VideoMAE_BdSLW60_FrameRate_Corrected_with_Augment_20_epoch_RQ_GB

This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5902
• Accuracy: 0.915
• Precision: 0.9412
• Recall: 0.915
• F1: 0.9057

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• training_steps: 18560
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
9.3789	0.0501	929	2.4070	0.4117	0.4786	0.4117	0.3632
2.3248	1.0501	1858	0.8424	0.7817	0.8018	0.7817	0.7540
0.858	2.0501	2787	0.6534	0.8233	0.8511	0.8233	0.8072
0.5065	3.0501	3716	0.5403	0.8733	0.9145	0.8733	0.8657
0.3166	4.0501	4645	1.0851	0.8317	0.8330	0.8317	0.8113
0.2901	5.0501	5574	0.9035	0.8367	0.8630	0.8367	0.8234
0.2002	6.0501	6503	0.8559	0.88	0.8737	0.88	0.8648
0.3852	7.0501	7432	0.6272	0.8883	0.9167	0.8883	0.8889
0.1676	8.0501	8361	0.7550	0.885	0.9165	0.885	0.8753
0.1147	9.0501	9290	0.7963	0.8717	0.8853	0.8717	0.8587
0.1002	10.0501	10219	0.7064	0.8917	0.9123	0.8917	0.8862
0.1555	11.0501	11148	0.6789	0.9017	0.9237	0.9017	0.8961
0.0757	12.0501	12077	0.9329	0.88	0.9259	0.88	0.8687
0.0246	13.0501	13006	0.6579	0.9033	0.9194	0.9033	0.8998
0.1073	14.0501	13935	0.3330	0.94	0.9503	0.94	0.9370
0.0087	15.0501	14864	1.1771	0.8533	0.8747	0.8533	0.8383
0.0005	16.0501	15793	0.6474	0.9017	0.9223	0.9017	0.8936
0.0018	17.0501	16722	0.6391	0.9067	0.9130	0.9067	0.8952
0.0001	18.0501	17651	0.6105	0.9167	0.9447	0.9167	0.9087
0.0	19.0490	18560	0.5902	0.915	0.9412	0.915	0.9057

Framework versions

• Transformers 4.46.1
• Pytorch 2.5.1+cu124
• Datasets 3.1.0
• Tokenizers 0.20.1