- Pydocs
- Getting Started Guide
- Choose a demonstration dataset for images
- Choose a demonstration dataset for text
- Automatic dataset balancing
- Stratified validation splits
- Collect and prepare more datasets
- Allow for manifest files instead of directories
- Create a benchmarking script
- Enable fine-tuning for faster training (Just simply loading pretrained weights will not fit for downstream task with different labels)
- (Optional) If datasets other than text and image are chosen, an appropriate model for this task should be defined and implemented for training.
- Set up cloud storage to upload pre-trained weights and enable
download
command - Define API for use as a library
- Upgrade ResNet architecture to something more modern
- Upgrade RoBERTa architecture to something more modern
- Pre-train models at different sizes
- Pre-train models on new datasets
- Add tools to export to other formats (e.g. ONNX)
- Build a live demo
- Announce on social media