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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>
<th>v3.0<img src="https://user-images.githubusercontent.com/26833433/54523854-227d0580-4979-11e9-9801-26a3be239875.jpg" width="400"></th>
<th><img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="200">
<br><br/>
<p> <a href="https://itunes.apple.com/app/id1452689527">
<img href="https://itunes.apple.com/app/id1452689527" src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180">
</a> </p></th>
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# Introduction
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.
# Description
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/.
# Requirements
Python 3.7 or later with the following `pip3 install -U -r requirements.txt` packages:
- `numpy`
- `torch >= 1.0.0`
- `opencv-python`
- `tqdm`
# Tutorials
* [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning)
* [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image)
* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data)
# Training
**Start Training:** Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`.
**Resume Training:** Run `train.py --resume` resumes training from the latest checkpoint `weights/latest.pt`.
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.
`from utils import utils; utils.plot_results()`
## Image Augmentation
`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.
Augmentation | Description
--- | ---
Translation | +/- 10% (vertical and horizontal)
Rotation | +/- 5 degrees
Shear | +/- 2 degrees (vertical and horizontal)
Scale | +/- 10%
Reflection | 50% probability (horizontal-only)
H**S**V Saturation | +/- 50%
HS**V** Intensity | +/- 50%
<img src="https://user-images.githubusercontent.com/26833433/50525037-6cbcbc00-0ad9-11e9-8c38-9fd51af530e0.jpg">
## Speed
https://cloud.google.com/deep-learning-vm/
**Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)
**CPU platform:** Intel Skylake
**GPUs:** 1-4x P100 ($0.493/hr), 1-8x V100 ($0.803/hr)
**HDD:** 100 GB SSD
**Dataset:** COCO train 2014
GPUs | `batch_size` | batch time | epoch time | epoch cost
--- |---| --- | --- | ---
<i></i> | (images) | (s/batch) | |
1 P100 | 16 | 0.39s | 48min | $0.39
2 P100 | 32 | 0.48s | 29min | $0.47
4 P100 | 64 | 0.65s | 20min | $0.65
1 V100 | 16 | 0.25s | 31min | $0.41
2 V100 | 32 | 0.29s | 18min | $0.48
4 V100 | 64 | 0.41s | 13min | $0.70
8 V100 | 128 | 0.49s | 7min | $0.80
# Inference
Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder:
**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights`
<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="600">
**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights`
<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="600">
**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights`
<img src="https://user-images.githubusercontent.com/26833433/54747926-e051ff00-4bd8-11e9-8b5d-93a41d871ec7.jpg" width="600">
## Webcam
Run `detect.py` with `webcam=True` to show a live webcam feed.
# Pretrained Weights
- Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights
- PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI
# mAP
- Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights.
- Use `test.py --weights weights/latest.pt` to test the latest training results.
- Compare to official darknet results from https://arxiv.org/abs/1804.02767.
<i></i> | ultralytics/yolov3 | darknet
--- | ---| ---
YOLOv3-320 | 51.3 | 51.5
YOLOv3-416 | 54.9 | 55.3
YOLOv3-608 | 57.9 | 57.9
``` bash
sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3
# bash yolov3/data/get_coco_dataset.sh
sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3
python3 test.py --save-json --conf-thres 0.001 --img-size 416
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')
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.310
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.267
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585
python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16
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')
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.579
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.335
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.429
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572
```
# Contact
For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.