MAE-CT-CPC-Dicotomized-n0-m1-d1-v9

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: 1.6419
• Accuracy: 0.5

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• training_steps: 2400

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6819	0.0204	49	0.6641	0.5789
0.6201	1.0204	98	0.6368	0.5789
0.6787	2.0204	147	0.6109	0.5789
0.6484	3.0204	196	0.6305	0.6316
0.6357	4.0204	245	0.6794	0.5263
0.5692	5.0204	294	0.6938	0.5263
0.658	6.0204	343	0.6696	0.5789
0.5112	7.0204	392	0.5669	0.6316
0.3719	8.0204	441	0.8023	0.6316
0.4573	9.0204	490	0.5991	0.6316
0.2915	10.0204	539	0.7899	0.6842
0.3723	11.0204	588	0.9848	0.5263
0.1372	12.0204	637	0.9510	0.6316
0.0066	13.0204	686	1.1872	0.5789
0.2218	14.0204	735	1.1692	0.7368
0.7666	15.0204	784	1.1125	0.6842
0.2171	16.0204	833	1.2615	0.7368
0.1152	17.0204	882	1.3683	0.7368
0.1941	18.0204	931	1.3915	0.7368
0.0233	19.0204	980	1.5972	0.5789
0.2367	20.0204	1029	1.7445	0.6842
0.1575	21.0204	1078	2.4036	0.6316
0.0008	22.0204	1127	2.2633	0.6316
0.019	23.0204	1176	2.2078	0.6842
0.0006	24.0204	1225	2.1281	0.6316
0.0004	25.0204	1274	2.0100	0.7368
0.0002	26.0204	1323	2.3862	0.6316
0.0003	27.0204	1372	2.1579	0.6842
0.0372	28.0204	1421	2.1226	0.6842
0.1547	29.0204	1470	2.4949	0.6842
0.0015	30.0204	1519	1.8815	0.6842
0.0005	31.0204	1568	2.2582	0.6316
0.0002	32.0204	1617	2.1774	0.6316
0.0001	33.0204	1666	2.2222	0.6316
0.0001	34.0204	1715	2.3560	0.6316
0.0001	35.0204	1764	2.3904	0.6316
0.0001	36.0204	1813	2.2240	0.7368
0.0002	37.0204	1862	2.2880	0.6842
0.0002	38.0204	1911	2.3010	0.6842
0.0001	39.0204	1960	2.3124	0.6842
0.0001	40.0204	2009	2.2903	0.6842
0.1744	41.0204	2058	2.3067	0.6842
0.0001	42.0204	2107	2.3422	0.6842
0.0001	43.0204	2156	2.3477	0.6842
0.0001	44.0204	2205	2.3783	0.6316
0.0001	45.0204	2254	2.3447	0.6842
0.0001	46.0204	2303	2.3415	0.6842
0.0001	47.0204	2352	2.3419	0.6842
0.0001	48.02	2400	2.3470	0.6842

Framework versions

• Transformers 4.45.2
• Pytorch 2.0.1+cu117
• Datasets 3.0.1
• Tokenizers 0.20.0