yolov3-master.zip

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yolov3-master.zip
  • yolov3-master
  • models
  • common.py
    12.3KB
  • yolo.py
    12.3KB
  • yolov3-tiny.yaml
    1.2KB
  • yolov3-spp.yaml
    1.5KB
  • yolov3.yaml
    1.5KB
  • __init__.py
    0B
  • export.py
    3.8KB
  • experimental.py
    4.9KB
  • data
  • images
  • bus.jpg
    476KB
  • zidane.jpg
    165KB
  • scripts
  • get_coco.sh
    935B
  • get_voc.sh
    4.3KB
  • coco.yaml
    1.7KB
  • hyp.scratch.yaml
    1.5KB
  • hyp.finetune.yaml
    846B
  • voc.yaml
    735B
  • coco128.yaml
    1.5KB
  • .github
  • ISSUE_TEMPLATE
  • feature-request.md
    737B
  • bug-report.md
    1.5KB
  • question.md
    140B
  • workflows
  • rebase.yml
    542B
  • greetings.yml
    4.4KB
  • codeql-analysis.yml
    2KB
  • ci-testing.yml
    2.9KB
  • stale.yml
    852B
  • dependabot.yml
    201B
  • weights
  • download_weights.sh
    290B
  • utils
  • google_app_engine
  • additional_requirements.txt
    105B
  • Dockerfile
    821B
  • app.yaml
    173B
  • loss.py
    8KB
  • plots.py
    16.9KB
  • metrics.py
    7.8KB
  • general.py
    20.6KB
  • datasets.py
    41.3KB
  • activations.py
    2.2KB
  • autoanchor.py
    6.8KB
  • torch_utils.py
    11.7KB
  • __init__.py
    0B
  • google_utils.py
    4.8KB
  • tutorial.ipynb
    382.1KB
  • test.py
    15.9KB
  • train.py
    30.8KB
  • Dockerfile
    1.7KB
  • LICENSE
    34.3KB
  • detect.py
    7.9KB
  • requirements.txt
    602B
  • .gitignore
    3.9KB
  • .dockerignore
    3.5KB
  • README.md
    9.1KB
  • hubconf.py
    4.4KB
  • .gitattributes
    72B
内容介绍
<a href="https://apps.apple.com/app/id1452689527" target="_blank" rel='nofollow' onclick='return false;'> <img src="https://user-images.githubusercontent.com/26833433/99805965-8f2ca800-2b3d-11eb-8fad-13a96b222a23.jpg" width="1000"></a> &nbsp <a href="https://github.com/ultralytics/yolov3/actions" rel='nofollow' onclick='return false;'><img src="https://github.com/ultralytics/yolov3/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a> BRANCH NOTICE: The [ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository is now divided into two branches: * [Master branch](https://github.com/ultralytics/yolov3/tree/master): Forward-compatible with all [YOLOv5](https://github.com/ultralytics/yolov5) models and methods (**recommended**). ```bash $ git clone https://github.com/ultralytics/yolov3 # master branch (default) ``` * [Archive branch](https://github.com/ultralytics/yolov3/tree/archive): Backwards-compatible with original [darknet](https://pjreddie.com/darknet/) *.cfg models (⚠️ no longer maintained). ```bash $ git clone -b archive https://github.com/ultralytics/yolov3 # archive branch ``` <img src="https://user-images.githubusercontent.com/26833433/100382066-c8bc5200-301a-11eb-907b-799a0301595e.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. ## Pretrained Checkpoints | Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS | |---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: | | [YOLOv3](https://github.com/ultralytics/yolov3/releases) | 43.3 | 43.3 | 63.0 | 4.8ms | 208 || 61.9M | 156.4B | [YOLOv3-SPP](https://github.com/ultralytics/yolov3/releases) | **44.3** | **44.3** | **64.6** | 4.9ms | 204 || 63.0M | 157.0B | [YOLOv3-tiny](https://github.com/ultralytics/yolov3/releases) | 17.6 | 34.9 | 34.9 | **1.7ms** | **588** || 8.9M | 13.3B ** AP<sup>test</sup> 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<sub>GPU</sub> 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/yolov3/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/yolov3/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 YOLOv3 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/yolov3/blob/master/tutorial.ipynb" rel='nofollow' onclick='return false;'><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/yolov3](https://www.kaggle.com/ultralytics/yolov3) - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) - **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker) ## Inference detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/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 yolov3.pt --conf 0.25 Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov3.pt']) Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB) Downloading https://github.com/ultralytics/yolov3/releases/download/v1.0/yolov3.pt to yolov3.pt... 100% 118M/118M [00:05<00:00, 24.2MB/s] Fusing layers... Model Summary: 261 layers, 61922845 parameters, 0 gradients image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 buss, Done. (0.014s) image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.014s) Results saved to runs/detect/exp Done. (0.133s) ``` <img src="https://user-images.githubusercontent.com/26833433/100375993-06b37900-300f-11eb-8d2d-5fc7b22fbfbd.jpg" width="500"> ### PyTorch Hub To run **batched inference** with YOLO3 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36): ```python import torch from PIL import Image # Model model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True).autoshape() # for PIL/cv2/np inputs and NMS # Images img1 = Image.open('zidane.jpg') img2 = Image.open('bus.jpg') imgs = [img1, img2] # batched list of images # Inference prediction = model(imgs, size=640) # includes NMS ``` ## Training Download [COCO](https://github.com/ultralytics/yolov3/blob/master/data/scripts/get_coco.sh) and run command below. Training times for YOLOv3/YOLOv3-SPP/YOLOv3-tiny are 6/6/2 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). ```bash $ python train.py --data coco.yaml --cfg yolov3.yaml --weights
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