Related Issues (20)
- Rewrite the custom dataset to use pycocotools HOT 1
- Port code to a single notebook for training on Kaggle HOT 2
- Figure out optimal memory usage on Kaggle GPUs
- Set up evaluation for Kaggle HOT 1
- Set up model checkpointing
- Implement self-supervised learning
- Reflect on the results of the competition
- Run smaller experiements HOT 1
- Use automatic mixed precision (AMP)
- Try a pre-trained UNet model (if they exist...)
- Read up on results from competition HOT 1
- Try open source packages for segmentation
- Predict using an ensemble of models
- Try a different single model HOT 1
- Follow UNet paper for training specifics HOT 1
- Separate Training, Validation, and Testing data into different datasets HOT 2
- Release 3 - Changes from UNet paper
- Investigate issue with mAP metric HOT 1
- Verify training script functionality HOT 2
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