# 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 all `pip install -U -r requirements.txt` packages including `torch >= 1.5`. Docker images come with all dependencies preinstalled. Docker requirements are:
- Nvidia Driver >= 440.44
- Docker Engine - CE >= 19.03
# Tutorials
* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) < highly recommended!!
* [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class)
* [Google Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb) with quick training, inference and testing examples
* [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
* [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
* [A TensorRT Implementation of YOLOv3-SPP](https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3-spp)
# Training
**Start Training:** `python3 train.py` to begin training after downloading COCO data with `data/get_coco2017.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()`
## Image Augmentation
`datasets.py` applies OpenCV-powered (https://opencv.org/) augmentation to the input image. We use a **mosaic dataloader** (pictured below) to increase image variability during training.
## Speed
https://cloud.google.com/deep-learning-vm/
**Machine type:** preemptible [n1-standard-16](https://cloud.google.com/compute/docs/machine-types) (16 vCPUs, 60 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:** 1 TB SSD
**Dataset:** COCO train 2014 (117,263 images)
**Model:** `yolov3-spp.cfg`
**Command:** `python3 train.py --img 416 --batch 32 --accum 2`
GPU |n| `--batch --accum` | img/s | epoch
time | epoch
cost
--- |--- |--- |--- |--- |---
K80 |1| 32 x 2 | 11 | 175 min | $0.58
T4 |1
2| 32 x 2
64 x 1 | 41
61 | 48 min
32 min | $0.28
$0.36
V100 |1
2| 32 x 2
64 x 1 | 122
**178** | 16 min
**11 min** | **$0.23**
$0.31
2080Ti |1
2| 32 x 2
64 x 1 | 81
140 | 24 min
14 min | -
-
# Inference
```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`
**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.pt`
**YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.pt`
**YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.pt`
# 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
|Size |COCO mAP
@0.5...0.95 |COCO mAP
@0.5
--- | --- | --- | ---
YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |320 |14.0
28.7
30.5
**37.7** |29.1
51.8
52.3
**56.8**
YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |416 |16.0
31.2
33.9
**41.2** |33.0
55.4
56.9
**60.6**
YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |512 |16.6
32.7
35.6
**42.6** |34.9
57.7
59.5
**62.4**
YOLOv3-tiny
YOLOv3
YOLOv3-SPP
**[YOLOv3-SPP-ultralytics](https://drive.google.com/open?id=1UcR-zVoMs7DH5dj3N1bswkiQTA4dmKF4)** |608 |16.6
33.1
37.0
**43.1** |35.4
58.2
60.7
**62.8**
- mAP@0.5 run at `--iou-thr 0.5`, mAP@0.5...0.95 run at `--iou-thr 0.7`
- Darknet results: https://arxiv.org/abs/1804.02767
```bash
$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 640 --augment
Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='coco2014.data', device='', img_size=640, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weight
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)
Class Images Targets P R mAP@0.5 F1: 100%|█████████| 313/313 [03:00<00:00, 1.74it/s]
all 5e+03 3.51e+04 0.375 0.743 0.64 0.492
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.647
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.496
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.361
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.666
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.492
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810
Speed: 17.5/2.3/19.9 ms inference/NMS/total per 640x640 image at batch-size 16
```
# Reproduce Our Results
This command trains `yolov3-spp.cfg` from scratch to our mAP above. Training takes about one week on a 2080Ti.
```bash
$ python3 train.py --weights '' --cfg yolov3-spp.cfg --epochs 300 --batch 16 --accum 4 --multi
```
# Reproduce Our Environment
To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a:
- **GCP** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
- **Google Colab Notebook** with 12 hours of free GPU time: [Google Colab Notebook](https://colab.sandbox.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb)
- **Docker Image** from https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart)
# 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.
