181 lines
8.8 KiB
Markdown
Executable File
181 lines
8.8 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 the following `pip3 install -U -r requirements.txt` packages:
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- `numpy`
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- `torch >= 1.1.0`
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- `opencv-python`
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- `tqdm`
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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:** n1-standard-8 (8 vCPUs, 30 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:** 100 GB SSD
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**Dataset:** COCO train 2014 (117,263 images)
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GPUs | `batch_size` | images/sec | epoch time | epoch cost
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--- |---| --- | --- | ---
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K80 | 64 (32x2) | 11 | 175 min | $0.58
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T4 | 64 (32x2) | 40 | 49 min | $0.29
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T4 x2 | 64 (64x1) | 61 | 32 min | $0.36
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V100 | 64 (32x2) | 115 | 17 min | $0.24
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V100 x2 | 64 (64x1) | 150 | 13 min | $0.36
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2080Ti | 64 (32x2) | 81 | 24 min | -
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2080Ti x2 | 64 (64x1) | 140 | 14 min | -
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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 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 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 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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- `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights.
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- `test.py --weights weights/last.pt` tests latest checkpoint.
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- mAPs on COCO2014 using pycocotools.
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- mAP@0.5 run at `--nms-thres 0.5`, mAP@0.5...0.95 run at `--nms-thres 0.7`.
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- YOLOv3-SPP ultralytics is `ultralytics68.pt` with `yolov3-spp.cfg`.
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- Darknet results published in https://arxiv.org/abs/1804.02767.
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<i></i> |img-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>**35.4** |29.0<br>51.5<br>52.3<br>**54.3**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |416 |16.0<br>31.1<br>33.9<br>**39.0** |32.9<br>55.3<br>56.8<br>**59.2**
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YOLOv3-tiny<br>YOLOv3<br>YOLOv3-SPP<br>**YOLOv3-SPP ultralytics** |608 |16.6<br>33.0<br>37.0<br>**40.7** |35.5<br>57.9<br>60.6<br>**60.7**
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```bash
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Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='', img_size=608, iou_thres=0.5, nms_thres=0.7, save_json=True, weights='ultralytics68.pt')
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Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
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Downloading https://drive.google.com/uc?export=download&id=1Jm8kqnMdMGUUxGo8zMFZMJ0eaPwLkxSG as ultralytics68.pt... Done (2.2s)
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Class Images Targets P R mAP@0.5 F1: 100% 313/313 [16:23<00:00, 1.59s/it]
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all 5e+03 3.58e+04 0.0465 0.831 0.586 0.0868
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.407
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.598
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.444
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.245
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.446
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.511
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.326
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.537
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.595
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.427
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.641
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.706
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```
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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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