meat_calssify_fresh_crop_fixed_V_0_3

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: 1.6191
• Accuracy: 0.75

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
1.0921	1.0	621	1.0844	0.3654
1.0438	2.0	1242	1.1755	0.4423
1.1558	3.0	1863	1.0148	0.4487
1.1274	4.0	2484	1.0371	0.5705
1.1992	5.0	3105	1.0598	0.3718
1.2195	6.0	3726	1.3422	0.5256
1.2366	7.0	4347	1.8744	0.4359
1.1302	8.0	4968	1.4699	0.5705
1.1137	9.0	5589	1.3480	0.6154
0.9534	10.0	6210	1.4723	0.5769
1.0269	11.0	6831	1.0999	0.6538
1.0545	12.0	7452	1.2980	0.6090
0.8669	13.0	8073	1.3408	0.6731
0.9571	14.0	8694	1.4879	0.6474
0.8345	15.0	9315	1.4030	0.6603
0.6623	16.0	9936	1.0840	0.7308
0.6014	17.0	10557	1.5054	0.7115
0.5599	18.0	11178	1.1956	0.7628
0.7209	19.0	11799	1.9734	0.5962
0.6289	20.0	12420	1.6165	0.6923
0.429	21.0	13041	1.5766	0.6987
0.628	22.0	13662	1.3948	0.7179
0.5427	23.0	14283	1.7663	0.6795
0.3806	24.0	14904	1.6219	0.6987
0.4443	25.0	15525	1.5065	0.7051
0.3648	26.0	16146	1.5225	0.7308
0.3812	27.0	16767	1.3488	0.75
0.3106	28.0	17388	1.7758	0.6987
0.3725	29.0	18009	1.4190	0.7372
0.4284	30.0	18630	1.6205	0.7115
0.2257	31.0	19251	1.5535	0.7628
0.2869	32.0	19872	1.2077	0.8013
0.3128	33.0	20493	1.9065	0.7051
0.2802	34.0	21114	1.2794	0.7885
0.2647	35.0	21735	1.5823	0.7436
0.3054	36.0	22356	1.3412	0.7756
0.2619	37.0	22977	1.4471	0.7308
0.2289	38.0	23598	1.8176	0.7244
0.1554	39.0	24219	1.5014	0.7628
0.1794	40.0	24840	1.4112	0.7628
0.1835	41.0	25461	1.8688	0.7244
0.2177	42.0	26082	1.2748	0.7821
0.1063	43.0	26703	1.4471	0.7628
0.1798	44.0	27324	1.1872	0.7949
0.1511	45.0	27945	1.3028	0.8077
0.1659	46.0	28566	1.7257	0.7308
0.0917	47.0	29187	1.2314	0.8205
0.1554	48.0	29808	1.4090	0.7821
0.0927	49.0	30429	1.0295	0.8397
0.1188	50.0	31050	1.6191	0.75

Framework versions

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