A quite minimal implementation of YOLOv3 in PyTorch spanning only around 800 lines of code (not including plot functions etc.) with support for training and evaluation and complete with helper functions for inference. There is currently pretrained weights for Pascal-VOC with MS COCO coming up. With minimal changes in the model with regards to the output format the original weights can also be loaded seamlessly.
$ git clone https://github.com/SannaPersson/YOLOv3-PyTorch.git
$ cd YOLOv3-PyTorch
$ pip install requirements.txtPretrained weights for Pascal-VOC can be downloaded from this page: https://www.kaggle.com/sannapersson/yolov3-weights-for-pascal-voc-with-781-map
Download YOLOv3 weights from https://pjreddie.com/media/files/yolov3.weights. Save the weights to PyTorch format by running the model_with_weights.py file. Change line in train.py to import model_with_weights.py instead of model.py since the original output format is slightly different.
Download the preprocessed dataset from link. Just unzip this in the main directory. The file structure of the dataset is a folder with images, a folder with corresponding text files containing the bounding boxes and class targets for each image and two csv-files containing the subsets of the data used for training and testing.
Edit the config.py file to match the setup you want to use. Then run train.py
| Model | mAP @ 50 IoU |
|---|---|
| YOLOv3 (Pascal VOC) | 78.2 |
| YOLOv3 (MS-COCO) | Not done yet |
The model was evaluated with confidence 0.2 and IOU threshold 0.45 using NMS.
The implementation is based on the following paper:
by Joseph Redmon, Ali Farhadi
We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that’s pretty swell. It’s a little bigger than last time but more accurate. It’s still fast though, don’t worry. At 320 × 320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 AP50 in 51 ms on a Titan X, compared to 57.5 AP50 in 198 ms by RetinaNet, similar performance but 3.8× faster. As always, all the code is online at https://pjreddie.com/yolo/.
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}