This repository is dedicated to training models using offline datasets from the CARLA Simulator. It is designed to support various deep learning tasks including VAE, ResNet, and LSTM models on BEV (Bird's Eye View) and FPV (First Person View) images.
- VAE on BEV Images: Training Variational Autoencoders (VAE) on BEV images with a reconstruction loss.
- ResNet Finetuning on FPVBEV Dataset: Finetuning ResNet on the FPVBEV dataset using a contrastive loss.
- LSTM on Time-Series BEV-Action Dataset: Training Long Short-Term Memory (LSTM) on time-series BEV-Action dataset.
- Dataset collected yourself or download from here.
- Python 3.x
- Necessary Python libraries (listed in
requirements.txt)
Clone this repository.
Before training, you need to configure the dataset path:
- Modify the 'root_dir' in 'initial.py' to point to the location of your dataset.
- Use the '--expname DIRECTORY_NAME' argument to specify the dataset's directory. Your dataset should be structured as follows:
- 'fpv.npy': FPV image dataset.
- 'bev.npy': BEV image dataset.
- 'action.npy': Action value dataset.
- 'terminal.npy': A binary array file indicating the terminal moment for each episode.
To train a model, run the 'train.py' script. Use the '-h' option to see all available training options. Use the '--model' option to select the model you want to train:
- FPV_BEV_CARLA: Finetuning ResNet model.
- BEV_VAE_CARLA: Training the VAE model.
- BEV_LSTM_CARLA: Training the LSTM model.