CT-MAE-RIS2-Phase2-V1

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: 0.4894
• Accuracy: 0.8043

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: 3250

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6186	0.02	65	0.7367	0.5435
0.5974	1.02	130	0.8185	0.5435
0.5491	2.02	195	0.8372	0.5435
0.6156	3.02	260	0.6620	0.5870
0.6255	4.02	325	0.6835	0.5435
0.438	5.02	390	1.2116	0.5435
0.4653	6.02	455	0.6002	0.5652
0.5876	7.02	520	0.4894	0.8043
0.3801	8.02	585	0.8324	0.5435
0.4474	9.02	650	1.1581	0.5652
0.694	10.02	715	0.5354	0.7174
0.4773	11.02	780	0.6181	0.6957
0.6208	12.02	845	0.5677	0.7609
0.344	13.02	910	0.7452	0.6087
0.254	14.02	975	0.6362	0.7391
0.4578	15.02	1040	0.8304	0.6957
0.3954	16.02	1105	0.6049	0.7609
0.248	17.02	1170	0.9506	0.6739
0.1334	18.02	1235	1.1876	0.6739
0.534	19.02	1300	0.6296	0.7391
0.3556	20.02	1365	1.3007	0.6957
0.5439	21.02	1430	1.5066	0.6739
0.4107	22.02	1495	0.9273	0.8043
0.61	23.02	1560	1.0008	0.7174
0.6482	24.02	1625	0.7548	0.7609
0.199	25.02	1690	0.7917	0.7826
0.1185	26.02	1755	0.7529	0.7826
0.3886	27.02	1820	0.8627	0.7609
0.0123	28.02	1885	1.3886	0.7174
0.5328	29.02	1950	1.2803	0.6957
0.2961	30.02	2015	1.4397	0.7174
0.1192	31.02	2080	2.2563	0.6304
0.145	32.02	2145	1.0465	0.7609
0.0924	33.02	2210	0.9859	0.7826
0.1016	34.02	2275	1.0758	0.7826
0.1894	35.02	2340	1.2088	0.7609
0.2657	36.02	2405	1.5409	0.7391
0.1235	37.02	2470	1.2736	0.7609
0.1539	38.02	2535	1.2608	0.7609
0.03	39.02	2600	1.2058	0.7609
0.1447	40.02	2665	1.1072	0.7609
0.0888	41.02	2730	1.1454	0.7826
0.0016	42.02	2795	1.1194	0.7826
0.1489	43.02	2860	1.2170	0.7609
0.0004	44.02	2925	1.1894	0.7609
0.0004	45.02	2990	1.3329	0.7391
0.0014	46.02	3055	1.1887	0.7609
0.1675	47.02	3120	1.2652	0.7391
0.012	48.02	3185	1.3228	0.7391
0.0475	49.02	3250	1.3507	0.7391

Framework versions

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