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👁 EdgeYOLO: An Edge-Real-Time Object Detector

EdgeYOLO reaches 34FPS with 50.6% AP in COCO2017 dataset and 25.9% AP in VisDrone2019 (image input size is 640x640, batch=16, post-process included).

Новый детектор обнаружения небольших объектов с высокой точностью, не требующий больших вычислительных мощностей.

🖥 Github: https://github.com/lsh9832/edgeyolo

⭐️Paper: https://arxiv.org/abs/2302.07483v1

⭐️ Weights: https://github.com/LSH9832/edgeyolo/releases/tag/v0.0.0

💻 Dataset: https://paperswithcode.com/dataset/visdrone

👉 @bigdata_1
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👁 EdgeYOLO: An Edge-Real-Time Object Detector

EdgeYOLO reaches 34FPS with 50.6% AP in COCO2017 dataset and 25.9% AP in VisDrone2019 (image input size is 640x640, batch=16, post-process included).

Новый детектор обнаружения небольших объектов с высокой точностью, не требующий больших вычислительных мощностей.

🖥 Github: https://github.com/lsh9832/edgeyolo

⭐️Paper: https://arxiv.org/abs/2302.07483v1

⭐️ Weights: https://github.com/LSH9832/edgeyolo/releases/tag/v0.0.0

💻 Dataset: https://paperswithcode.com/dataset/visdrone

👉 @bigdata_1

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