car-detection-bayes/README.md

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# Introduction

This directory contains software developed by Ultralytics LLC. For more information on Ultralytics projects please visit:
http://www.ultralytics.com  

# Description

The https://github.com/ultralytics/yolov3 repo contains code to train YOLOv3 on the COCO dataset: https://cocodataset.org/#home. **Credit to P.J. Reddie 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 3.6 or later with the following `pip3 install -U -r requirements.txt` packages:

- `numpy`
- `torch`
- `opencv-python`

# Training

Run `train.py` to begin training. Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the validation set. An Nvidia GTX 1080 Ti will run about 16 epochs per day. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here (coming soon)
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "training loss")

# Inference

Checkpoints will be saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here.
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "example")

# Testing

Run `test.py` to test the latest checkpoint on the 5000 validation images. Joseph Redmon's official YOLOv3 weights produce a mAP of .581 using this method, compared to .579 in his paper.

# Contact

For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at http://www.ultralytics.com/contact