161 lines
9.5 KiB
Markdown
Executable File
161 lines
9.5 KiB
Markdown
Executable File
<a align="left" href="https://apps.apple.com/app/id1452689527" target="_blank">
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<img width="800" src="https://user-images.githubusercontent.com/26833433/99805965-8f2ca800-2b3d-11eb-8fad-13a96b222a23.jpg"></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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This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
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<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114424655-a0dc1e00-9bb8-11eb-9a2e-cbe21803f05c.png"></p>
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<details>
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<summary>YOLOv5-P5 640 Figure (click to expand)</summary>
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<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
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</details>
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<details>
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<summary>Figure Notes (click to expand)</summary>
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* 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.
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* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
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* **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov3.pt yolov3-spp.pt yolov3-tiny.pt yolov5l.pt`
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</details>
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## Branch Notice
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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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## Pretrained Checkpoints
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[assets3]: https://github.com/ultralytics/yolov3/releases
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[assets5]: https://github.com/ultralytics/yolov5/releases
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Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
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--- |--- |--- |--- |--- |--- |---|--- |---
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[YOLOv3-tiny][assets3] |640 |17.6 |17.6 |34.8 |**1.2** | |8.8 |13.2
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[YOLOv3][assets3] |640 |43.3 |43.3 |63.0 |4.1 | |61.9 |156.3
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[YOLOv3-SPP][assets3] |640 |44.3 |44.3 |64.6 |4.1 | |63.0 |157.1
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[YOLOv5l][assets5] |640 |**48.2** |**48.2** |**66.9** |3.7 | |47.0 |115.4
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<details>
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<summary>Table Notes (click to expand)</summary>
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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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* AP values are for single-model single-scale unless otherwise noted. **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 FP16 inference, postprocessing and NMS. **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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</details>
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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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* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ RECOMMENDED
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* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
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* [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518) 🌟 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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* [TorchScript, ONNX, CoreML 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 and Kaggle** notebooks 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> <a href="https://www.kaggle.com/ultralytics/yolov3"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
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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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- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart)
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- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov3"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker" alt="Docker Pulls"></a>
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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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'https://youtu.be/NUsoVlDFqZg' # YouTube video
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'rtsp://example.com/media.mp4' # RTSP, RTMP, 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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```
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<img width="500" src="https://user-images.githubusercontent.com/26833433/100375993-06b37900-300f-11eb-8d2d-5fc7b22fbfbd.jpg">
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### PyTorch Hub
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To run **batched inference** with YOLOv5 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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# Model
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model = torch.hub.load('ultralytics/yolov3', 'yolov3') # or 'yolov3_spp', 'yolov3_tiny'
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# Image
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img = 'https://ultralytics.com/images/zidane.jpg'
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# Inference
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results = model(img)
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results.print() # or .show(), .save()
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```
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## Training
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Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov3/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). 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 width="800" src="https://user-images.githubusercontent.com/26833433/100378028-af170c80-3012-11eb-8521-f0d2a8d021bc.png">
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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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