ViViT_WLASL_100_SR_4_ep200_p20

This model is a fine-tuned version of google/vivit-b-16x2-kinetics400 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.7950
• Accuracy: 0.6272
• Precision: 0.6755
• Recall: 0.6272
• F1: 0.6047

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	Precision	Recall	F1
18.9788	0.005	180	4.6909	0.0266	0.0055	0.0266	0.0082
18.6803	1.0050	360	4.6180	0.0266	0.0034	0.0266	0.0058
18.0341	2.0050	540	4.5181	0.0473	0.0205	0.0473	0.0238
17.0617	3.0050	721	4.3376	0.0740	0.0541	0.0740	0.0478
15.6302	4.005	901	4.0709	0.1183	0.0772	0.1183	0.0815
14.0851	5.0050	1081	3.7552	0.1864	0.1536	0.1864	0.1449
11.953	6.0050	1261	3.4065	0.2663	0.2643	0.2663	0.2221
9.8223	7.0050	1442	3.1038	0.3373	0.3284	0.3373	0.2944
7.8126	8.005	1622	2.7834	0.4142	0.4201	0.4142	0.3676
6.0952	9.0050	1802	2.5129	0.4763	0.4950	0.4763	0.4368
4.3155	10.0050	1982	2.2757	0.5059	0.5594	0.5059	0.4894
3.0214	11.0050	2163	2.0460	0.5473	0.5640	0.5473	0.5178
2.0687	12.005	2343	1.8803	0.5917	0.6184	0.5917	0.5731
1.3523	13.0050	2523	1.7261	0.5917	0.6065	0.5917	0.5601
0.7828	14.0050	2703	1.6275	0.6036	0.6644	0.6036	0.5924
0.4222	15.0050	2884	1.5284	0.6420	0.6756	0.6420	0.6245
0.3113	16.005	3064	1.5459	0.6272	0.6664	0.6272	0.6092
0.2021	17.0050	3244	1.4441	0.6657	0.6991	0.6657	0.6484
0.1698	18.0050	3424	1.5340	0.6124	0.6511	0.6124	0.5942
0.1199	19.0050	3605	1.3935	0.6509	0.6746	0.6509	0.6288
0.0244	20.005	3785	1.4782	0.6686	0.7130	0.6686	0.6574
0.0407	21.0050	3965	1.3890	0.6686	0.7149	0.6686	0.6557
0.0719	22.0050	4145	1.4897	0.6598	0.7189	0.6598	0.6477
0.1163	23.0050	4326	1.3919	0.6716	0.7218	0.6716	0.6639
0.1167	24.005	4506	1.5690	0.6538	0.7189	0.6538	0.6380
0.0366	25.0050	4686	1.5032	0.6746	0.6979	0.6746	0.6541
0.1065	26.0050	4866	1.4893	0.6391	0.6475	0.6391	0.6135
0.0454	27.0050	5047	1.5013	0.6243	0.6601	0.6243	0.6022
0.0844	28.005	5227	1.5609	0.6598	0.6974	0.6598	0.6388
0.109	29.0050	5407	1.4840	0.6657	0.7151	0.6657	0.6507
0.1508	30.0050	5587	1.8017	0.6036	0.6784	0.6036	0.5903
0.1114	31.0050	5768	1.6676	0.6391	0.6721	0.6391	0.6134
0.0931	32.005	5948	1.5345	0.6746	0.7082	0.6746	0.6520
0.0619	33.0050	6128	1.7462	0.6302	0.6424	0.6302	0.6008
0.2698	34.0050	6308	1.7032	0.6331	0.6711	0.6331	0.6126
0.1108	35.0050	6489	1.7695	0.6538	0.6784	0.6538	0.6265
0.1006	36.005	6669	2.0188	0.5828	0.6289	0.5828	0.5661
0.0823	37.0050	6849	1.6487	0.6568	0.6874	0.6568	0.6425
0.0632	38.0050	7029	1.8014	0.6361	0.6917	0.6361	0.6253
0.1162	39.0050	7210	1.6741	0.6450	0.6672	0.6450	0.6196
0.0846	40.005	7390	1.8032	0.6361	0.6948	0.6361	0.6205
0.1528	41.0050	7570	1.8375	0.6331	0.6732	0.6331	0.6102
0.0695	42.0050	7750	1.6722	0.6568	0.7030	0.6568	0.6417
0.1516	43.0050	7931	1.7811	0.6716	0.7009	0.6716	0.6495
0.1565	44.005	8111	1.8077	0.6538	0.6884	0.6538	0.6347
0.0728	45.0050	8291	1.7950	0.6272	0.6755	0.6272	0.6047

Framework versions

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