Add Scaled-YOLOv4 family and yolov7x pretrained models

Artifacts.toml

@@ -46,6 +46,38 @@ lazy = true
     sha256 = "9e8ef718f074b15bf8b49b1750764a737367ba08a2422ea51fc67e687bba9c98"
     url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights2/yolov4-COCO.tar.gz"
 
+[yolov4-csp-COCO]
+git-tree-sha1 = "0ed4c06e067b130a78eab84aaff62621f9b08282"
+lazy = true
+
+    [[yolov4-csp-COCO.download]]
+    sha256 = "e6a6ce7551b94b3ff4f493e19670c16a1c766578b16e56d2008c6cfc7c8bec02"
+    url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights/yolov4-csp-COCO.tar.gz"
+
+[yolov4-csp-x-swish-COCO]
+git-tree-sha1 = "842cc97e64259829d7c23398ae135ab8e4fa58f1"
+lazy = true
+
+    [[yolov4-csp-x-swish-COCO.download]]
+    sha256 = "8a6c602e6c6c862ba82980c9552339fc665fc11943a4536df6456009472ededa"
+    url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights/yolov4-csp-x-swish-COCO.tar.gz"
+
+[yolov4-p5-COCO]
+git-tree-sha1 = "9d783cbaf16302afb118956b79491b23553e71f2"
+lazy = true
+
+    [[yolov4-p5-COCO.download]]
+    sha256 = "3c158eafa4719f0e502bbc5f710084d470b6af32c6fee59344c625b450acee79"
+    url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights/yolov4-p5-COCO.tar.gz"
+
+[yolov4-p6-COCO]
+git-tree-sha1 = "cdee6a06e053e68aa606e5761312667ac778a8d5"
+lazy = true
+
+    [[yolov4-p6-COCO.download]]
+    sha256 = "7ae1725e5b0d11ee09ef4cc40dcd4e8a7223302a42d3802df0ec28e46a235e3d"
+    url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights/yolov4-p6-COCO.tar.gz"
+
 [yolov4-tiny-COCO]
 git-tree-sha1 = "ddad97b7d60da0cffd2b12003a5c42e88fcaedae"
 lazy = true
@@ -54,6 +86,14 @@ lazy = true
     sha256 = "6e96d427f769d2dd2f1aadf96f6b8d8e8e19fc5a6e8e81ffa4dc8bda9c7d6ea3"
     url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights2/yolov4-tiny-COCO.tar.gz"
 
+[yolov4x-mish-COCO]
+git-tree-sha1 = "b04bfb646371d6e2eefd737b4154bca45b096ccd"
+lazy = true
+
+    [[yolov4x-mish-COCO.download]]
+    sha256 = "3bdd747199ddf911af9f65ea3aae9f361ee17835803ca110357e0c7160f8f685"
+    url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights/yolov4x-mish-COCO.tar.gz"
+
 [yolov7-COCO]
 git-tree-sha1 = "817fb08f81f72429d84a08d552d54d8195d937de"
 lazy = true
@@ -69,3 +109,11 @@ lazy = true
     [[yolov7-tiny-COCO.download]]
     sha256 = "d46863dfebb4b6008eb91f291ba59281bfd6d256c828a33b825969695abcea1a"
     url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights2/yolov7-tiny-COCO.tar.gz"
+
+[yolov7x-COCO]
+git-tree-sha1 = "57a1c0aa23f2bf5ef80d46f1eeb965ccedfc61f5"
+lazy = true
+
+    [[yolov7x-COCO.download]]
+    sha256 = "1acd8798e8eaa4836c403501315084ba0a0d9f107355506186ed295a249bebac"
+    url = "https://github.com/r3tex/ObjectDetector.jl/releases/download/weights/yolov7x-COCO.tar.gz"

CHANGELOG.md

@@ -2,6 +2,12 @@
 
 ## Unreleased
 
+### New models
+- Add the Scaled-YOLOv4 family and yolov7x, all with official COCO weights:
+  `v4_csp_COCO`, `v4_csp_x_swish_COCO`, `v4x_mish_COCO`, `v4_p5_COCO`, `v4_p6_COCO`,
+  `v7x_COCO`. Pretrained models now default to their native input size
+  (416 for the previously-supported models, unchanged).
+
 ### Performance
 - Batchnorm is now folded into the conv weights at load time (as darknet's
   `fuse_conv_batchnorm` does), removing the BatchNorm pass entirely: ~4-9% faster forward

Project.toml

@@ -13,7 +13,6 @@ 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"
@@ -28,9 +27,6 @@ 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"]
 
@@ -42,7 +38,6 @@ CUDA = "4, 5, 6"
 Cairo = "1.1.1"
 Colors = "0.13.0"
 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"

README.md

@@ -2,7 +2,7 @@
 
 Object detection via YOLO in Julia. YOLO models are loaded directly from Darknet .cfg and .weights files as Flux models. Uses CUDA, if available.
 
-Supported YOLO models are: `v2`, `v2-tiny`, `v3`, `v3-spp`, `v3-tiny`, `v4`, `v4-tiny`, `v7`, `v7-tiny`
+Supported YOLO models are: `v2`, `v2-tiny`, `v3`, `v3-spp`, `v3-tiny`, `v4`, `v4-tiny`, `v4-csp`, `v4-csp-x-swish`, `v4x-mish`, `v4-p5`, `v4-p6` (Scaled-YOLOv4), `v7`, `v7-tiny`, `v7x`
 
 Other less standard models may work also.
 
@@ -111,7 +111,20 @@ YOLO.v4_tiny_COCO()
 
 YOLO.v7_COCO()
 YOLO.v7_tiny_COCO()
+
+# Scaled-YOLOv4 family (native sizes: csp 512, csp_x_swish & x_mish 640, p5 896, p6 1280)
+YOLO.v4_csp_COCO()
+YOLO.v4_csp_x_swish_COCO()
+YOLO.v4x_mish_COCO()
+YOLO.v4_p5_COCO()
+YOLO.v4_p6_COCO()
+
+# larger yolov7 (native size 640)
+YOLO.v7x_COCO()
 ```
+
+Each model defaults to its native input size; pass `w`/`h` to override. Note
+`v4_p6` requires dimensions divisible by 64 (the others require 32).
 Their width and height can be modified with:
 ```julia
 YOLO.v3_COCO(w=416,h=416)