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Glenn Jocher
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README.md
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README.md
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# Introduction
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This directory contains PyTorch YOLOv3 software developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information please visit https://www.ultralytics.com.
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# Description
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## Introduction
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The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. Training is done on the COCO dataset by default: https://cocodataset.org/#home. **Credit to Joseph Redmon for YOLO:** https://pjreddie.com/darknet/yolo/.
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# Requirements
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Python 3.7 or later with all `pip install -U -r requirements.txt` packages including `torch >= 1.5`. Docker images come with all dependencies preinstalled. Docker requirements are:
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- Nvidia Driver >= 440.44
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- Docker Engine - CE >= 19.03
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## Requirements
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# Tutorials
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Python 3.7 or later with all `requirements.txt` dependencies installed, including `torch >= 1.5`. To install run:
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```bash
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$ pip install -U -r requirements.txt
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```
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## Tutorials
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* <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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* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) < highly recommended!!
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* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
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* [Google Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb) with quick training, inference and testing examples
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* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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* [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
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* [A TensorRT Implementation of YOLOv3 and YOLOv4](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3-spp)
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# Training
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## Training
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**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco2017.sh`. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set.
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@ -49,13 +49,15 @@ Python 3.7 or later with all `pip install -U -r requirements.txt` packages inclu
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<img src="https://user-images.githubusercontent.com/26833433/78175826-599d4800-7410-11ea-87d4-f629071838f6.png" width="900">
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## Image Augmentation
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### Image Augmentation
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`datasets.py` applies OpenCV-powered (https://opencv.org/) augmentation to the input image. We use a **mosaic dataloader** to increase image variability during training.
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<img src="https://user-images.githubusercontent.com/26833433/80769557-6e015d00-8b02-11ea-9c4b-69310eb2b962.jpg" width="900">
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## Speed
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### Speed
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https://cloud.google.com/deep-learning-vm/
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**Machine type:** preemptible [n1-standard-8](https://cloud.google.com/compute/docs/machine-types) (8 vCPUs, 30 GB memory)
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@ -73,6 +75,7 @@ T4 |1<br>2| 32 x 2<br>64 x 1 | 41<br>61 | 48 min<br>32 min | $0.09<br>$0.11
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V100 |1<br>2| 32 x 2<br>64 x 1 | 122<br>**178** | 16 min<br>**11 min** | **$0.21**<br>$0.28
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2080Ti |1<br>2| 32 x 2<br>64 x 1 | 81<br>140 | 24 min<br>14 min | -<br>-
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# Inference
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```bash
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@ -96,10 +99,11 @@ python3 detect.py --source ...
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<img src="https://user-images.githubusercontent.com/26833433/64067833-51d5b500-cc2f-11e9-8208-6fe197809131.jpg" width="500">
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# Pretrained Weights
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## Pretrained Checkpoints
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Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0)
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## Darknet Conversion
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```bash
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@ -114,7 +118,8 @@ $ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolo
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Success: converted 'weights/yolov3-spp.pt' to 'weights/yolov3-spp.weights'
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```
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# mAP
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## mAP
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<i></i> |Size |COCO mAP<br>@0.5...0.95 |COCO mAP<br>@0.5
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--- | --- | --- | ---
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@ -153,7 +158,7 @@ Speed: 17.5/2.3/19.9 ms inference/NMS/total per 640x640 image at batch-size 16
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<!-- Speed: 11.4/2.2/13.6 ms inference/NMS/total per 608x608 image at batch-size 1 -->
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# Reproduce Our Results
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## Reproduce Our Results
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Run commands below. Training takes about one week on a 2080Ti per model.
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```bash
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@ -162,17 +167,31 @@ $ python train.py --data coco2014.data --weights '' --batch-size 32 --cfg yolov3
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```
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<img src="https://user-images.githubusercontent.com/26833433/80831822-57a9de80-8ba0-11ea-9684-c47afb0432dc.png" width="900">
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# Reproduce Our Environment
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## Reproduce Our Environment
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To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a:
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- **GCP** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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- **Google Colab Notebook** with 12 hours of free GPU time: [Google Colab Notebook](https://colab.sandbox.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb)
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- **Docker Image** from https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
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- **Google Colab Notebook** with 12 hours of free GPU time. <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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- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
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# Citation
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[](https://zenodo.org/badge/latestdoi/146165888)
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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 us at https://contact.ultralytics.com.
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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** surveillance 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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