checkpoint-12105/optimizer.pt 687 MBcheckpoint-24210/optimizer.pt 687 MBcheckpoint-6147/optimizer.pt 687 MBcheckpoint-6147/pytorch_model.bin 343 MBpytorch_model.bin 343 MBcheckpoint-12105/model.safetensors 343 MBcheckpoint-24210/model.safetensors 343 MBmodel.safetensors 343 MBcheckpoint-12105/rng_state.pth 14.6 kBcheckpoint-24210/rng_state.pth 14.6 kBcheckpoint-6147/rng_state.pth 14.6 kBcheckpoint-12105/training_args.bin 4.7 kBcheckpoint-24210/training_args.bin 4.7 kBtraining_args.bin 4.7 kBcheckpoint-6147/training_args.bin 4 kBcheckpoint-12105/scheduler.pt 0.6 kBcheckpoint-24210/scheduler.pt 0.6 kBcheckpoint-6147/scheduler.pt 0.6 kBAchieved 78% weighted accuracy for classification between 3 common galaxy types (S, E, Sb).
See my Kaggle notebook for more details.
Classification report:
precision recall f1-score support
E 0.7656 0.8848 0.8209 13592
S 0.7526 0.6685 0.7081 13591
SB 0.8262 0.7900 0.8077 13591
accuracy 0.7811 40774
macro avg 0.7815 0.7811 0.7789 40774
weighted avg 0.7815 0.7811 0.7789 40774
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