VideoMAE_Base_wlasl_100_longtail_200

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: 2.9736
• Accuracy: 0.4822

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: 36000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
18.7021	0.005	180	4.6442	0.0118
18.6026	1.0050	360	4.6319	0.0118
18.5393	2.0050	540	4.6238	0.0178
18.3789	3.0050	721	4.6170	0.0178
18.3763	4.005	901	4.6237	0.0178
18.334	5.0050	1081	4.6342	0.0296
18.1779	6.0050	1261	4.6036	0.0296
17.9948	7.0050	1442	4.5903	0.0266
17.9333	8.005	1622	4.6144	0.0237
17.7505	9.0050	1802	4.5865	0.0118
17.4917	10.0050	1982	4.5626	0.0207
16.9821	11.0050	2163	4.3615	0.0444
16.2362	12.005	2343	4.1515	0.0533
15.2255	13.0050	2523	3.9603	0.0740
14.0646	14.0050	2703	3.7594	0.0828
12.8642	15.0050	2884	3.4154	0.1420
11.6502	16.005	3064	3.3917	0.1627
10.332	17.0050	3244	3.0359	0.2249
8.9465	18.0050	3424	2.8625	0.2840
7.6629	19.0050	3605	2.8202	0.3107
6.2517	20.005	3785	2.6478	0.3343
5.1876	21.0050	3965	2.4982	0.3728
4.0929	22.0050	4145	2.3891	0.3876
3.0425	23.0050	4326	2.2212	0.4083
2.3667	24.005	4506	2.1609	0.4586
1.7821	25.0050	4686	2.2471	0.4260
1.4215	26.0050	4866	2.2263	0.4675
1.1324	27.0050	5047	2.2360	0.4556
0.9114	28.005	5227	2.2021	0.4852
0.6966	29.0050	5407	2.3123	0.4408
0.5676	30.0050	5587	2.1198	0.5355
0.4494	31.0050	5768	2.2495	0.4911
0.3321	32.005	5948	2.2658	0.5089
0.227	33.0050	6128	2.4423	0.4882
0.2203	34.0050	6308	2.4358	0.4763
0.2643	35.0050	6489	2.5521	0.4675
0.111	36.005	6669	2.5787	0.4882
0.1009	37.0050	6849	2.4022	0.5059
0.1275	38.0050	7029	2.5451	0.5
0.1874	39.0050	7210	2.8339	0.4586
0.1695	40.005	7390	3.0320	0.4320
0.1735	41.0050	7570	2.6961	0.4941
0.1299	42.0050	7750	2.9589	0.4675
0.1399	43.0050	7931	2.6799	0.5325
0.118	44.005	8111	2.8731	0.5
0.1583	45.0050	8291	2.8757	0.4970
0.1667	46.0050	8471	2.9294	0.4941
0.202	47.0050	8652	3.1443	0.4615
0.1301	48.005	8832	2.8941	0.5207
0.2298	49.0050	9012	3.1270	0.4704
0.1858	50.0050	9192	2.9736	0.4822

Framework versions

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