meat_calssify_fresh_crop_fixed_epoch100_V_0_7

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: 0.6404
• Accuracy: 0.8101

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
1.098	1.0	10	1.0916	0.4114
1.0825	2.0	20	1.0892	0.3861
1.0588	3.0	30	1.0802	0.3608
1.0298	4.0	40	1.0551	0.4430
0.981	5.0	50	1.0183	0.4620
0.9274	6.0	60	0.9723	0.5063
0.8655	7.0	70	0.9539	0.5443
0.8275	8.0	80	0.8944	0.5696
0.815	9.0	90	0.8859	0.6013
0.7543	10.0	100	0.9931	0.5253
0.7501	11.0	110	0.9048	0.5316
0.7036	12.0	120	0.8500	0.6329
0.6742	13.0	130	0.8228	0.6203
0.6331	14.0	140	0.8357	0.6076
0.5479	15.0	150	0.7833	0.6203
0.5342	16.0	160	0.9080	0.5633
0.5066	17.0	170	0.9448	0.5823
0.5077	18.0	180	0.7903	0.6772
0.3941	19.0	190	0.7109	0.7089
0.3837	20.0	200	0.7422	0.6899
0.3622	21.0	210	0.6693	0.7152
0.3725	22.0	220	0.7556	0.6709
0.3945	23.0	230	0.7561	0.6835
0.3325	24.0	240	0.8287	0.6203
0.3456	25.0	250	1.2373	0.5759
0.3829	26.0	260	0.8878	0.6013
0.3148	27.0	270	0.8503	0.7025
0.3543	28.0	280	0.6707	0.7152
0.2581	29.0	290	0.6273	0.7595
0.2237	30.0	300	0.5921	0.7722
0.1946	31.0	310	0.5894	0.7911
0.2369	32.0	320	0.7187	0.7278
0.2564	33.0	330	0.8258	0.7025
0.1967	34.0	340	0.5263	0.7911
0.1974	35.0	350	0.7137	0.7152
0.1656	36.0	360	0.6219	0.7722
0.1874	37.0	370	0.7103	0.7342
0.22	38.0	380	0.6303	0.7785
0.1705	39.0	390	0.6412	0.7658
0.1848	40.0	400	0.6148	0.7785
0.1567	41.0	410	0.5199	0.8101
0.113	42.0	420	0.7023	0.7595
0.1704	43.0	430	0.6339	0.7848
0.1829	44.0	440	0.5446	0.8165
0.1325	45.0	450	0.6403	0.7658
0.1375	46.0	460	0.6033	0.8101
0.1425	47.0	470	0.5715	0.8101
0.16	48.0	480	0.6529	0.7911
0.1862	49.0	490	0.7063	0.7468
0.1583	50.0	500	0.4872	0.7975
0.1141	51.0	510	0.7283	0.7089
0.1333	52.0	520	0.6197	0.8101
0.1062	53.0	530	0.5728	0.8291
0.1159	54.0	540	0.7551	0.7532
0.1152	55.0	550	0.7598	0.7532
0.1339	56.0	560	0.7102	0.7658
0.1244	57.0	570	0.5283	0.8038
0.1247	58.0	580	0.6756	0.7658
0.1269	59.0	590	0.7887	0.7468
0.1321	60.0	600	0.6724	0.7658
0.1267	61.0	610	0.6647	0.7911
0.1066	62.0	620	0.5684	0.8038
0.1058	63.0	630	0.6389	0.7848
0.0944	64.0	640	0.5810	0.7975
0.0751	65.0	650	0.8577	0.7215
0.1129	66.0	660	0.5848	0.8038
0.1448	67.0	670	0.5494	0.7911
0.0962	68.0	680	0.6846	0.7722
0.0766	69.0	690	0.5374	0.8101
0.0955	70.0	700	0.6121	0.7848
0.0917	71.0	710	0.6612	0.7848
0.0832	72.0	720	0.6200	0.7911
0.0686	73.0	730	0.6439	0.8038
0.082	74.0	740	0.5290	0.8291
0.0853	75.0	750	0.7542	0.7595
0.0789	76.0	760	0.6179	0.8165
0.1031	77.0	770	0.5439	0.8354
0.0724	78.0	780	0.6302	0.8165
0.0695	79.0	790	0.6113	0.7975
0.1089	80.0	800	0.7490	0.7532
0.0714	81.0	810	0.6824	0.8038
0.09	82.0	820	0.5732	0.8165
0.0962	83.0	830	0.6818	0.7785
0.0614	84.0	840	0.5182	0.8165
0.0685	85.0	850	0.6753	0.7532
0.0861	86.0	860	0.5541	0.8228
0.09	87.0	870	0.7829	0.7658
0.0565	88.0	880	0.7735	0.7595
0.0574	89.0	890	0.6467	0.8038
0.0431	90.0	900	0.6314	0.8038
0.091	91.0	910	0.6226	0.8038
0.055	92.0	920	0.7533	0.7785
0.0776	93.0	930	0.6564	0.7975
0.056	94.0	940	0.6182	0.8038
0.0683	95.0	950	0.5490	0.8228
0.0695	96.0	960	0.6460	0.7911
0.0464	97.0	970	0.6381	0.7975
0.0483	98.0	980	0.5261	0.8608
0.0487	99.0	990	0.5322	0.8291
0.0537	100.0	1000	0.6404	0.8101

Framework versions

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