videomae-base-Vsl-Lab-PC-V9

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3401
• Accuracy: 0.8112

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: 10
• eval_batch_size: 10
• 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: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0001	0.02	81	1.0305	0.8455
0.0001	1.02	162	1.0570	0.8412
0.0	2.02	243	1.0822	0.8369
0.0	3.02	324	1.1003	0.8412
0.0	4.02	405	1.1198	0.8412
0.5974	5.02	486	1.9278	0.6953
0.2193	6.02	567	1.1442	0.7554
0.0261	7.02	648	0.9625	0.8026
0.0859	8.02	729	1.1657	0.8155
0.0044	9.02	810	1.2597	0.8197
0.0007	10.02	891	1.2663	0.8112
0.0001	11.02	972	1.2367	0.8240
0.0002	12.02	1053	1.1224	0.8326
0.0002	13.02	1134	1.1528	0.8326
0.0	14.02	1215	1.1598	0.8326
0.0	15.02	1296	1.1572	0.8369
0.0	16.02	1377	1.1560	0.8369
0.0	17.02	1458	1.1555	0.8369
0.0002	18.02	1539	1.3611	0.8112
0.0	19.02	1620	1.2183	0.8326
0.0	20.02	1701	1.2105	0.8283
0.0	21.02	1782	1.2063	0.8283
0.0	22.02	1863	1.2034	0.8283
0.0	23.02	1944	1.2020	0.8283
0.001	24.02	2025	1.1831	0.8412
0.0687	25.02	2106	1.2683	0.8240
0.0	26.02	2187	1.2521	0.8240
0.0	27.02	2268	1.2430	0.8326
0.0	28.02	2349	1.2394	0.8326
0.0001	29.02	2430	1.2711	0.8283
0.0	30.02	2511	1.2562	0.8283
0.0	31.02	2592	1.2484	0.8326
0.0	32.02	2673	1.2432	0.8326
0.0	33.02	2754	1.2387	0.8326
0.0	34.02	2835	1.2349	0.8326
0.0	35.02	2916	1.2319	0.8326
0.0	36.02	2997	1.2294	0.8326
0.0	37.02	3078	1.2276	0.8326
0.0	38.02	3159	1.2245	0.8326
0.0	39.02	3240	1.2232	0.8326
0.0	40.02	3321	1.2217	0.8326
0.0	41.02	3402	1.2204	0.8326
0.0	42.02	3483	1.3696	0.7983
0.0001	43.02	3564	1.3923	0.7940
0.0	44.02	3645	1.3421	0.8112
0.0	45.02	3726	1.3414	0.8112
0.0	46.02	3807	1.3407	0.8112
0.0	47.02	3888	1.3403	0.8112
0.0	48.02	3969	1.3401	0.8112
0.0	49.01	4000	1.3401	0.8112

Framework versions

• Transformers 4.39.1
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2