199 lines
10 KiB
Markdown
Executable File
199 lines
10 KiB
Markdown
Executable File
<table style="width:100%">
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<tr>
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<td>
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<img src="https://user-images.githubusercontent.com/26833433/61591130-f7beea00-abc2-11e9-9dc0-d6abcf41d713.jpg">
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</td>
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<td align="center">
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<a href="https://www.ultralytics.com" target="_blank">
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<img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="160"></a>
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<img src="https://user-images.githubusercontent.com/26833433/61591093-2b4d4480-abc2-11e9-8b46-d88eb1dabba1.jpg">
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<a href="https://itunes.apple.com/app/id1452689527" target="_blank">
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<img src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180"></a>
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</td>
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<td>
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<img src="https://user-images.githubusercontent.com/26833433/61591100-55066b80-abc2-11e9-9647-52c0e045b288.jpg">
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</td>
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</tr>
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</table>
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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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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 of the `pip install -U -r requirements.txt` packages including:
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- `torch >= 1.3`
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- `opencv-python`
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- `Pillow`
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All dependencies are included in the associated docker images. Docker requirements are:
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- `nvidia-docker`
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- Nvidia Driver Version >= 440.44
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# Tutorials
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* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
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* [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning)
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* [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image)
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* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
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* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data)
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# Jupyter Notebook
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Our Jupyter [notebook](https://colab.research.google.com/github/ultralytics/yolov3/blob/master/examples.ipynb) provides quick training, inference and testing examples.
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# Training
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**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco_dataset.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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**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
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**Plot Training:** `from utils import utils; utils.plot_results()` plots training results from `coco_16img.data`, `coco_64img.data`, 2 example datasets available in the `data/` folder, which train and test on the first 16 and 64 images of the COCO2014-trainval dataset.
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<img src="https://user-images.githubusercontent.com/26833433/63258271-fe9d5300-c27b-11e9-9a15-95038daf4438.png" width="900">
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## Image Augmentation
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`datasets.py` applies random OpenCV-powered (https://opencv.org/) augmentation to the input images in accordance with the following specifications. Augmentation is applied **only** during training, not during inference. Bounding boxes are automatically tracked and updated with the images. 416 x 416 examples pictured below.
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Augmentation | Description
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--- | ---
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Translation | +/- 10% (vertical and horizontal)
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Rotation | +/- 5 degrees
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Shear | +/- 2 degrees (vertical and horizontal)
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Scale | +/- 10%
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Reflection | 50% probability (horizontal-only)
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H**S**V Saturation | +/- 50%
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HS**V** Intensity | +/- 50%
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<img src="https://user-images.githubusercontent.com/26833433/66699231-27beea80-ece5-11e9-9cad-bdf9d82c500a.jpg" width="900">
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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-16](https://cloud.google.com/compute/docs/machine-types) (16 vCPUs, 60 GB memory)
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**CPU platform:** Intel Skylake
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**GPUs:** K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.83/hr) CUDA with [Nvidia Apex](https://github.com/NVIDIA/apex) FP16/32
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**HDD:** 1 TB SSD
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**Dataset:** COCO train 2014 (117,263 images)
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**Model:** `yolov3-spp.cfg`
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**Command:** `python3 train.py --img 416 --batch 32 --accum 2`
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GPU |n| `--batch --accum` | img/s | epoch<br>time | epoch<br>cost
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--- |--- |--- |--- |--- |---
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K80 |1| 32 x 2 | 11 | 175 min | $0.58
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T4 |1<br>2| 32 x 2<br>64 x 1 | 41<br>61 | 48 min<br>32 min | $0.28<br>$0.36
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V100 |1<br>2| 32 x 2<br>64 x 1 | 122<br>**178** | 16 min<br>**11 min** | **$0.23**<br>$0.31
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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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`detect.py` runs inference on any sources:
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```bash
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python3 detect.py --source ...
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```
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- Image: `--source file.jpg`
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- Video: `--source file.mp4`
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- Directory: `--source dir/`
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- Webcam: `--source 0`
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- RTSP stream: `--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa`
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- HTTP stream: `--source http://wmccpinetop.axiscam.net/mjpg/video.mjpg`
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To run a specific models:
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**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.weights`
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<img src="https://user-images.githubusercontent.com/26833433/64067835-51d5b500-cc2f-11e9-982e-843f7f9a6ea2.jpg" width="500">
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**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.weights`
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<img src="https://user-images.githubusercontent.com/26833433/64067834-51d5b500-cc2f-11e9-9357-c485b159a20b.jpg" width="500">
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**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.weights`
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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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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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$ git clone https://github.com/ultralytics/yolov3 && cd yolov3
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# convert darknet cfg/weights to pytorch model
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$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
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Success: converted 'weights/yolov3-spp.weights' to 'converted.pt'
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# convert cfg/pytorch model to darknet weights
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$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
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Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
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```
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# mAP
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```bash
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python3 test.py --weights ... --cfg ...
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```
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- mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7`
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- YOLOv3-SPP ultralytics is `ultralytics68.pt` with `yolov3-spp.cfg`
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- Darknet results: https://arxiv.org/abs/1804.02767
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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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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |320 |14.0<br>28.7<br>30.5<br>**35.5** |29.1<br>51.8<br>52.3<br>**55.4**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |416 |16.0<br>31.2<br>33.9<br>**39.2** |33.0<br>55.4<br>56.9<br>**59.9**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |512 |16.6<br>32.7<br>35.6<br>**40.5** |34.9<br>57.7<br>59.5<br>**61.4**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |608 |16.6<br>33.1<br>37.0<br>**41.1** |35.4<br>58.2<br>60.7<br>**61.5**
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```bash
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$ python3 test.py --img-size 608 --iou-thr 0.6 --weights ultralytics68.pt --cfg yolov3-spp.cfg
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Namespace(batch_size=32, cfg='yolov3-spp.cfg', conf_thres=0.001, data='data/coco2014.data', device='', img_size=608, iou_thres=0.6, save_json=True, task='test', weights='ultralytics68.pt')
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Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
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Class Images Targets P R mAP@0.5 F1: 100% 157/157 [03:30<00:00, 1.16it/s]
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all 5e+03 3.51e+04 0.0353 0.891 0.606 0.0673
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.409
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.615
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.437
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.242
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.448
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.337
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.612
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.438
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.658
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746
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```
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# Reproduce Our Results
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This command trains `yolov3-spp.cfg` from scratch to our mAP above. Training takes about one week on a 2080Ti.
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```bash
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$ python3 train.py --weights '' --cfg yolov3-spp.cfg --epochs 273 --batch 16 --accum 4 --multi --pre
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```
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<img src="https://user-images.githubusercontent.com/26833433/70661588-76bbca00-1c19-11ea-86f9-23350d8c3193.png" width="900">
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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.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw)
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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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# 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 additional questions or comments please email Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.
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