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YOLOv6 TensorRT Windows C++: yolort from Wei Zeng.

YOLOv6 TensorRT Python: yolov6-tensorrt-python from Linaom1214.

YOLOv6 ONNXRuntime/MNN/TNN C++: YOLOv6-ORT, YOLOv6-MNN and YOLOv6-TNN from DefTruth.

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YOLOv6 NCNN Android app demo: ncnn-android-yolov6 from FeiGeChuanShu.

For M/L/L-ReLU models, we adopt self-distillation methods to further improve the performance.

For N/T/S models, we use more training epochs strategy.

Params and FLOPs of YOLOv6 are estimated on deployed models.

Refer to Test speed tutorial to reproduce the speed results of YOLOv6.

Results of the mAP and speed are evaluated on COCO val2017 dataset with the input resolution of 640×640.

# remember to replace /path/to/your/checkpoint/path to the checkpoint path which you want to resume training.

Fix the degration of performance when evaluating on 640圆40 inputs.

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Release M/L models and update N/T/S models with enhanced performance.

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YOLOv6-S strikes 43.5% AP with 495 FPS, and the quantized YOLOv6-S model achieves 43.3% AP at a accelerated speed of 869 FPS on T4. Mxtube.Net, youtube to mp3, download youtube, mymp3song, hindi music lyrics ,download punjabi music, free punjabi music, hindi songs mp3 ,punjabi wap. Coco Bluray Full Hindi Dubbed Movie Download Filmyzilla, Coco (2017) Bluray Hindi 480p Movie Download FilmyZilla.Com, Coco Hindi Dubbed HD Mp4 Movie. YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. For industrial deployment, we adopt QAT with channel-wise distillation and graph optimization to pursue extreme performance. And some Bag-of-freebies methods are introduced to further improve the performance, such as self-distillation and more training epochs. YOLOv6 has a series of models for various industrial scenarios, including N/T/S/M/L, which the architectures vary considering the model size for better accuracy-speed trade-off. YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. Implementation of paper - YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications Introduction