目标检测模型-YOLOv4-Pytorch版本

# YOLOv4 This is PyTorch implementation of [YOLOv4](https://github.com/AlexeyAB/darknet) which is based on [ultralytics/yolov3](https://github.com/ultralytics/yolov3). * [[original Darknet implementation of YOLOv4]](https://github.com/AlexeyAB/darknet) * [[ultralytics/yolov5 based PyTorch implementation of YOLOv4]](https://github.com/WongKinYiu/PyTorch_YOLOv4/tree/u5). ### development log <details><summary> <b>Expand</b> </summary> * `2021-10-31` - support [RS loss](https://arxiv.org/abs/2107.11669), [aLRP loss](https://arxiv.org/abs/2009.13592), [AP loss](https://arxiv.org/abs/2008.07294). * `2021-10-30` - support [alpha IoU](https://arxiv.org/abs/2110.13675). * `2021-10-20` - design resolution calibration methods. * `2021-10-15` - support joint detection, instance segmentation, and semantic segmentation. [`seg-yolo`]() * `2021-10-13` - design ratio yolo. * `2021-09-22` - pytorch 1.9 compatibility. * `2021-09-21` - support [DIM](https://arxiv.org/abs/1808.06670). * `2021-09-16` - support [Dynamic Head](https://arxiv.org/abs/2106.08322). * `2021-08-28` - design domain adaptive training. * `2021-08-22` - design re-balance models. * `2021-08-21` - support [simOTA](https://arxiv.org/abs/2107.08430). * `2021-08-14` - design approximation-based methods. * `2021-07-27` - design new decoders. * `2021-07-22` - support 1) decoupled head, 2) anchor-free, and 3) multi positives in [yolox](https://arxiv.org/abs/2107.08430). * `2021-07-10` - design distribution-based implicit modeling. * `2021-07-06` - support outlooker attention. [`volo`](https://arxiv.org/abs/2106.13112) * `2021-07-06` - design self emsemble training method. * `2021-06-23` - design cross multi-stage correlation module. * `2021-06-18` - design cross stage cross correlation module. * `2021-06-17` - support cross correlation module. [`ccn`](https://arxiv.org/abs/2010.12138) * `2021-06-17` - support attention modules. [`cbam`](https://arxiv.org/abs/1807.06521) [`saan`](https://arxiv.org/abs/2010.12138) * `2021-04-20` - support swin transformer. [`swin`](https://arxiv.org/abs/2103.14030) * `2021-03-16` - design new stem layers. * `2021-03-13` - design implicit modeling. [`nn`]() [`mf`]() [`lc`]() * `2021-01-26` - support vision transformer. [`tr`](https://arxiv.org/abs/2010.11929) * `2021-01-26` - design mask objectness. * `2021-01-25` - design rotate augmentation. * `2021-01-23` - design collage augmentation. * `2021-01-22` - support [VoVNet](https://arxiv.org/abs/1904.09730), [VoVNetv2](https://arxiv.org/abs/1911.06667). * `2021-01-22` - support [EIoU](https://arxiv.org/abs/2101.08158). * `2021-01-19` - support instance segmentation. [`mask-yolo`]() * `2021-01-17` - support anchor-free-based methods. [`center-yolo`]() * `2021-01-14` - support joint detection and classification. [`classify-yolo`]() * `2020-01-02` - design new [PRN](https://github.com/WongKinYiu/PartialResidualNetworks) and [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks)-based models. * `2020-12-22` - support transfer learning. * `2020-12-18` - support non-local series self-attention blocks. [`gc`](https://arxiv.org/abs/1904.11492) [`dnl`](https://arxiv.org/abs/2006.06668) * `2020-12-16` - support down-sampling blocks in cspnet paper. [`down-c`]() [`down-d`](https://arxiv.org/abs/1812.01187) * `2020-12-03` - support imitation learning. * `2020-12-02` - support [squeeze and excitation](https://arxiv.org/abs/1709.01507). * `2020-11-26` - support multi-class multi-anchor joint detection and embedding. * `2020-11-25` - support [joint detection and embedding](https://arxiv.org/abs/1909.12605). [`track-yolo`]() * `2020-11-23` - support teacher-student learning. * `2020-11-17` - pytorch 1.7 compatibility. * `2020-11-06` - support inference with initial weights. * `2020-10-21` - fully supported by darknet. * `2020-09-18` - design fine-tune methods. * `2020-08-29` - support [deformable kernel](https://arxiv.org/abs/1910.02940). * `2020-08-25` - pytorch 1.6 compatibility. * `2020-08-24` - support channel last training/testing. * `2020-08-16` - design CSPPRN. * `2020-08-15` - design deeper model. [`csp-p6-mish`]() * `2020-08-11` - support [HarDNet](https://arxiv.org/abs/1909.00948). [`hard39-pacsp`]() [`hard68-pacsp`]() [`hard85-pacsp`]() * `2020-08-10` - add DDP training. * `2020-08-06` - support [DCN](https://arxiv.org/abs/1703.06211), [DCNv2](https://arxiv.org/abs/1811.11168). [`yolov4-dcn`]() * `2020-08-01` - add pytorch hub. * `2020-07-31` - support [ResNet](https://arxiv.org/abs/1512.03385), [ResNeXt](https://arxiv.org/abs/1611.05431), [CSPResNet](https://github.com/WongKinYiu/CrossStagePartialNetworks), [CSPResNeXt](https://github.com/WongKinYiu/CrossStagePartialNetworks). [`r50-pacsp`]() [`x50-pacsp`]() [`cspr50-pacsp`]() [`cspx50-pacsp`]() * `2020-07-28` - support [SAM](https://arxiv.org/abs/2004.10934). [`yolov4-pacsp-sam`]() * `2020-07-24` - update api. * `2020-07-23` - support CUDA accelerated Mish activation function. * `2020-07-19` - support and training tiny YOLOv4. [`yolov4-tiny`]() * `2020-07-15` - design and training conditional YOLOv4. [`yolov4-pacsp-conditional`]() * `2020-07-13` - support [MixUp](https://arxiv.org/abs/1710.09412) data augmentation. * `2020-07-03` - design new stem layers. * `2020-06-16` - support floating16 of GPU inference. * `2020-06-14` - convert .pt to .weights for darknet fine-tuning. * `2020-06-13` - update multi-scale training strategy. * `2020-06-12` - design scaled YOLOv4 follow [ultralytics](https://github.com/ultralytics/yolov5). [`yolov4-pacsp-s`]() [`yolov4-pacsp-m`]() [`yolov4-pacsp-l`]() [`yolov4-pacsp-x`]() * `2020-06-07` - design [scaling methods](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/images/scalingCSP.png) for CSP-based models. [`yolov4-pacsp-25`]() [`yolov4-pacsp-75`]() * `2020-06-03` - update COCO2014 to COCO2017. * `2020-05-30` - update FPN neck to CSPFPN. [`yolov4-yocsp`]() [`yolov4-yocsp-mish`]() * `2020-05-24` - update neck of YOLOv4 to CSPPAN. [`yolov4-pacsp`]() [`yolov4-pacsp-mish`]() * `2020-05-15` - training YOLOv4 with Mish activation function. [`yolov4-yospp-mish`]() [`yolov4-paspp-mish`]() * `2020-05-08` - design and training YOLOv4 with [FPN](https://arxiv.org/abs/1612.03144) neck. [`yolov4-yospp`]() * `2020-05-01` - training YOLOv4 with Leaky activation function using PyTorch. [`yolov4-paspp`]() [`PAN`](https://arxiv.org/abs/1803.01534) </details> ## Pretrained Models & Comparison | Model | Test Size | AP^test | AP₅₀^test | AP₇₅^test | AP_S^test | AP_M^test | AP_L^test | cfg | weights | | :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | | **YOLOv4** | 640 | 50.0% | 68.4% | 54.7% | 30.5% | 54.3% | 63.3% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4.cfg) | [weights](https://drive.google.com/file/d/1TSvLHH48eJJk7Glr5p2lscVet2jCazhi/view?usp=sharing) | | | | | | | | | | **YOLOv4**_pacsp-s | 640 | 39.0% | 57.8% | 42.4% | 20.6% | 42.6% | 50.0% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-csp-s-leaky.cfg) | [weights](https://drive.google.com/file/d/1r1zeY8whdZNUGisxiZQFnbwYSIolCAwi/view?usp=sharing) | | **YOLOv4**_pacsp | 640 | 49.8% | 68.4% | 54.3% | 30.1% | 54.0% | 63.4% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-csp-leaky.cfg) | [weights](https://drive.google.com/file/d/1W_zrTbCmctTgnv6BSjmNDJ3xGdKye4sw/view?usp=sharing) | | **YOLOv4**_pacsp-x | 640 | **52.2%** | **70.5%** | **56.8%** | **32.7%** | **56.3%** | **65.9%** | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/master/cfg/yolov4-csp-x-leaky.cfg) | [weights](https://drive.google.com/file/d/1jL9727DVG2-iirG9EWRtAsa4vFei-L35/view?usp=sharing) | | | | | | | | | | **YOLOv4**_pacsp-s-mish | 640 | 40.8% | 59.5% | 44.3% | 22.4% | 44.6% | 51.8% | [cfg](https://github.com/WongKinYiu/PyTorch_YOLOv4/blob/m