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Remove hardcoded class count assertions to support multiple datasets - #3
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Co-authored-by: zhulinchng <24189730+zhulinchng@users.noreply.github.com>
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[WIP] Update formatting and simplification based on feedback
Remove hardcoded class count assertions to support multiple datasets
Jan 14, 2026
zhulinchng
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January 14, 2026 01:23
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Pull request overview
This PR removes hardcoded assertions that enforced exactly 10 classes in the training script, enabling support for datasets with different class counts like stanford-dogs (120 classes) in addition to imagenette/woof (10 classes).
Changes:
- Removed training dataset assertion requiring exactly 10 classes
- Removed validation dataset assertion requiring exactly 10 classes
- Updated comments to reflect the removal of validation logic
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The training script enforced exactly 10 classes via assertions, preventing use with stanford-dogs (120 classes) despite comments indicating multi-dataset support.
Changes
num_classes == 10assertion for training datasetlen(val_dataset.classes) == 10assertion for validation datasetThe script now accepts any number of classes determined from the dataset itself via
len(train_dataset.classes), supporting imagenette/woof (10 classes) and stanford-dogs (120 classes).✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.