Torchserve 服务器使用在 docker 上运行的 YoloV5 模型，使用 GPU 和静态批量推理来执行生产就绪和实时...

<a href="https://apps.apple.com/app/id1452689527" target="_blank"> <img src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg" width="1000"></a> &nbsp  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/26833433/90187293-6773ba00-dd6e-11ea-8f90-cd94afc0427f.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 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. - **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP. - **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. ## Pretrained Checkpoints | Model | AP^val | AP^test | AP₅₀ | Speed_GPU | FPS_GPU || params | FLOPS | |---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: | | [YOLOv5s](https://github.com/ultralytics/yolov5/releases) | 37.0 | 37.0 | 56.2 | **2.4ms** | **416** || 7.5M | 13.2B | [YOLOv5m](https://github.com/ultralytics/yolov5/releases) | 44.3 | 44.3 | 63.2 | 3.4ms | 294 || 21.8M | 39.4B | [YOLOv5l](https://github.com/ultralytics/yolov5/releases) | 47.7 | 47.7 | 66.5 | 4.4ms | 227 || 47.8M | 88.1B | [YOLOv5x](https://github.com/ultralytics/yolov5/releases) | **49.2** | **49.2** | **67.7** | 6.9ms | 145 || 89.0M | 166.4B | | | | | | || | | [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA|**50.8**| **50.8** | **68.9** | 25.5ms | 39 || 89.0M | 354.3B | | | | | | || | | [YOLOv3-SPP](https://github.com/ultralytics/yolov5/releases) | 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 denote val2017 accuracy. ** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` ** Speed_GPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` ## Requirements Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run: ```bash $ pip install -r requirements.txt ``` ## Tutorials * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)&nbsp; 🚀 RECOMMENDED * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ NEW * [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) * [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)&nbsp; ⭐ NEW * [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 detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```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 rtmp://192.168.1.105/live/test # rtmp stream http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream ``` To run inference on example images in `data/images`: ```bash $ python detect.py --source data/images --weights yolov5s.pt --conf 0.25 Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt']) Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB) Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s] Fusing layers... Model Summary: 232 layers, 7459581 parameters, 0 gradients image 1/2 data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s) im