# Introduction
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.
# 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 >= 1.1.0`
- `opencv-python`
- `tqdm`
# Tutorials
* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
* [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)
# Jupyter Notebook
Our Jupyter [notebook](https://colab.research.google.com/github/ultralytics/yolov3/blob/master/examples.ipynb) provides quick training, inference and testing examples.
# Training
**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.
**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.
**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.
## 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%
## Speed
https://cloud.google.com/deep-learning-vm/
**Machine type:** n1-standard-8 (8 vCPUs, 30 GB memory)
**CPU platform:** Intel Skylake
**GPUs:** K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.83/hr) CUDA with [Nvidia Apex](https://github.com/NVIDIA/apex) FP16/32
**HDD:** 100 GB SSD
**Dataset:** COCO train 2014 (117,263 images)
GPUs | `batch_size` | images/sec | epoch time | epoch cost
--- |---| --- | --- | ---
K80 | 64 (32x2) | 11 | 175 min | $0.58
T4 | 64 (32x2) | 40 | 49 min | $0.29
T4 x2 | 64 (64x1) | 61 | 32 min | $0.36
V100 | 64 (32x2) | 115 | 17 min | $0.24
V100 x2 | 64 (64x1) | 150 | 13 min | $0.36
2080Ti | 64 (32x2) | 81 | 24 min | -
2080Ti x2 | 64 (64x1) | 140 | 14 min | -
# Inference
`detect.py` runs inference on any sources:
```bash
python3 detect.py --source ...
```
- Image: `--source file.jpg`
- Video: `--source file.mp4`
- Directory: `--source dir/`
- Webcam: `--source 0`
- RTSP stream: `--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa`
- HTTP stream: `--source http://wmccpinetop.axiscam.net/mjpg/video.mjpg`
To run a specific models:
**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights`
**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights`
**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights`
# Pretrained Weights
Download from: [https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0](https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0)
## Darknet Conversion
```bash
$ git clone https://github.com/ultralytics/yolov3 && cd yolov3
# convert darknet cfg/weights to pytorch model
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
Success: converted 'weights/yolov3-spp.weights' to 'converted.pt'
# convert cfg/pytorch model to darknet weights
$ python3 -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
```
# mAP
- `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights.
- `test.py --weights weights/last.pt` tests latest checkpoint.
- Compare to darknet published results https://arxiv.org/abs/1804.02767.
[ultralytics/yolov3](https://github.com/ultralytics/yolov3) mAP@0.5 vs. [darknet](https://arxiv.org/abs/1804.02767)-reported mAP@0.5
| 320 | 416 | 608
--- | --- | --- | ---
darknet `YOLOv3-tiny` | 29.0 | 33.1 | 35.5
darknet `YOLOv3` | 51.5 | 55.3 | 57.9
darknet `YOLOv3-SPP` | 52.3 | 56.8 | **60.6**
ultralytics `YOLOv3-SPP` | **53.9** | **58.7** | 60.1
```bash
$ python3 test.py --save-json --img-size 608 --weights ultralytics68.pt
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=0.5, save_json=True, weights='ultralytics68.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP@0.5 F1: 100% 313/313 [06:52<00:00, 1.24it/s]
all 5e+03 3.58e+04 0.107 0.779 0.59 0.182
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.398 <---
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.601 <---
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.425
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.505
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.325
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.519
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.543
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.366
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.584
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665
```
# Citation
[](https://zenodo.org/badge/latestdoi/146165888)
# Contact
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.
