ctsinov1

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: 0.7987
• Accuracy: 0.8586

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: 2
• eval_batch_size: 2
• 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: 17500

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5787	0.02	350	0.5787	0.7104
0.5175	1.02	700	0.7402	0.8081
0.4062	2.02	1050	0.8532	0.8283
0.7962	3.02	1400	0.7184	0.8114
0.8225	4.02	1750	1.6868	0.5657
0.724	5.02	2100	1.0066	0.7508
0.1468	6.02	2450	0.7703	0.8316
0.8406	7.02	2800	0.5863	0.8485
0.4485	8.02	3150	0.6602	0.8384
0.0134	9.02	3500	0.6907	0.8316
0.11	10.02	3850	0.7098	0.8316
0.6557	11.02	4200	0.6507	0.8384
0.2642	12.02	4550	0.6555	0.8519
0.2413	13.02	4900	0.6481	0.8519
0.6278	14.02	5250	0.6555	0.8552
0.0107	15.02	5600	0.6550	0.8519
0.3013	16.02	5950	0.7405	0.8485
0.5055	17.02	6300	0.6563	0.8451
0.0059	18.02	6650	0.6917	0.8485
0.4332	19.02	7000	0.6888	0.8384
0.2602	20.02	7350	0.7993	0.8418
0.2142	21.02	7700	0.7131	0.8451
0.5742	22.02	8050	0.9735	0.7980
0.2504	23.02	8400	0.8314	0.8384
0.8514	24.02	8750	0.7481	0.8418
0.8148	25.02	9100	0.7210	0.8384
0.2594	26.02	9450	0.9980	0.8249
0.6742	27.02	9800	0.7987	0.8586
0.0063	28.02	10150	0.9369	0.8316
0.5186	29.02	10500	1.0871	0.8148
0.3076	30.02	10850	0.8931	0.8350
0.1113	31.02	11200	1.0014	0.8384
0.2201	32.02	11550	0.8628	0.8485
0.0324	33.02	11900	0.9972	0.8350
0.4411	34.02	12250	1.0592	0.8350
0.0011	35.02	12600	1.0746	0.8283
0.3917	36.02	12950	0.9696	0.8384
0.7268	37.02	13300	1.1062	0.8182
0.3747	38.02	13650	1.0368	0.8350
0.5584	39.02	14000	1.0149	0.8418
0.4637	40.02	14350	1.0104	0.8316
0.0014	41.02	14700	1.0437	0.8418
0.6253	42.02	15050	1.1687	0.8148
0.0009	43.02	15400	1.0243	0.8418
0.0003	44.02	15750	1.0864	0.8316
0.291	45.02	16100	1.0647	0.8384
0.4962	46.02	16450	1.1166	0.8316
0.0919	47.02	16800	1.1209	0.8283
0.0007	48.02	17150	1.1260	0.8316
0.0008	49.02	17500	1.1139	0.8350

Framework versions

• Transformers 4.56.2
• Pytorch 2.6.0+cu118
• Datasets 2.2.1
• Tokenizers 0.22.1