videomae-base-finetuned-kinetics-finetuned-freeway-subset

This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0016
• Accuracy: 1.0

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: 5
• eval_batch_size: 5
• 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: 1800

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6551	0.02	36	0.7171	0.5
0.6085	1.02	72	0.7057	0.5
0.5876	2.02	108	0.5834	0.75
0.5757	3.02	144	0.5360	0.7143
0.7017	4.02	180	0.4589	0.7143
0.5681	5.02	216	0.3667	0.8214
0.3418	6.02	252	0.2312	0.9286
0.6408	7.02	288	0.2724	0.8929
0.3846	8.02	324	0.1288	0.9643
0.1551	9.02	360	0.6198	0.8214
0.4879	10.02	396	0.3241	0.8571
0.0982	11.02	432	0.1815	0.9286
0.112	12.02	468	0.2504	0.9643
0.3627	13.02	504	0.2357	0.9643
0.2329	14.02	540	0.6101	0.8571
0.141	15.02	576	0.0218	1.0
0.1755	16.02	612	0.0791	0.9643
0.079	17.02	648	0.1167	0.9643
0.0859	18.02	684	0.0118	1.0
0.0131	19.02	720	0.0020	1.0
0.0014	20.02	756	0.0054	1.0
0.0008	21.02	792	0.0841	0.9286
0.0024	22.02	828	0.0852	0.9643
0.1803	23.02	864	0.0005	1.0
0.0001	24.02	900	0.0379	0.9643
0.0012	25.02	936	0.0705	0.9643
0.0018	26.02	972	0.0010	1.0
0.0007	27.02	1008	0.0210	1.0
0.0028	28.02	1044	0.0030	1.0
0.0465	29.02	1080	0.0005	1.0
0.0004	30.02	1116	0.0011	1.0
0.0016	31.02	1152	0.0005	1.0
0.0	32.02	1188	0.0012	1.0
0.0002	33.02	1224	0.0001	1.0
0.0315	34.02	1260	0.0027	1.0
0.0253	35.02	1296	0.0003	1.0
0.0011	36.02	1332	0.0022	1.0
0.0001	37.02	1368	0.0006	1.0
0.0007	38.02	1404	0.0120	1.0
0.0001	39.02	1440	0.1001	0.9643
0.0005	40.02	1476	0.0331	0.9643
0.0009	41.02	1512	0.0418	0.9643
0.0035	42.02	1548	0.0761	0.9643
0.0001	43.02	1584	0.0020	1.0
0.0001	44.02	1620	0.0020	1.0
0.0	45.02	1656	0.0010	1.0
0.0001	46.02	1692	0.0009	1.0
0.0005	47.02	1728	0.0012	1.0
0.0001	48.02	1764	0.0015	1.0
0.0002	49.02	1800	0.0016	1.0

Framework versions

• Transformers 4.42.0.dev0
• Pytorch 2.1.1
• Datasets 2.19.2
• Tokenizers 0.19.1