This project fine tunes a pretrained ResNet-152 model for Car Model Recognition.
Code snippets for Car Detection with YOLOv3 has been adopted from this project (https://github.com/eriklindernoren/PyTorch-YOLOv3.git)
Download data and model weights
- Download necessary files
bash ./download.sh
- Download
best.tar, the model trained for the project from Google Drive and put under./model/directory
https://drive.google.com/open?id=1B4NZOXJS2lMjYjloPmY-JGIrTBL5p6Dx
Prepare training data
- Preprocess the data
- crop the car images defined by the bouding boxes
- split them into training/validation datasets with the ratio of 8:2.
python ./load_data.py
To Train
- Train with ResNet-152 as base model.
python ./train.py
To Test
- Test accuracy on validation dataset
python ./train.py --validation
To Predict
- Run prediction on a single image (cropped car image)
python ./predict.py --mode predict --image_path <image_path>
- Run detection and prediction on a single image (not cropped) with visualization
python ./predict.py --mode predict_and_detect --image_path <image_path>
TODO
- Replace ResNet-152 with EfficientNet (https://arxiv.org/abs/1905.11946) (implemented with PyTorch in the project, not trained yet)