car-detection-bayes/README.md

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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/) and to **Erik Lindernoren for the PyTorch implementation** this work is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3).

# Requirements

Python 3.7 or later with the following `pip3 install -U -r requirements.txt` packages:

- `numpy`
- `torch >= 1.0.0`
- `opencv-python`

# 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()`
![Alt](https://user-images.githubusercontent.com/26833433/53494085-3251aa00-3a9d-11e9-8af7-8c08cf40d70b.png "train.py 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-highmem-4 (4 vCPUs, 26 GB memory)  
**CPU platform:** Intel Skylake  
**GPUs:** 1-4 x NVIDIA Tesla P100  
**HDD:** 100 GB SSD  

GPUs | `batch_size` | speed | COCO epoch
--- |---| --- | --- 
(P100)   |  (images)  | (s/batch) | (min/epoch)
1 | 16 | 0.54s  | 66min
2 | 32 | 0.99s | 61min
4 | 64 | 1.61s | 49min

# Inference

Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder:

**YOLOv3:** `detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.pt`
<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="700">

**YOLOv3-tiny:** `detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.pt`
<img src="https://user-images.githubusercontent.com/26833433/50374155-21427380-05ea-11e9-8d24-f1a4b2bac1ad.jpg" width="700">

## 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.