meat_calssify_fresh_no_crop_V_0_1

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

• Loss: 1.5082
• Accuracy: 0.7273

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: 1
• eval_batch_size: 1
• 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
1.1032	1.0	44	1.0785	0.3636
1.0983	2.0	88	1.1069	0.2727
1.1025	3.0	132	1.0854	0.4545
1.1036	4.0	176	1.1205	0.3636
1.16	5.0	220	1.0577	0.4545
1.0902	6.0	264	1.1767	0.2727
1.0789	7.0	308	1.2790	0.4545
1.1269	8.0	352	1.1196	0.4545
1.1132	9.0	396	1.1290	0.3636
1.092	10.0	440	1.2584	0.2727
0.988	11.0	484	0.9824	0.4545
0.9695	12.0	528	1.3389	0.2727
0.9343	13.0	572	1.2876	0.3636
0.8517	14.0	616	1.1018	0.4545
0.7473	15.0	660	1.2833	0.4545
0.7194	16.0	704	1.5489	0.4545
0.5832	17.0	748	1.2821	0.5455
0.4905	18.0	792	0.9996	0.7273
0.3587	19.0	836	1.1785	0.6364
0.178	20.0	880	1.3718	0.5455
0.1253	21.0	924	2.1013	0.4545
0.5536	22.0	968	1.4723	0.5455
0.5241	23.0	1012	1.6866	0.6364
0.2453	24.0	1056	1.6747	0.5455
0.1193	25.0	1100	1.3248	0.7273
0.0892	26.0	1144	2.3257	0.4545
0.3273	27.0	1188	1.8027	0.5455
0.3587	28.0	1232	2.1175	0.3636
0.1693	29.0	1276	0.8854	0.7273
0.2323	30.0	1320	1.5909	0.6364
0.1056	31.0	1364	1.5556	0.6364
0.0158	32.0	1408	1.8192	0.6364
0.2085	33.0	1452	2.1498	0.5455
0.1137	34.0	1496	1.8617	0.4545
0.287	35.0	1540	1.5198	0.5455
0.25	36.0	1584	2.1324	0.4545
0.0135	37.0	1628	2.1540	0.4545
0.1104	38.0	1672	2.2697	0.5455
0.2252	39.0	1716	2.5110	0.4545
0.0584	40.0	1760	2.6245	0.3636
0.2366	41.0	1804	2.2701	0.5455
0.089	42.0	1848	2.3318	0.5455
0.1237	43.0	1892	2.2786	0.5455
0.0121	44.0	1936	1.2596	0.6364
0.1234	45.0	1980	1.2882	0.7273
0.0116	46.0	2024	1.4629	0.7273
0.0508	47.0	2068	1.8392	0.6364
0.0221	48.0	2112	1.7354	0.6364
0.3441	49.0	2156	2.0862	0.5455
0.138	50.0	2200	1.5082	0.7273

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0
• Datasets 2.19.2
• Tokenizers 0.19.1