150 lines
7.6 KiB
Markdown
Executable File
150 lines
7.6 KiB
Markdown
Executable File
<table style="width:100%">
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<tr>
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<th>v2.2<img src="https://user-images.githubusercontent.com/26833433/52743528-e6096300-2fe2-11e9-970c-5fee45769fab.jpg" width="400"></th>
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<th>v3.0<img src="https://user-images.githubusercontent.com/26833433/54523854-227d0580-4979-11e9-9801-26a3be239875.jpg" width="400"></th>
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<th><img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="200">
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<br><br/>
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<p> <a href="https://itunes.apple.com/app/id1452689527">
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<img href="https://itunes.apple.com/app/id1452689527" src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180">
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</a> </p></th>
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</tr>
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</table>
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# Introduction
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This directory contains python software and an iOS App 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/) and to **Erik Lindernoren for the PyTorch implementation** this work is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3).
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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 >= 1.0.0`
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- `opencv-python`
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# Tutorials
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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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# Training
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**Start Training:** Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
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**Resume Training:** Run `train.py --resume` resumes training from the latest checkpoint `weights/latest.pt`.
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Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. Default training settings produce loss plots below, with **training speed of 0.6 s/batch on a 1080 Ti (18 epochs/day)** or 0.45 s/batch on a 2080 Ti.
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`from utils import utils; utils.plot_results()`
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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/26833433/50525037-6cbcbc00-0ad9-11e9-8c38-9fd51af530e0.jpg">
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## Speed
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https://cloud.google.com/deep-learning-vm/
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**Machine type:** n1-highmem-4 (4 vCPUs, 26 GB memory)
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**CPU platform:** Intel Skylake
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**GPUs:** 1-4 x NVIDIA Tesla P100
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**HDD:** 100 GB SSD
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GPUs | `batch_size` | speed | COCO epoch
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--- |---| --- | ---
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(P100) | (images) | (s/batch) | (min/epoch)
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1 | 16 | 0.54s | 66min
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2 | 32 | 0.99s | 61min
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4 | 64 | 1.61s | 49min
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# Inference
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Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder:
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**YOLOv3:** `detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.pt`
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<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="700">
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**YOLOv3-tiny:** `detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.pt`
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<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="700">
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## Webcam
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Run `detect.py` with `webcam=True` to show a live webcam feed.
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# Pretrained Weights
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- Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights
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- PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
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# mAP
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- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
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- Use `test.py --weights weights/latest.pt` to test the latest training results.
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- Compare to official darknet results from https://arxiv.org/abs/1804.02767.
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<i></i> | ultralytics/yolov3 | darknet
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--- | ---| ---
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YOLOv3-320 | 51.3 | 51.5
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YOLOv3-416 | 54.9 | 55.3
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YOLOv3-608 | 57.9 | 57.9
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``` bash
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sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
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# bash yolov3/data/get_coco_dataset.sh
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sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
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cd yolov3
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python3 test.py --save-json --conf-thres 0.001 --img-size 416
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Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.310
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.267
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585
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python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
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Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights')
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.579
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.335
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.429
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572
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```
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# Contact
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For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.
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