sign_language_classification_v1

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3445
• Accuracy: 0.8056

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.2889	1.0	8	3.2714	0.0556
3.2492	2.0	16	3.2615	0.125
3.2263	3.0	24	3.2034	0.125
3.1271	4.0	32	3.1297	0.2083
2.9592	5.0	40	3.0655	0.2639
2.9414	6.0	48	2.9282	0.3472
2.7337	7.0	56	2.8254	0.4028
2.6683	8.0	64	2.6909	0.4583
2.5837	9.0	72	2.5904	0.5417
2.4566	10.0	80	2.5380	0.5833
2.2188	11.0	88	2.4682	0.5417
2.2885	12.0	96	2.3196	0.5833
2.005	13.0	104	2.2824	0.6667
1.9293	14.0	112	2.1967	0.6389
1.8396	15.0	120	2.0287	0.7361
1.7066	16.0	128	2.0357	0.7361
1.6911	17.0	136	1.9670	0.7361
1.6285	18.0	144	1.9186	0.7361
1.6064	19.0	152	1.9239	0.6944
1.6067	20.0	160	1.7723	0.7778
1.4094	21.0	168	1.7701	0.75
1.4664	22.0	176	1.7453	0.75
1.3255	23.0	184	1.7103	0.7083
1.3253	24.0	192	1.7216	0.7778
1.2416	25.0	200	1.5770	0.7778
1.1696	26.0	208	1.5099	0.7917
1.1645	27.0	216	1.4630	0.7917
1.0646	28.0	224	1.4989	0.7917
1.0149	29.0	232	1.5569	0.7222
1.0799	30.0	240	1.3602	0.8333
0.9528	31.0	248	1.3782	0.8472
1.0461	32.0	256	1.3698	0.8333
0.9019	33.0	264	1.3251	0.8611
0.9494	34.0	272	1.3586	0.8472
0.9439	35.0	280	1.3526	0.8333
0.9089	36.0	288	1.2728	0.8333
0.8962	37.0	296	1.3006	0.7917
0.9482	38.0	304	1.2592	0.8611
0.8804	39.0	312	1.3527	0.7778
0.8348	40.0	320	1.2759	0.8056
0.7823	41.0	328	1.3071	0.8194
0.8944	42.0	336	1.2428	0.8194
0.9677	43.0	344	1.2903	0.7778
0.9584	44.0	352	1.3119	0.7639
0.8342	45.0	360	1.3502	0.7778
0.7878	46.0	368	1.1941	0.8333
0.7817	47.0	376	1.2670	0.8056
0.812	48.0	384	1.2068	0.8194
0.9714	49.0	392	1.3480	0.75
0.9362	50.0	400	1.4028	0.7083

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0