videomae-base-finetuned-engine-subset-20230313

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: 0.8913
• Accuracy: 0.6745

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: 6
• eval_batch_size: 6
• 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: 1110

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.6212	0.03	38	2.3629	0.3774
2.455	1.03	76	2.3674	0.2170
2.4311	2.03	114	2.2191	0.3231
2.2768	3.03	152	2.1227	0.3608
1.7528	4.03	190	1.7296	0.4363
1.5381	5.03	228	1.5016	0.4340
1.407	6.03	266	1.2878	0.5448
1.1053	7.03	304	1.5210	0.4009
1.0893	8.03	342	1.3902	0.4623
0.8136	9.03	380	1.6456	0.4033
0.9565	10.03	418	1.1826	0.5613
1.0147	11.03	456	1.2099	0.5118
0.9125	12.03	494	1.1850	0.5495
0.7091	13.03	532	1.2324	0.5354
0.7361	14.03	570	1.0225	0.6226
0.6979	15.03	608	1.0738	0.5590
0.5265	16.03	646	1.1062	0.5873
0.5651	17.03	684	1.1402	0.5802
0.7182	18.03	722	1.0974	0.5802
0.6582	19.03	760	1.0529	0.6179
0.5709	20.03	798	0.9655	0.6344
0.4808	21.03	836	1.0441	0.6226
0.5816	22.03	874	0.9445	0.6439
0.5057	23.03	912	1.0248	0.6321
0.6253	24.03	950	0.9518	0.6604
0.6841	25.03	988	0.8913	0.6745
0.5933	26.03	1026	0.9013	0.6439
0.389	27.03	1064	0.9090	0.6627
0.3705	28.03	1102	0.8936	0.6722
0.6043	29.01	1110	0.8942	0.6722

Framework versions

• Transformers 4.26.1
• Pytorch 1.12.1+cu113
• Datasets 2.10.1
• Tokenizers 0.13.2