videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen

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

• Loss: 0.6309
• Accuracy: 0.8900

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• training_steps: 11100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.5158	0.02	222	3.6067	0.0588
2.8571	1.02	444	3.1445	0.3014
1.8854	2.02	666	2.3644	0.4607
1.5533	3.02	888	1.7967	0.5621
1.3935	4.02	1110	1.3755	0.6502
1.1722	5.02	1332	1.2232	0.7109
0.2896	6.02	1554	1.2859	0.6256
0.3166	7.02	1776	1.2910	0.6720
0.6902	8.02	1998	1.2702	0.6995
0.4193	9.02	2220	1.2087	0.7137
0.1889	10.02	2442	1.0500	0.7611
0.4502	11.02	2664	1.1647	0.7118
0.7703	12.02	2886	1.1037	0.7242
0.0957	13.02	3108	1.0967	0.7706
0.3202	14.02	3330	1.0479	0.7545
0.3634	15.02	3552	1.0714	0.8057
0.3883	16.02	3774	1.2323	0.7498
0.0322	17.02	3996	1.0504	0.7848
0.5108	18.02	4218	1.1356	0.7915
0.309	19.02	4440	1.1409	0.7592
0.56	20.02	4662	1.0828	0.7915
0.3675	21.02	4884	0.9154	0.8123
0.0076	22.02	5106	1.0974	0.8133
0.0451	23.02	5328	1.0361	0.8152
0.2558	24.02	5550	0.7830	0.8237
0.0125	25.02	5772	0.8728	0.8171
0.4184	26.02	5994	0.8413	0.8265
0.2566	27.02	6216	1.0644	0.8009
0.1257	28.02	6438	0.8641	0.8265
0.1326	29.02	6660	0.8444	0.8417
0.0436	30.02	6882	0.8615	0.8322
0.0408	31.02	7104	0.8075	0.8332
0.0316	32.02	7326	0.8699	0.8341
0.2235	33.02	7548	0.8151	0.8455
0.0079	34.02	7770	0.8099	0.8550
0.001	35.02	7992	0.8640	0.8370
0.0007	36.02	8214	0.7146	0.8483
0.464	37.02	8436	0.7917	0.8464
0.0005	38.02	8658	0.7239	0.8531
0.0004	39.02	8880	0.7702	0.8701
0.1705	40.02	9102	0.7543	0.8521
0.0039	41.02	9324	0.7456	0.8673
0.0168	42.02	9546	0.7255	0.8730
0.2615	43.02	9768	0.7453	0.8758
0.0004	44.02	9990	0.6824	0.8806
0.236	45.02	10212	0.6624	0.8825
0.0007	46.02	10434	0.6727	0.8815
0.0004	47.02	10656	0.6478	0.8863
0.268	48.02	10878	0.6309	0.8900
0.0025	49.02	11100	0.6284	0.8900

Framework versions

• Transformers 4.33.2
• Pytorch 1.12.1+cu113
• Datasets 2.14.5
• Tokenizers 0.13.3