timesformer-base-finetuned-k400-finetuned-snapdata_short_classification-sample_rate16

This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6607
• Accuracy: 0.6590
• 0 Precision: 0.7498
• 0 Recall: 0.3966
• 0 F1-score: 0.5188
• 0 Support: 2002.0
• 1 Precision: 0.6296
• 1 Recall: 0.8857
• 1 F1-score: 0.7360
• 1 Support: 2318.0
• Accuracy F1-score: 0.6590
• Macro avg Precision: 0.6897
• Macro avg Recall: 0.6411
• Macro avg F1-score: 0.6274
• Macro avg Support: 4320.0
• Weighted avg Precision: 0.6853
• Weighted avg Recall: 0.6590
• Weighted avg F1-score: 0.6353
• Weighted avg Support: 4320.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• training_steps: 62000
• label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	0 Precision	0 Recall	0 F1-score	0 Support	1 Precision	1 Recall	1 F1-score	1 Support	Accuracy F1-score	Macro avg Precision	Macro avg Recall	Macro avg F1-score	Macro avg Support	Weighted avg Precision	Weighted avg Recall	Weighted avg F1-score	Weighted avg Support
0.7164	0.0200	1241	0.6246	0.6813	0.6716	0.6109	0.6398	2002.0	0.6883	0.7420	0.7141	2318.0	0.6813	0.6799	0.6765	0.6770	4320.0	0.6806	0.6813	0.6797	4320.0
0.6897	1.0200	2482	0.6288	0.6803	0.6527	0.6628	0.6577	2002.0	0.7049	0.6954	0.7001	2318.0	0.6803	0.6788	0.6791	0.6789	4320.0	0.6807	0.6803	0.6805	4320.0
0.5827	2.0200	3723	0.6261	0.6676	0.6171	0.7448	0.6750	2002.0	0.7316	0.6009	0.6599	2318.0	0.6676	0.6744	0.6729	0.6674	4320.0	0.6786	0.6676	0.6669	4320.0
0.615	3.0200	4964	0.6607	0.6590	0.7498	0.3966	0.5188	2002.0	0.6296	0.8857	0.7360	2318.0	0.6590	0.6897	0.6411	0.6274	4320.0	0.6853	0.6590	0.6353	4320.0

Framework versions

• Transformers 4.46.3
• Pytorch 2.0.0+cu117
• Datasets 3.1.0
• Tokenizers 0.20.3