加载中…
加载中…
checkpoint-1326/optimizer.pt 687 MBcheckpoint-28722/optimizer.pt 687 MBcheckpoint-33509/optimizer.pt 687 MBcheckpoint-9945/optimizer.pt 687 MBpytorch_model.bin 343 MBcheckpoint-1326/model.safetensors 343 MBcheckpoint-28722/model.safetensors 343 MBcheckpoint-33509/model.safetensors 343 MBcheckpoint-9945/model.safetensors 343 MBmodel.safetensors 343 MBcheckpoint-1326/rng_state.pth 14.6 kBcheckpoint-28722/rng_state.pth 14.6 kBcheckpoint-33509/rng_state.pth 14.6 kBcheckpoint-9945/rng_state.pth 14.6 kBcheckpoint-28722/training_args.bin 4.7 kBcheckpoint-33509/training_args.bin 4.7 kBcheckpoint-9945/training_args.bin 4.7 kBtraining_args.bin 4.7 kBcheckpoint-1326/training_args.bin 4.4 kBcheckpoint-1326/scheduler.pt 0.6 kBcheckpoint-28722/scheduler.pt 0.6 kBcheckpoint-33509/scheduler.pt 0.6 kBcheckpoint-9945/scheduler.pt 0.6 kBReturns with about 98.7% accuracy whether the face belongs to man or woman based on face image.
See https://www.kaggle.com/code/dima806/man-woman-face-image-detection-vit for more details.
Classification report:
precision recall f1-score support
man 0.9885 0.9857 0.9871 51062
woman 0.9857 0.9885 0.9871 51062
accuracy 0.9871 102124
macro avg 0.9871 0.9871 0.9871 102124
weighted avg 0.9871 0.9871 0.9871 102124