# yolov3

**Repository Path**: woodsgao/yolov3

## Basic Information

- **Project Name**: yolov3
- **Description**: YOLOv3 in PyTorch > ONNX > CoreML > iOS
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-08-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<table style="width:100%">
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      <img src="https://user-images.githubusercontent.com/26833433/61591130-f7beea00-abc2-11e9-9dc0-d6abcf41d713.jpg">
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    <td align="center">
    <a href="https://www.ultralytics.com" target="_blank">
    <img src="https://storage.googleapis.com/ultralytics/logo/logoname1000.png" width="160"></a>
      <img src="https://user-images.githubusercontent.com/26833433/61591093-2b4d4480-abc2-11e9-8b46-d88eb1dabba1.jpg">
          <a href="https://itunes.apple.com/app/id1452689527" target="_blank">
    <img src="https://user-images.githubusercontent.com/26833433/50044365-9b22ac00-0082-11e9-862f-e77aee7aa7b0.png" width="180"></a>
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      <img src="https://user-images.githubusercontent.com/26833433/61591100-55066b80-abc2-11e9-9647-52c0e045b288.jpg">
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# Introduction

This directory contains PyTorch YOLOv3 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 >= 1.1.0`
- `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`.

**Resume Training:** `python3 train.py --resume` to resume training from `weights/last.pt`.

Each epoch trains on 117,263 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 0.25 s/batch on a V100 GPU (almost 50 COCO epochs/day)**.

Here we see training results from `coco_1img.data`, `coco_10img.data` and `coco_100img.data`, 3 example files available in the `data/` folder, which train and test on the first 1, 10 and 100 images of the coco2014 trainval dataset.

`from utils import utils; utils.plot_results()`
![results](https://user-images.githubusercontent.com/26833433/56207787-ec9e7000-604f-11e9-94dd-e1fcc374270f.png)

## 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/26833433/61579359-507b7d80-ab04-11e9-8a2a-bd6f59bbdfb4.jpg">

## 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 ($0.80/hr) CUDA with Nvidia Apex FP16/32 
**HDD:** 100 GB SSD  
**Dataset:** COCO train 2014 (117,263 images)

GPUs | `batch_size` | batch time | epoch time | epoch cost
--- |---| --- | --- | --- 
1 K80 | 64 (32x2) | 2.9s  | 175min  | $0.58
1 T4 | 64 (32x2) | 0.80s  | 49min  | $0.29
2 T4 | 64 (64x1) | 0.52s  | 32min  | $0.36
1 2080ti | 64 (32x2) | -  | -  | -
1 V100 | 64 (32x2) | 0.38s | 23min | $0.31
2 V100 | 64 (64x1) | 0.30s | 18min | $0.46

# Inference

`detect.py` runs inference on all images **and videos** in 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

`detect.py` with `webcam=True` shows 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

## 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 most recent checkpoint.
- `test.py --weights weights/best.pt` tests best checkpoint.
- Compare to darknet published results https://arxiv.org/abs/1804.02767.

<!---
%<i></i> | ultralytics/yolov3 OR-NMS 5:52@416 (`pycocotools`) | darknet  
--- | --- | ---  
YOLOv3-320 | 51.9 (51.4) | 51.5  
YOLOv3-416 | 55.0 (54.9) | 55.3  
YOLOv3-608 | 57.5 (57.8) | 57.9  

<i></i> | ultralytics/yolov3 MERGE-NMS 7:15@416 (`pycocotools`) | darknet  
--- | --- | ---  
YOLOv3-320 | 52.3 (51.7) | 51.5  
YOLOv3-416 | 55.4 (55.3) | 55.3  
YOLOv3-608 | 57.9 (58.1) | 57.9  

<i></i> | ultralytics/yolov3 MERGE+earlier_pred4 8:34@416 (`pycocotools`) | darknet  
--- | --- | ---  
YOLOv3-320 | 52.3 (51.8) | 51.5  
YOLOv3-416 | 55.5 (55.4) | 55.3  
YOLOv3-608 | 57.9 (58.2) | 57.9  
--->
<i></i> | [ultralytics/yolov3](https://github.com/ultralytics/yolov3) | [darknet](https://arxiv.org/abs/1804.02767) 
--- | --- | ---  
`YOLOv3 320` | 51.8 | 51.5  
`YOLOv3 416` | 55.4 | 55.3  
`YOLOv3 608` | 58.2 | 57.9  
`YOLOv3-spp 320` | 52.4 | -  
`YOLOv3-spp 416` | 56.5 | -  
`YOLOv3-spp 608` | 60.7 | 60.6  

``` bash
# install pycocotools
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 --img-size 608
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
                Class    Images   Targets         P         R       mAP        F1: 100% 313/313 [07:40<00:00,  2.34s/it]
                all       5e+03  3.58e+04     0.117     0.788     0.595     0.199
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.367
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.607 <--
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.387
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.297
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.465
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.495
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621

python3 test.py --save-json --img-size 416
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
                Class    Images   Targets         P         R       mAP        F1: 100% 313/313 [07:01<00:00,  1.41s/it]
                all       5e+03  3.58e+04     0.105     0.746     0.554      0.18
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.336
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.565 <--
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.350
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.361
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.281
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.433
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.459
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622
```

# Citation

[![DOI](https://zenodo.org/badge/146165888.svg)](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.