opencv基础，pytorch 基础知识，pytorch实现的基础的网络结构

<a href="https://apps.apple.com/app/id1452689527" target="_blank"> <img src="https://user-images.githubusercontent.com/26833433/82944393-f7644d80-9f4f-11ea-8b87-1a5b04f555f1.jpg" width="1000"></a> &nbsp  本代码是官方代码的注释版本，有兴趣的可以下载阅读。除了注释和可视化代码外，其余部分都没有任何修改，截止2020.08.09 请配合[微信公众号](https://mp.weixin.qq.com/s?__biz=MzUyMjE2MTE0Mw==&mid=2247491231&idx=1&sn=f977e9a0a0a93f0b6b8722a881c196e8&chksm=f9d14007cea6c91193d424d7e13767126d01ab421bff119f994c48cfc870a127fba9de4cf17a&mpshare=1&scene=1&srcid=0814INoD9iew57hpnXYVJwPt&sharer_sharetime=1597363225239&sharer_shareid=c553e41483c1830b4e61b371606920a5&exportkey=AdHs3j%2FP1UQtE%2FDSGoYVIRw%3D&pass_ticket=IuL0rmjnUBIudh8LY9JlGxO9o537c4MY9aNCnSiN1sRBdDL8S12dk3fN2XHaDUYH&wx_header=0#rd)或者[知乎文章](https://zhuanlan.zhihu.com/p/183838757)进行阅读，体验更好 This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk. <img src="https://user-images.githubusercontent.com/26833433/85340570-30360a80-b49b-11ea-87cf-bdf33d53ae15.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. - **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP. - **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972). - **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145). - **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP). - **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations. - **April 1, 2020**: Start development of future compound-scaled [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models. ## Pretrained Checkpoints | Model | AP^val | AP^test | AP₅₀ | Speed_GPU | FPS_GPU || params | FLOPS | |---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: | | [YOLOv5s](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 36.1 | 36.1 | 55.3 | **2.1ms** | **476** || 7.5M | 13.2B | [YOLOv5m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 43.5 | 43.5 | 62.5 | 3.0ms | 333 || 21.8M | 39.4B | [YOLOv5l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 47.0 | 47.1 | 65.6 | 3.9ms | 256 || 47.8M | 88.1B | [YOLOv5x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | **49.0** | **49.0** | **67.4** | 6.1ms | 164 || 89.0M | 166.4B | | | | | | || | | [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B ** AP^test denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy. ** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 672 --conf 0.001` ** Speed_GPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by `python test.py --data coco.yaml --img 640 --conf 0.1` ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ## Requirements Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.6`. To install run: ```bash $ pip install -r requirements.txt ``` ## Tutorials * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) * [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251) * [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) * [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) * [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) * [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) * [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx) ## Environments YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - **Google Colab Notebook** with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> - **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5) - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) - **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart)  ## Inference Inference can be run on most common media formats. Model [checkpoints](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) are downloaded automatically if available. Results are saved to `./inference/output`. ```bash $ python detect.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream ``` To run inference on examples in the `./inference/images` folder: ```bash $ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4 Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt') Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB) Downloading https://drive.google.com/uc?e