meat_calssify_fresh_crop_fixed_epoch100_V_0_6

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: 0.5432
• Accuracy: 0.8165

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: 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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1096	1.0	10	1.1024	0.3291
1.0818	2.0	20	1.0876	0.4241
1.0619	3.0	30	1.0912	0.3987
1.0289	4.0	40	1.0680	0.4494
1.0023	5.0	50	1.0357	0.4241
0.9453	6.0	60	1.0095	0.4937
0.8856	7.0	70	0.9563	0.5443
0.8301	8.0	80	0.9307	0.5506
0.7855	9.0	90	0.9109	0.5696
0.7062	10.0	100	0.8383	0.5949
0.6655	11.0	110	0.7534	0.7089
0.617	12.0	120	0.8160	0.6646
0.5821	13.0	130	0.7361	0.6962
0.5097	14.0	140	0.7594	0.6899
0.4944	15.0	150	0.7679	0.6709
0.4787	16.0	160	0.6902	0.7595
0.4234	17.0	170	0.8019	0.6835
0.451	18.0	180	0.6534	0.7532
0.4402	19.0	190	0.6980	0.7152
0.395	20.0	200	0.7276	0.6962
0.3548	21.0	210	0.7002	0.6899
0.3231	22.0	220	0.7080	0.7089
0.2844	23.0	230	0.6934	0.7152
0.2909	24.0	240	0.6465	0.7468
0.3433	25.0	250	0.5959	0.7595
0.2718	26.0	260	0.6787	0.7405
0.2516	27.0	270	0.5951	0.8165
0.2717	28.0	280	0.6355	0.7405
0.2509	29.0	290	0.6980	0.6899
0.2407	30.0	300	0.5824	0.7722
0.2444	31.0	310	0.6241	0.7342
0.2153	32.0	320	0.5982	0.7658
0.2294	33.0	330	0.6701	0.7532
0.1901	34.0	340	0.8116	0.6772
0.252	35.0	350	0.5960	0.7848
0.1761	36.0	360	0.5993	0.7785
0.1892	37.0	370	0.6046	0.7785
0.15	38.0	380	0.6442	0.8038
0.1705	39.0	390	0.7802	0.7152
0.2171	40.0	400	0.7078	0.7278
0.2233	41.0	410	0.5835	0.7785
0.2252	42.0	420	0.7923	0.7089
0.2304	43.0	430	0.6414	0.7342
0.231	44.0	440	0.5405	0.7911
0.1573	45.0	450	0.7065	0.7468
0.1203	46.0	460	0.7642	0.7278
0.1712	47.0	470	0.6204	0.7658
0.1406	48.0	480	0.6591	0.7658
0.1877	49.0	490	0.7968	0.7152
0.1457	50.0	500	0.6790	0.7658
0.1391	51.0	510	0.6116	0.7658
0.1508	52.0	520	0.7331	0.7722
0.1468	53.0	530	0.6900	0.7722
0.13	54.0	540	0.5799	0.7975
0.1078	55.0	550	0.6568	0.7785
0.0945	56.0	560	0.6073	0.8101
0.1358	57.0	570	0.4966	0.8291
0.1605	58.0	580	0.6678	0.7722
0.1673	59.0	590	0.5742	0.7975
0.1112	60.0	600	0.6181	0.8038
0.1144	61.0	610	0.5031	0.8354
0.1154	62.0	620	0.6085	0.8038
0.1141	63.0	630	0.4470	0.8734
0.1201	64.0	640	0.5089	0.8291
0.0871	65.0	650	0.5447	0.8481
0.0939	66.0	660	0.5763	0.7911
0.1098	67.0	670	0.6186	0.7911
0.1062	68.0	680	0.6349	0.8038
0.0755	69.0	690	0.8513	0.7278
0.1257	70.0	700	0.6852	0.7911
0.0651	71.0	710	0.7073	0.7722
0.1024	72.0	720	0.5794	0.8354
0.0887	73.0	730	0.7889	0.7278
0.1014	74.0	740	0.5774	0.8228
0.0986	75.0	750	0.6864	0.8038
0.098	76.0	760	0.4825	0.8354
0.1049	77.0	770	0.7881	0.7658
0.0997	78.0	780	0.5239	0.8418
0.0939	79.0	790	0.6434	0.8228
0.0851	80.0	800	0.5087	0.8291
0.0683	81.0	810	0.6410	0.7658
0.084	82.0	820	0.6120	0.8101
0.0717	83.0	830	0.6231	0.8038
0.0811	84.0	840	0.4338	0.8544
0.066	85.0	850	0.4633	0.8544
0.0746	86.0	860	0.6960	0.7595
0.0864	87.0	870	0.6154	0.8101
0.0432	88.0	880	0.5864	0.8228
0.0644	89.0	890	0.6045	0.7911
0.0644	90.0	900	0.5924	0.8101
0.0976	91.0	910	0.6515	0.8165
0.0593	92.0	920	0.5491	0.8101
0.0884	93.0	930	0.6618	0.8101
0.0752	94.0	940	0.5612	0.8165
0.0423	95.0	950	0.5914	0.8101
0.0685	96.0	960	0.5502	0.8291
0.0425	97.0	970	0.6455	0.7975
0.0639	98.0	980	0.5402	0.8291
0.056	99.0	990	0.5159	0.8481
0.0663	100.0	1000	0.5432	0.8165

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1
• Datasets 2.20.0
• Tokenizers 0.19.1