148 lines
9.1 KiB
Markdown
Executable File
148 lines
9.1 KiB
Markdown
Executable File
<a href="https://apps.apple.com/app/id1452689527" target="_blank">
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<img src="https://user-images.githubusercontent.com/26833433/99805965-8f2ca800-2b3d-11eb-8fad-13a96b222a23.jpg" width="1000"></a>
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<a href="https://github.com/ultralytics/yolov3/actions"><img src="https://github.com/ultralytics/yolov3/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
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BRANCH NOTICE: The [ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository is now divided into two branches:
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* [Master branch](https://github.com/ultralytics/yolov3/tree/master): Forward-compatible with all [YOLOv5](https://github.com/ultralytics/yolov5) models and methods (**recommended**).
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```bash
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$ git clone https://github.com/ultralytics/yolov3 # master branch (default)
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```
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* [Archive branch](https://github.com/ultralytics/yolov3/tree/archive): Backwards-compatible with original [darknet](https://pjreddie.com/darknet/) *.cfg models (⚠️ no longer maintained).
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```bash
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$ git clone https://github.com/ultralytics/yolov3 -b archive # archive branch
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```
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<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.
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## Pretrained Checkpoints
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| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
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|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: |
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| [YOLOv3](https://github.com/ultralytics/yolov3/releases) | 43.3 | 43.3 | 63.0 | 4.8ms | 208 || 61.9M | 156.4B
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| [YOLOv3-SPP](https://github.com/ultralytics/yolov3/releases) | **44.3** | **44.3** | **64.6** | 4.9ms | 204 || 63.0M | 157.0B
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| [YOLOv3-tiny](https://github.com/ultralytics/yolov3/releases) | 17.6 | 34.9 | 34.9 | **1.7ms** | **588** || 8.9M | 13.3B
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** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.
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** 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`
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** 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`
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** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
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** 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`
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## Requirements
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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:
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```bash
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$ pip install -r requirements.txt
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```
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## Tutorials
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* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) 🚀 RECOMMENDED
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* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
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* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
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* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW
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* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)
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* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
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* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
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* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
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* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
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* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW
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* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
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## Environments
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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):
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- **Google Colab Notebook** with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov3](https://www.kaggle.com/ultralytics/yolov3)
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- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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- **Docker Image** https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) 
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## Inference
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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`.
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```bash
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$ python detect.py --source 0 # webcam
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file.jpg # image
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file.mp4 # video
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path/ # directory
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path/*.jpg # glob
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rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
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rtmp://192.168.1.105/live/test # rtmp stream
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http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
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```
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To run inference on example images in `data/images`:
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```bash
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$ python detect.py --source data/images --weights yolov3.pt --conf 0.25
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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'])
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Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)
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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]
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Fusing layers...
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Model Summary: 261 layers, 61922845 parameters, 0 gradients
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image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 buss, Done. (0.014s)
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image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.014s)
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Results saved to runs/detect/exp
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Done. (0.133s)
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```
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<img src="https://user-images.githubusercontent.com/26833433/100375993-06b37900-300f-11eb-8d2d-5fc7b22fbfbd.jpg" width="500">
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### PyTorch Hub
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To run **batched inference** with YOLO3 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
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```python
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import torch
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from PIL import Image
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# Model
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model = torch.hub.load('ultralytics/yolov3', 'yolov3', pretrained=True).autoshape() # for PIL/cv2/np inputs and NMS
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# Images
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img1 = Image.open('zidane.jpg')
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img2 = Image.open('bus.jpg')
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imgs = [img1, img2] # batched list of images
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# Inference
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prediction = model(imgs, size=640) # includes NMS
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```
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## Training
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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).
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```bash
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$ python train.py --data coco.yaml --cfg yolov3.yaml --weights '' --batch-size 24
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yolov3-spp.yaml 24
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yolov3-tiny.yaml 64
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```
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<img src="https://user-images.githubusercontent.com/26833433/100378028-af170c80-3012-11eb-8521-f0d2a8d021bc.png" width="900">
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## Citation
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[](https://zenodo.org/badge/latestdoi/146165888)
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## About Us
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Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
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- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.**
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- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
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- **Custom data training**, hyperparameter evolution, and model exportation to any destination.
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For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
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## Contact
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**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
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