597e-8 + 4.3564604e-8 5.07503e-9 + 2.609664e-9 7.754648e-7 + 1.470107e-8 1.0804373e-9 + 3.5059063e-8 2.5944753e-6 + 2.0716814e-7 1.5563327e-7 + 3.838229e-7 1.6772532e-6 + 8.156206e-6 2.2776225e-8 + 3.978885e-8 1.7183316e-8 + 2.0486934e-6 2.8718358e-7 + 4.2104375e-6 7.814725e-8 + 5.88018e-7 5.567012e-7 + 6.102524e-7 5.818898e-8 + 1.2426872e-7 2.0988692e-7 + 3.96326e-7 1.18481e-7 + 8.999482e-6 1.2901135e-7 + 1.2870642e-7 7.9409844e-8 + 7.386114e-6 1.405275e-6 + 1.7699445e-7 2.7791904e-5 + 1.985172e-5 3.3442266e-6 + 5.905588e-7 2.522737e-6 + 0.0 0.0 + 0.97430605 0.9687549 + 2.0 17.0 1.0 1.0 ], - "od_v7_tiny_COCO_dog-cycle-car" => [ - 0.23326883 0.24692076 0.87750936; - 0.16659045 0.32880506 0.05724503; - 0.99945176 0.58260775 0.99936235; - 0.6959942 0.9460746 0.21617691; - 0.8789436 0.8887681 0.8588778; - 1.4946814f-6 3.0589767f-5 1.834683f-6; - 0.87812895 0.00021136845 1.2171082f-6; - 1.9512123f-5 9.067649f-7 0.8488883; - 3.8671315f-5 1.7459831f-7 7.956116f-6; - 4.41067f-9 1.143911f-9 7.992269f-6; - 0.0010054375 1.31437f-7 0.0001130747; - 6.3309766f-5 7.259642f-9 8.911626f-7; - 1.6869703f-6 1.4512985f-7 0.008529328; - 4.3029486f-5 1.679339f-7 7.596815f-6; - 7.0778833f-7 5.173757f-7 2.5333608f-7; - 4.3235065f-7 2.001139f-6 4.7207916f-8; - 7.8412444f-7 2.4082206f-6 7.3800784f-8; - 5.1162743f-7 4.40597f-7 3.9542606f-6; - 0.00054870785 5.3375675f-7 2.497095f-6; - 7.7079534f-8 1.855705f-5 8.166525f-8; - 7.550775f-7 0.041727617 2.08718f-9; - 3.398203f-6 0.8709969 3.1543732f-7; - 6.2263643f-7 4.515328f-5 7.588888f-8; - 2.645802f-7 1.771373f-5 3.2631588f-6; - 1.3919276f-8 1.2036842f-6 4.4282606f-7; - 8.363614f-9 1.1729305f-6 1.1018458f-6; - 8.27397f-8 2.9632762f-5 5.4121915f-7; - 3.54654f-7 7.9775697f-7 2.8401496f-8; - 3.1973315f-8 4.398656f-6 1.4916415f-11; - 7.4736563f-6 7.469886f-5 5.222629f-7; - 2.6754933f-6 2.464211f-7 1.0733061f-6; - 5.5734668f-6 1.7133018f-6 1.4398946f-8; - 4.6862877f-7 3.9189614f-5 3.6832745f-11; - 3.3863536f-5 3.9602405f-6 2.8633417f-6; - 1.0283353f-5 2.1736587f-5 6.042754f-8; - 2.6647625f-5 1.20509f-6 7.7492555f-9; - 3.291347f-7 3.056189f-6 1.887237f-8; - 4.1582166f-7 3.5336543f-6 8.7523455f-7; - 9.040487f-6 6.424001f-7 7.170283f-8; - 1.3247427f-6 4.686856f-6 8.997547f-8; - 5.2317614f-7 1.1577043f-6 7.108299f-8; - 3.3986116f-6 6.9690263f-6 2.986443f-8; - 2.144241f-6 1.2360302f-6 2.8981287f-8; - 0.0002075382 1.535913f-5 1.7732877f-8; - 7.7381634f-7 9.532781f-6 9.266443f-8; - 3.7700747f-6 6.704066f-7 3.908403f-10; - 6.8423233f-6 4.0493132f-6 2.066297f-7; - 4.7407954f-7 8.9629566f-7 2.7909746f-9; - 7.962025f-8 1.6502379f-7 8.191646f-11; - 9.984178f-8 2.0143216f-6 5.091273f-9; - 1.5706698f-6 1.4198218f-8 9.779417f-9; - 6.5825253f-7 1.1745065f-5 2.2346944f-9; - 3.5702914f-7 1.6370221f-6 2.2831831f-7; - 2.1695032f-10 2.6746716f-7 4.532625f-10; - 1.4984087f-5 2.6627657f-7 2.5149907f-8; - 4.3495003f-9 2.4597404f-7 5.5179514f-9; - 2.161116f-6 5.573089f-7 8.3356565f-11; - 1.3801825f-7 2.595205f-6 7.1060313f-9; - 2.568445f-7 8.402493f-6 1.7616218f-10; - 3.3779827f-8 2.1384953f-6 5.7082093f-7; - 1.2026548f-8 7.1080837f-7 4.336305f-9; - 0.0003338309 2.3541075f-5 6.7529714f-7; - 7.784574f-8 7.6792176f-8 1.5792538f-6; - 1.2113688f-5 2.8917295f-6 2.3871942f-7; - 6.713532f-7 1.5617853f-7 5.3035586f-7; - 1.4619914f-6 4.757903f-9 7.313877f-9; - 8.253083f-9 1.5271523f-7 7.435833f-8; - 4.952903f-7 1.0746751f-8 3.8027174f-6; - 1.983528f-7 4.5224976f-8 1.7956559f-8; - 4.0026987f-7 3.903486f-6 1.3213838f-6; - 9.143754f-8 3.67565f-7 5.8607153f-8; - 1.4071102f-6 3.808819f-8 5.448604f-8; - 1.2951821f-7 2.0997302f-7 3.789712f-7; - 5.702675f-7 2.8672106f-8 7.4244366f-7; - 1.2549508f-5 8.226409f-9 5.57541f-8; - 1.1530242f-6 2.0981176f-6 4.2658638f-7; - 4.6528706f-7 2.2161903f-7 2.3315702f-6; - 2.725468f-7 3.139983f-7 1.26820625f-8; - 1.4044959f-7 8.005283f-8 8.9782235f-9; - 2.5871472f-5 6.026331f-7 1.5651376f-7; - 4.837131f-7 6.289965f-7 1.951331f-8; - 1.1314056f-6 1.7354479f-6 6.814624f-11; - 1.9973461f-8 2.513335f-5 1.2030649f-9; - 4.4508984f-6 9.740525f-6 5.5189325f-8; - 2.8936176f-6 2.1682385f-5 6.664649f-8; - 0.0 0.0 0.0; - 0.87812895 0.8709969 0.8488883; - 2.0 17.0 3.0; + "od_v7_tiny_COCO_dog-cycle-car" => Float32[ + 0.23326883 0.24692076 0.87750936 + 0.16659045 0.32880506 0.05724503 + 0.99945176 0.58260775 0.99936235 + 0.6959942 0.9460746 0.21617691 + 0.8789436 0.8887681 0.8588778 + 1.4946814e-6 3.0589767e-5 1.834683e-6 + 0.87812895 0.00021136845 1.2171082e-6 + 1.9512123e-5 9.067649e-7 0.8488883 + 3.8671315e-5 1.7459831e-7 7.956116e-6 + 4.41067e-9 1.143911e-9 7.992269e-6 + 0.0010054375 1.31437e-7 0.0001130747 + 6.3309766e-5 7.259642e-9 8.911626e-7 + 1.6869703e-6 1.4512985e-7 0.008529328 + 4.3029486e-5 1.679339e-7 7.596815e-6 + 7.0778833e-7 5.173757e-7 2.5333608e-7 + 4.3235065e-7 2.001139e-6 4.7207916e-8 + 7.8412444e-7 2.4082206e-6 7.3800784e-8 + 5.1162743e-7 4.40597e-7 3.9542606e-6 + 0.00054870785 5.3375675e-7 2.497095e-6 + 7.7079534e-8 1.855705e-5 8.166525e-8 + 7.550775e-7 0.041727617 2.08718e-9 + 3.398203e-6 0.8709969 3.1543732e-7 + 6.2263643e-7 4.515328e-5 7.588888e-8 + 2.645802e-7 1.771373e-5 3.2631588e-6 + 1.3919276e-8 1.2036842e-6 4.4282606e-7 + 8.363614e-9 1.1729305e-6 1.1018458e-6 + 8.27397e-8 2.9632762e-5 5.4121915e-7 + 3.54654e-7 7.9775697e-7 2.8401496e-8 + 3.1973315e-8 4.398656e-6 1.4916415e-11 + 7.4736563e-6 7.469886e-5 5.222629e-7 + 2.6754933e-6 2.464211e-7 1.0733061e-6 + 5.5734668e-6 1.7133018e-6 1.4398946e-8 + 4.6862877e-7 3.9189614e-5 3.6832745e-11 + 3.3863536e-5 3.9602405e-6 2.8633417e-6 + 1.0283353e-5 2.1736587e-5 6.042754e-8 + 2.6647625e-5 1.20509e-6 7.7492555e-9 + 3.291347e-7 3.056189e-6 1.887237e-8 + 4.1582166e-7 3.5336543e-6 8.7523455e-7 + 9.040487e-6 6.424001e-7 7.170283e-8 + 1.3247427e-6 4.686856e-6 8.997547e-8 + 5.2317614e-7 1.1577043e-6 7.108299e-8 + 3.3986116e-6 6.9690263e-6 2.986443e-8 + 2.144241e-6 1.2360302e-6 2.8981287e-8 + 0.0002075382 1.535913e-5 1.7732877e-8 + 7.7381634e-7 9.532781e-6 9.266443e-8 + 3.7700747e-6 6.704066e-7 3.908403e-10 + 6.8423233e-6 4.0493132e-6 2.066297e-7 + 4.7407954e-7 8.9629566e-7 2.7909746e-9 + 7.962025e-8 1.6502379e-7 8.191646e-11 + 9.984178e-8 2.0143216e-6 5.091273e-9 + 1.5706698e-6 1.4198218e-8 9.779417e-9 + 6.5825253e-7 1.1745065e-5 2.2346944e-9 + 3.5702914e-7 1.6370221e-6 2.2831831e-7 + 2.1695032e-10 2.6746716e-7 4.532625e-10 + 1.4984087e-5 2.6627657e-7 2.5149907e-8 + 4.3495003e-9 2.4597404e-7 5.5179514e-9 + 2.161116e-6 5.573089e-7 8.3356565e-11 + 1.3801825e-7 2.595205e-6 7.1060313e-9 + 2.568445e-7 8.402493e-6 1.7616218e-10 + 3.3779827e-8 2.1384953e-6 5.7082093e-7 + 1.2026548e-8 7.1080837e-7 4.336305e-9 + 0.0003338309 2.3541075e-5 6.7529714e-7 + 7.784574e-8 7.6792176e-8 1.5792538e-6 + 1.2113688e-5 2.8917295e-6 2.3871942e-7 + 6.713532e-7 1.5617853e-7 5.3035586e-7 + 1.4619914e-6 4.757903e-9 7.313877e-9 + 8.253083e-9 1.5271523e-7 7.435833e-8 + 4.952903e-7 1.0746751e-8 3.8027174e-6 + 1.983528e-7 4.5224976e-8 1.7956559e-8 + 4.0026987e-7 3.903486e-6 1.3213838e-6 + 9.143754e-8 3.67565e-7 5.8607153e-8 + 1.4071102e-6 3.808819e-8 5.448604e-8 + 1.2951821e-7 2.0997302e-7 3.789712e-7 + 5.702675e-7 2.8672106e-8 7.4244366e-7 + 1.2549508e-5 8.226409e-9 5.57541e-8 + 1.1530242e-6 2.0981176e-6 4.2658638e-7 + 4.6528706e-7 2.2161903e-7 2.3315702e-6 + 2.725468e-7 3.139983e-7 1.26820625e-8 + 1.4044959e-7 8.005283e-8 8.9782235e-9 + 2.5871472e-5 6.026331e-7 1.5651376e-7 + 4.837131e-7 6.289965e-7 1.951331e-8 + 1.1314056e-6 1.7354479e-6 6.814624e-11 + 1.9973461e-8 2.513335e-5 1.2030649e-9 + 4.4508984e-6 9.740525e-6 5.5189325e-8 + 2.8936176e-6 2.1682385e-5 6.664649e-8 + 0.0 0.0 0.0 + 0.87812895 0.8709969 0.8488883 + 2.0 17.0 3.0 1.0 1.0 1.0 ], - "od_v7_tiny_COCO_dog-cycle-car_nonsquare" => [ - 0.21098387 0.24594076; - 0.16210589 0.21256444; - 0.99821246 0.5849901; - 0.5709939 0.8199513; - 0.7701267 0.90673345; - 1.4335743f-6 9.9756144f-5; - 0.7663358 0.00018426332; - 0.00015750644 7.464547f-6; - 9.5975454f-5 5.6190623f-7; - 4.1853347f-8 1.3555579f-9; - 0.005396336 1.4579688f-6; - 3.2416472f-5 1.6742394f-8; - 0.00021949186 1.0166412f-6; - 0.00027060503 2.2101574f-6; - 1.3700973f-8 1.9244644f-6; - 1.1206721f-7 1.6771866f-5; - 2.4542217f-7 8.816636f-6; - 4.3287798f-7 1.0163242f-6; - 0.00087150576 3.1314473f-6; - 1.0165805f-8 8.010309f-6; - 5.053699f-8 0.002072461; - 4.7598914f-7 0.90416116; - 3.7436752f-8 7.225644f-5; - 2.389189f-8 1.1487388f-5; - 1.0353663f-8 2.1804788f-6; - 1.1910078f-9 1.2675188f-6; - 6.4750547f-9 3.2380576f-5; - 3.709798f-8 1.1901969f-6; - 3.7169852f-9 1.1985976f-5; - 1.4390395f-5 0.00019688539; - 1.0319029f-6 2.9800987f-7; - 5.2115456f-6 1.6730553f-6; - 3.2725616f-8 6.5141954f-5; - 8.931652f-5 8.992765f-6; - 3.193306f-6 4.6158344f-5; - 1.0537543f-5 3.488207f-6; - 2.0297408f-8 1.2964749f-5; - 3.5978957f-8 4.888987f-6; - 2.8848683f-6 5.0393323f-6; - 6.898147f-8 6.9411185f-6; - 1.5558234f-7 1.8736006f-6; - 1.2645486f-6 1.7634573f-5; - 8.216346f-7 6.523373f-6; - 2.5472373f-5 1.678694f-5; - 2.9439985f-7 1.0994525f-5; - 4.239163f-8 5.808637f-7; - 7.794208f-7 3.4140648f-6; - 1.9310495f-7 7.2130365f-7; - 4.003923f-9 1.7126364f-7; - 5.482545f-9 1.1192175f-6; - 1.0874128f-6 2.0096966f-8; - 3.629685f-7 1.4216151f-5; - 4.482565f-9 7.897274f-7; - 2.1106909f-10 1.7462202f-7; - 3.6087133f-8 6.777663f-8; - 2.405052f-10 1.3337237f-7; - 1.1193056f-8 8.338947f-7; - 1.4665422f-8 3.7408252f-6; - 8.474725f-8 1.2430307f-5; - 1.6162385f-9 3.0034757f-6; - 5.599793f-9 8.352669f-7; - 0.00022092198 6.780975f-5; - 1.5643963f-7 2.6067008f-7; - 3.2985386f-6 1.041418f-5; - 6.364446f-7 6.275748f-7; - 6.068754f-8 9.289247f-9; - 3.55244f-8 2.7352712f-7; - 3.327137f-6 5.8168027f-8; - 4.3423383f-6 1.8230356f-8; - 1.3081218f-8 2.0205869f-6; - 6.2152163f-9 2.624801f-7; - 7.971649f-7 2.5176298f-8; - 7.8357914f-8 2.0895153f-7; - 4.1153867f-6 5.380238f-8; - 3.9168975f-5 1.2887991f-8; - 6.733457f-8 3.2629723f-6; - 1.0652488f-7 2.0929018f-7; - 3.2242536f-7 1.970271f-6; - 3.5293866f-7 1.3604377f-7; - 5.213509f-6 6.354789f-7; - 7.573748f-8 4.3409034f-7; - 4.8114686f-7 2.2710612f-6; - 7.176653f-10 5.4512988f-5; - 1.25226825f-5 1.129455f-5; - 2.099167f-7 1.2412348f-5; - 0.0 0.0; - 0.7663358 0.90416116; - 2.0 17.0; + "od_v7_tiny_COCO_dog-cycle-car_nonsquare" => Float32[ + 0.21098387 0.24594076 + 0.16210589 0.21256444 + 0.99821246 0.5849901 + 0.5709939 0.8199513 + 0.7701267 0.90673345 + 1.4335743e-6 9.9756144e-5 + 0.7663358 0.00018426332 + 0.00015750644 7.464547e-6 + 9.5975454e-5 5.6190623e-7 + 4.1853347e-8 1.3555579e-9 + 0.005396336 1.4579688e-6 + 3.2416472e-5 1.6742394e-8 + 0.00021949186 1.0166412e-6 + 0.00027060503 2.2101574e-6 + 1.3700973e-8 1.9244644e-6 + 1.1206721e-7 1.6771866e-5 + 2.4542217e-7 8.816636e-6 + 4.3287798e-7 1.0163242e-6 + 0.00087150576 3.1314473e-6 + 1.0165805e-8 8.010309e-6 + 5.053699e-8 0.002072461 + 4.7598914e-7 0.90416116 + 3.7436752e-8 7.225644e-5 + 2.389189e-8 1.1487388e-5 + 1.0353663e-8 2.1804788e-6 + 1.1910078e-9 1.2675188e-6 + 6.4750547e-9 3.2380576e-5 + 3.709798e-8 1.1901969e-6 + 3.7169852e-9 1.1985976e-5 + 1.4390395e-5 0.00019688539 + 1.0319029e-6 2.9800987e-7 + 5.2115456e-6 1.6730553e-6 + 3.2725616e-8 6.5141954e-5 + 8.931652e-5 8.992765e-6 + 3.193306e-6 4.6158344e-5 + 1.0537543e-5 3.488207e-6 + 2.0297408e-8 1.2964749e-5 + 3.5978957e-8 4.888987e-6 + 2.8848683e-6 5.0393323e-6 + 6.898147e-8 6.9411185e-6 + 1.5558234e-7 1.8736006e-6 + 1.2645486e-6 1.7634573e-5 + 8.216346e-7 6.523373e-6 + 2.5472373e-5 1.678694e-5 + 2.9439985e-7 1.0994525e-5 + 4.239163e-8 5.808637e-7 + 7.794208e-7 3.4140648e-6 + 1.9310495e-7 7.2130365e-7 + 4.003923e-9 1.7126364e-7 + 5.482545e-9 1.1192175e-6 + 1.0874128e-6 2.0096966e-8 + 3.629685e-7 1.4216151e-5 + 4.482565e-9 7.897274e-7 + 2.1106909e-10 1.7462202e-7 + 3.6087133e-8 6.777663e-8 + 2.405052e-10 1.3337237e-7 + 1.1193056e-8 8.338947e-7 + 1.4665422e-8 3.7408252e-6 + 8.474725e-8 1.2430307e-5 + 1.6162385e-9 3.0034757e-6 + 5.599793e-9 8.352669e-7 + 0.00022092198 6.780975e-5 + 1.5643963e-7 2.6067008e-7 + 3.2985386e-6 1.041418e-5 + 6.364446e-7 6.275748e-7 + 6.068754e-8 9.289247e-9 + 3.55244e-8 2.7352712e-7 + 3.327137e-6 5.8168027e-8 + 4.3423383e-6 1.8230356e-8 + 1.3081218e-8 2.0205869e-6 + 6.2152163e-9 2.624801e-7 + 7.971649e-7 2.5176298e-8 + 7.8357914e-8 2.0895153e-7 + 4.1153867e-6 5.380238e-8 + 3.9168975e-5 1.2887991e-8 + 6.733457e-8 3.2629723e-6 + 1.0652488e-7 2.0929018e-7 + 3.2242536e-7 1.970271e-6 + 3.5293866e-7 1.3604377e-7 + 5.213509e-6 6.354789e-7 + 7.573748e-8 4.3409034e-7 + 4.8114686e-7 2.2710612e-6 + 7.176653e-10 5.4512988e-5 + 1.25226825e-5 1.129455e-5 + 2.099167e-7 1.2412348e-5 + 0.0 0.0 + 0.7663358 0.90416116 + 2.0 17.0 1.0 1.0 ], ) diff --git a/test/results/dog-cycle-car/v2_COCO_in_padded.png b/test/results/dog-cycle-car/v2_COCO_in_padded.png new file mode 100644 index 00000000..d8be6dfb Binary files /dev/null and b/test/results/dog-cycle-car/v2_COCO_in_padded.png differ diff --git a/test/results/dog-cycle-car/v2_COCO_out_darknet.png b/test/results/dog-cycle-car/v2_COCO_out_darknet.png new file mode 100644 index 00000000..bc41e9ec Binary files /dev/null and b/test/results/dog-cycle-car/v2_COCO_out_darknet.png differ diff --git a/test/results/dog-cycle-car/v2_COCO_out_od.png b/test/results/dog-cycle-car/v2_COCO_out_od.png new file mode 100644 index 00000000..ed3bc3d4 Binary files /dev/null and b/test/results/dog-cycle-car/v2_COCO_out_od.png differ diff --git a/test/results/dog-cycle-car/v2_tiny_COCO_out_darknet.png b/test/results/dog-cycle-car/v2_tiny_COCO_out_darknet.png index b5bf8ecd..0ad3f264 100644 Binary files a/test/results/dog-cycle-car/v2_tiny_COCO_out_darknet.png and b/test/results/dog-cycle-car/v2_tiny_COCO_out_darknet.png differ diff --git a/test/results/dog-cycle-car/v2_tiny_COCO_out_od.png b/test/results/dog-cycle-car/v2_tiny_COCO_out_od.png index b5bf8ecd..87bdef9f 100644 Binary files a/test/results/dog-cycle-car/v2_tiny_COCO_out_od.png and b/test/results/dog-cycle-car/v2_tiny_COCO_out_od.png differ diff --git a/test/results/dog-cycle-car_nonsquare/v2_COCO_in_padded.png b/test/results/dog-cycle-car_nonsquare/v2_COCO_in_padded.png new file mode 100644 index 00000000..857c4d82 Binary files /dev/null and b/test/results/dog-cycle-car_nonsquare/v2_COCO_in_padded.png differ diff --git a/test/results/dog-cycle-car_nonsquare/v2_COCO_out_darknet.png b/test/results/dog-cycle-car_nonsquare/v2_COCO_out_darknet.png new file mode 100644 index 00000000..e295e862 Binary files /dev/null and b/test/results/dog-cycle-car_nonsquare/v2_COCO_out_darknet.png differ diff --git a/test/results/dog-cycle-car_nonsquare/v2_COCO_out_od.png b/test/results/dog-cycle-car_nonsquare/v2_COCO_out_od.png new file mode 100644 index 00000000..48b27f23 Binary files /dev/null and b/test/results/dog-cycle-car_nonsquare/v2_COCO_out_od.png differ diff --git a/test/results/dog-cycle-car_nonsquare/v2_tiny_COCO_out_darknet.png b/test/results/dog-cycle-car_nonsquare/v2_tiny_COCO_out_darknet.png index ce8ed989..c2aacd61 100644 Binary files a/test/results/dog-cycle-car_nonsquare/v2_tiny_COCO_out_darknet.png and b/test/results/dog-cycle-car_nonsquare/v2_tiny_COCO_out_darknet.png differ diff --git a/test/results/dog-cycle-car_nonsquare/v2_tiny_COCO_out_od.png b/test/results/dog-cycle-car_nonsquare/v2_tiny_COCO_out_od.png index ff0fd3b4..d7f2708a 100644 Binary files a/test/results/dog-cycle-car_nonsquare/v2_tiny_COCO_out_od.png and b/test/results/dog-cycle-car_nonsquare/v2_tiny_COCO_out_od.png differ diff --git a/test/runtests.jl b/test/runtests.jl index 5250d2eb..19bd0e46 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -4,7 +4,6 @@ using Darknet using FileIO using Flux: cpu using ImageCore -using LazyArtifacts using OrderedCollections: OrderedDict using PrettyTables using ReferenceTests