From af5f8a907877c354f2297ada97564615a4918af8 Mon Sep 17 00:00:00 2001 From: dronefreak Date: Mon, 13 Jul 2026 19:54:12 +0200 Subject: [PATCH] chore: Update README Signed-off-by: dronefreak --- README.md | 40 +++++++++++----------------------------- 1 file changed, 11 insertions(+), 29 deletions(-) diff --git a/README.md b/README.md index 6dac5cd..8f44e41 100644 --- a/README.md +++ b/README.md @@ -14,24 +14,6 @@ CABiNet (Context Aggregation Network) is a dual-branch convolutional neural netw UAVid Semantic Segmentation Demo

-## Key Features - -- **High Performance**: Achieves 75.9% mIoU on Cityscapes test set at 76 FPS (NVIDIA RTX 2080Ti) -- **Edge Deployment**: 8 FPS on Jetson Xavier NX for embedded applications -- **Dual-Branch Architecture**: Combines high-resolution spatial detailing with efficient context aggregation -- **Lightweight Design**: Reduced computational overhead through optimized global and local context blocks -- **Multi-Scale Support**: Effective feature extraction across different scales - -## Performance - -| Model | Dataset | mIoU | FPS (RTX 2080Ti) | FPS (Jetson Xavier NX) | Params (M) | -| ------------------ | ---------- | ----- | ---------------- | ---------------------- | ---------- | -| **CABiNet (ours)** | Cityscapes | 75.9% | 76 | 8 | ~3.0 | -| YOLOv8m-seg | Cityscapes | 73.5% | — | — | 27.3 | -| YOLOv11m-seg | Cityscapes | 74.2% | — | — | 22.4 | - -Cityscapes numbers above are literature-reported instance-segmentation (`-seg`) baselines, kept for historical reference. For UAVid, CABiNet is benchmarked directly against Ultralytics' dense semantic-segmentation task (`yolo26*-sem`, not `-seg`) under one shared pipeline — see [UAVid Model Zoo](#uavid-model-zoo) for the full per-model comparison. - ## Architecture The CABiNet architecture employs a dual-branch design that balances spatial detail preservation and contextual understanding: @@ -63,17 +45,17 @@ _Columns: Input images, State-of-the-art predictions, CABiNet predictions (white ## UAVid Model Zoo -CABiNet and Ultralytics YOLO26 (`semantic` task, dense per-pixel — not `-seg`) are trained and evaluated under one shared UAVid pipeline (`images/`+`masks/` format, 8 classes incl. Clutter). Full per-class breakdowns and model cards: [`hf_modelcards/`](hf_modelcards/); HF weights below will be populated once the models are pushed. - -| Model | mIoU (%) | Params (M) | FLOPs (B) | HF Weights | -| --------------------------- | -------- | ---------- | --------- | ---------- | -| CABiNet (MobileNetV3-Large) | TBD¹ | TBD | TBD | TBD | -| CABiNet (MobileNetV3-Small) | TBD² | TBD | TBD | TBD | -| YOLO26n-sem | 63.58 | TBD | TBD | TBD | -| YOLO26s-sem | 66.88 | TBD | TBD | TBD | -| YOLO26m-sem | 67.66 | TBD | TBD | TBD | -| YOLO26l-sem | 67.20 | TBD | TBD | TBD | -| YOLO26x-sem | TBD³ | TBD | TBD | TBD | +CABiNet and Ultralytics YOLO26 (`semantic` task, dense per-pixel — not `-seg`) are trained and evaluated under one shared UAVid pipeline (`images/`+`masks/` format, 8 classes incl. Clutter). + +| Model | mIoU (%) | Params (M) | FLOPs (B) | HF Weights | +| --------------------------- | -------- | ---------- | --------- | ----------------------------------------------------------------------------- | +| CABiNet (MobileNetV3-Large) | TBD¹ | TBD | TBD | [HF Model](https://huggingface.co/dronefreak/cabinet-mobilenetv3-small-uavid) | +| CABiNet (MobileNetV3-Small) | TBD² | TBD | TBD | [HF Model](https://huggingface.co/dronefreak/cabinet-mobilenetv3-large-uavid) | +| YOLO26n-sem | 63.58 | TBD | TBD | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26n-sem) | +| YOLO26s-sem | 66.88 | TBD | TBD | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26s-sem) | +| YOLO26m-sem | 67.66 | TBD | TBD | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26m-sem) | +| YOLO26l-sem | 67.20 | TBD | TBD | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26l-sem) | +| YOLO26x-sem | TBD³ | TBD | TBD | [HF Model](https://huggingface.co/dronefreak/uavid-yolo26x-sem) | ¹ Trained under the refactored pipeline but not yet re-evaluated. ² Not yet trained. ³ Not yet trained.