加载中…
加载中…
checkpoint-1180/optimizer.pt 687 MBcheckpoint-15740/optimizer.pt 687 MBcheckpoint-3148/optimizer.pt 687 MBcheckpoint-5388/optimizer.pt 687 MBcheckpoint-15740/pytorch_model.bin 343 MBcheckpoint-5388/pytorch_model.bin 343 MBpytorch_model.bin 343 MBcheckpoint-1180/model.safetensors 343 MBcheckpoint-15740/model.safetensors 343 MBcheckpoint-3148/model.safetensors 343 MBmodel.safetensors 343 MBcheckpoint-1180/rng_state.pth 14.6 kBcheckpoint-15740/rng_state.pth 14.6 kBcheckpoint-3148/rng_state.pth 14.6 kBcheckpoint-5388/rng_state.pth 14.6 kBcheckpoint-1180/training_args.bin 4.4 kBcheckpoint-15740/training_args.bin 4.4 kBcheckpoint-3148/training_args.bin 4.4 kBtraining_args.bin 4.4 kBcheckpoint-5388/training_args.bin 4 kBcheckpoint-1180/scheduler.pt 0.6 kBcheckpoint-15740/scheduler.pt 0.6 kBcheckpoint-3148/scheduler.pt 0.6 kBcheckpoint-5388/scheduler.pt 0.6 kBReturns facial emotion with about 91% accuracy based on facial human image.
See https://www.kaggle.com/code/dima806/facial-emotions-image-detection-vit for more details.
Classification report:
precision recall f1-score support
sad 0.8394 0.8632 0.8511 3596
disgust 0.9909 1.0000 0.9954 3596
angry 0.9022 0.9035 0.9028 3595
neutral 0.8752 0.8626 0.8689 3595
fear 0.8788 0.8532 0.8658 3596
surprise 0.9476 0.9449 0.9463 3596
happy 0.9302 0.9372 0.9336 3596
accuracy 0.9092 25170
macro avg 0.9092 0.9092 0.9091 25170
weighted avg 0.9092 0.9092 0.9091 25170