VideoMAE_WLASL_250_epochs

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: 4.9119
• Top 1 Accuracy: 0.1622
• Top 5 Accuracy: 0.4086
• Top 10 Accuracy: 0.5301
• Accuracy: 0.1624
• Precision: 0.1508
• Recall: 0.1624
• F1: 0.1418

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: 893000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Top 1 Accuracy	Top 5 Accuracy	Top 10 Accuracy	Accuracy	Precision	Recall	F1
30.4991	0.004	3572	7.6318	0.0008	0.0026	0.0054	0.0008	0.0000	0.0008	0.0001
30.456	1.0040	7144	7.6069	0.0015	0.0054	0.0092	0.0013	0.0000	0.0013	0.0000
30.2884	2.0040	10716	7.5924	0.0015	0.0059	0.0105	0.0015	0.0000	0.0015	0.0000
30.1254	3.0040	14289	7.5970	0.0015	0.0072	0.0102	0.0015	0.0000	0.0015	0.0000
29.9334	4.004	17861	7.5835	0.0033	0.0100	0.0166	0.0033	0.0000	0.0033	0.0001
28.9209	5.0040	21433	7.4102	0.0028	0.0140	0.0232	0.0028	0.0001	0.0028	0.0001
27.4124	6.0040	25005	7.0850	0.0072	0.0301	0.0554	0.0072	0.0013	0.0072	0.0013
25.7784	7.0040	28578	6.7483	0.0156	0.0600	0.1009	0.0158	0.0021	0.0158	0.0034
23.5018	8.004	32150	6.3460	0.0289	0.1055	0.1665	0.0291	0.0080	0.0291	0.0095
20.905	9.0040	35722	5.9416	0.0544	0.1647	0.2428	0.0544	0.0178	0.0544	0.0218
17.9146	10.0040	39294	5.5082	0.0792	0.2265	0.3279	0.0792	0.0374	0.0792	0.0414
14.3734	11.0040	42867	5.0939	0.1152	0.2939	0.4109	0.1152	0.0683	0.1152	0.0740
10.1724	12.004	46439	4.7246	0.1402	0.3432	0.4727	0.1399	0.0938	0.1399	0.1001
6.4722	13.0040	50011	4.3776	0.1675	0.4053	0.5426	0.1675	0.1327	0.1675	0.1338
3.9864	14.0040	53583	4.2916	0.1652	0.4346	0.5531	0.1655	0.1414	0.1655	0.1393
2.2789	15.0040	57156	4.2496	0.1724	0.4298	0.5590	0.1724	0.1497	0.1724	0.1460
1.8662	16.004	60728	4.3586	0.1688	0.4303	0.5523	0.1691	0.1509	0.1691	0.1451
1.3092	17.0040	64300	4.4090	0.1744	0.4336	0.5641	0.1744	0.1558	0.1744	0.1498
1.2238	18.0040	67872	4.4967	0.1680	0.4336	0.5575	0.1678	0.1544	0.1678	0.1453
1.3316	19.0040	71445	4.6016	0.1685	0.4213	0.5493	0.1685	0.1463	0.1685	0.1415
1.3673	20.004	75017	4.7247	0.1591	0.4139	0.5411	0.1588	0.1476	0.1588	0.1380
1.2106	21.0040	78589	4.8187	0.1629	0.4122	0.5291	0.1629	0.1396	0.1629	0.1362
1.2995	22.0040	82161	4.9119	0.1622	0.4086	0.5301	0.1624	0.1508	0.1624	0.1418

Framework versions

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