9_mae_4

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

• Loss: 0.4857
• Accuracy: 0.8667

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: 1e-05
• train_batch_size: 4
• eval_batch_size: 4
• 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: linear
• lr_scheduler_warmup_ratio: 0.1
• training_steps: 9700

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6583	0.0201	195	0.7953	0.4667
0.6439	1.0201	390	0.7813	0.4667
0.5285	2.0201	585	0.5919	0.8
0.3275	3.0201	780	0.5570	0.6222
0.5265	4.0201	975	0.7656	0.7333
0.3472	5.0201	1170	1.0666	0.5556
0.3978	6.0201	1365	0.7593	0.7333
0.5104	7.0201	1560	0.6972	0.7111
0.5969	8.0201	1755	0.5279	0.7778
0.5841	9.0201	1950	0.6547	0.8
0.4973	10.0201	2145	0.5363	0.7556
0.3399	11.0201	2340	1.0321	0.6667
0.2955	12.0201	2535	1.2320	0.6
0.5854	13.0201	2730	1.3658	0.7333
0.153	14.0201	2925	1.0651	0.7333
0.3668	15.0201	3120	0.5162	0.7333
0.2628	16.0201	3315	0.7342	0.8
0.5817	17.0201	3510	0.4857	0.8667
0.7572	18.0201	3705	0.4588	0.8222
0.4044	19.0201	3900	1.1526	0.7111
0.5366	20.0201	4095	1.3653	0.6667
0.5051	21.0201	4290	0.6016	0.8222
0.3338	22.0201	4485	0.7427	0.8222
0.7609	23.0201	4680	0.6195	0.8667
0.2756	24.0201	4875	0.9184	0.8
0.2845	25.0201	5070	1.1702	0.7333
0.3813	26.0201	5265	0.7869	0.8444
0.1671	27.0201	5460	0.8510	0.8222
0.3501	28.0201	5655	1.3892	0.7111
0.5373	29.0201	5850	1.8380	0.6667
0.0084	30.0201	6045	0.9612	0.8222
0.6559	31.0201	6240	1.5071	0.7111
0.5831	32.0201	6435	1.5741	0.7556
0.0256	33.0201	6630	1.6111	0.7333
0.4461	34.0201	6825	0.7689	0.8222
0.0307	35.0201	7020	0.6986	0.8667
0.0007	36.0201	7215	1.4886	0.7778
0.0672	37.0201	7410	1.4161	0.7778
0.0106	38.0201	7605	1.1670	0.8
0.0009	39.0201	7800	1.0056	0.8222
0.5082	40.0201	7995	0.8138	0.8222
0.143	41.0201	8190	0.9801	0.8444
0.3104	42.0201	8385	1.0276	0.8
0.2251	43.0201	8580	1.2434	0.7556
0.2293	44.0201	8775	1.2502	0.7778
0.1605	45.0201	8970	1.0620	0.7778
0.1764	46.0201	9165	1.3246	0.7556
0.0096	47.0201	9360	1.3971	0.7778
0.0002	48.0201	9555	1.3673	0.7778
0.001	49.0149	9700	1.4292	0.7556

Framework versions

• Transformers 4.46.2
• Pytorch 2.0.1+cu117
• Datasets 3.0.1
• Tokenizers 0.20.0