machine-learning-deep-learning/Machine-Learning-Collection
1
0
Fork 0
mirror of https://github.com/aladdinpersson/Machine-Learning-Collection.git synced 2026-02-20 13:50:41 +00:00
Code Issues Packages Projects Releases Wiki Activity

updated yolov3 readme

Browse Source
This commit is contained in:
Aladdin Persson
2021-02-22 17:11:52 +01:00
parent 4a6627a3bf
commit 884fb9b569
1 changed files with 11 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
ML/Pytorch/object_detection/YOLOv3/README.md
View File

@@ -1,26 +1,27 @@
# YOLOv3 in PyTorch
A quite minimal implementation of YOLOv3 in PyTorch spanning only around 600 lines of code 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.
A quite minimal implementation of YOLOv3 in PyTorch spanning only around 800 lines of code related to YOLOv3 (not counting plot image helper functions etc). The repository has 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.
## Installation
### Clone and install requirements
```bash
$ git clone
$ cd YOLOv3-PyTorch
$ git clone https://github.com/aladdinpersson/Machine-Learning-Collection
$ cd ML/Pytorch/object_detection/YOLOv3/
$ pip install requirements.txt
```
### Download pretrained weights on Pascal-VOC
Available on Kaggle: https://www.kaggle.com
### Dowload original weights
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. This works well for
Available on Kaggle: coming soon
### Download Pascal-VOC dataset
Download the processed dataset from the following link: coming soon
Download the preprocessed dataset from the following link: www.kaggle.com/aladdinpersson/pascalvoc-yolo-works-with-albumentations
Just unzip this in the main directory.
### Download MS-COCO dataset
Download the preprocessed dataset from the following link: www.kaggle.com/aladdinpersson/mscoco-yolo-works-with-albumentations
Just unzip this in the main directory.
### Training
Edit the config file to match the setup you want to use. Then run train.py
Edit the config.py file to match the setup you want to use. Then run train.py
### Results
| Model | mAP @ 50 IoU |
@@ -30,7 +31,6 @@ Edit the config file to match the setup you want to use. Then run train.py
The model was evaluated with confidence 0.2 and IOU threshold 0.45 using NMS.
## YOLOv3 paper
The implementation is based on the following paper: