meat_calssify_fresh_crop_fixed_epoch120_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: 0.5969
• 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: 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: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0907	1.0	10	1.0937	0.3291
1.0777	2.0	20	1.0915	0.3354
1.0596	3.0	30	1.0801	0.3544
1.0333	4.0	40	1.0463	0.4810
0.9965	5.0	50	0.9945	0.5316
0.9588	6.0	60	0.9807	0.5127
0.9183	7.0	70	0.9944	0.5063
0.8966	8.0	80	1.0074	0.5127
0.8199	9.0	90	0.9472	0.5506
0.8089	10.0	100	0.9102	0.5886
0.7151	11.0	110	0.7697	0.6962
0.6578	12.0	120	0.9557	0.5570
0.6375	13.0	130	0.7262	0.6709
0.5994	14.0	140	0.7609	0.6709
0.5086	15.0	150	0.7112	0.7089
0.4771	16.0	160	0.6239	0.7658
0.4631	17.0	170	0.7229	0.7278
0.5158	18.0	180	0.8230	0.6835
0.4938	19.0	190	0.6708	0.7278
0.4287	20.0	200	0.5675	0.7722
0.3256	21.0	210	0.6100	0.7468
0.3555	22.0	220	0.6967	0.7342
0.3453	23.0	230	0.6273	0.7532
0.3391	24.0	240	0.7153	0.7468
0.3129	25.0	250	0.7745	0.6835
0.3296	26.0	260	0.7254	0.7089
0.2695	27.0	270	0.6191	0.7658
0.2704	28.0	280	0.6397	0.7342
0.2466	29.0	290	0.7419	0.7342
0.2655	30.0	300	0.6966	0.7532
0.2448	31.0	310	0.6931	0.7532
0.2286	32.0	320	0.5090	0.7975
0.2419	33.0	330	0.5987	0.7405
0.1965	34.0	340	0.7482	0.7405
0.197	35.0	350	0.8851	0.7089
0.1829	36.0	360	0.5874	0.7848
0.187	37.0	370	0.7249	0.7405
0.2011	38.0	380	0.6821	0.7468
0.1927	39.0	390	0.7133	0.7848
0.2043	40.0	400	0.6956	0.7468
0.1661	41.0	410	0.6911	0.7532
0.1625	42.0	420	0.7424	0.7405
0.1876	43.0	430	0.6222	0.7722
0.1865	44.0	440	0.6250	0.8101
0.1604	45.0	450	0.6950	0.7405
0.2072	46.0	460	0.8475	0.7025
0.1816	47.0	470	0.5856	0.7975
0.1683	48.0	480	0.6331	0.7911
0.1698	49.0	490	0.8147	0.7278
0.1724	50.0	500	0.5908	0.7848
0.1231	51.0	510	0.4522	0.8291
0.1121	52.0	520	0.5198	0.8291
0.1335	53.0	530	0.6377	0.7785
0.1414	54.0	540	0.6638	0.7722
0.1263	55.0	550	0.6548	0.7722
0.1427	56.0	560	0.6146	0.7785
0.1137	57.0	570	0.5344	0.8101
0.1504	58.0	580	0.7023	0.7785
0.1549	59.0	590	0.8293	0.7152
0.1142	60.0	600	0.7865	0.7658
0.1271	61.0	610	0.6282	0.8038
0.1134	62.0	620	0.7117	0.7658
0.0954	63.0	630	0.5500	0.8165
0.1011	64.0	640	0.5801	0.7911
0.0878	65.0	650	0.4268	0.8418
0.1065	66.0	660	0.6277	0.8038
0.1298	67.0	670	0.5940	0.8038
0.111	68.0	680	0.6945	0.7785
0.0955	69.0	690	0.6320	0.8038
0.0728	70.0	700	0.6484	0.7975
0.0893	71.0	710	0.5842	0.8165
0.0962	72.0	720	0.5417	0.8354
0.0963	73.0	730	0.7366	0.7848
0.1103	74.0	740	0.5413	0.8354
0.1145	75.0	750	0.6310	0.7911
0.1093	76.0	760	0.5101	0.8481
0.0934	77.0	770	0.5049	0.8101
0.0914	78.0	780	0.6828	0.7975
0.0739	79.0	790	0.8685	0.7595
0.1098	80.0	800	0.5542	0.7975
0.0748	81.0	810	0.4864	0.8354
0.0763	82.0	820	0.6708	0.7785
0.0805	83.0	830	0.5730	0.8291
0.0936	84.0	840	0.5680	0.8228
0.0664	85.0	850	0.5645	0.8228
0.0932	86.0	860	0.4601	0.8608
0.0846	87.0	870	0.7324	0.7911
0.0799	88.0	880	0.6234	0.8101
0.0707	89.0	890	0.4808	0.8544
0.0626	90.0	900	0.6119	0.7848
0.066	91.0	910	0.5173	0.8228
0.0701	92.0	920	0.7111	0.7722
0.0862	93.0	930	0.6035	0.7975
0.0397	94.0	940	0.5329	0.8418
0.095	95.0	950	0.6635	0.7911
0.0688	96.0	960	0.4878	0.8734
0.07	97.0	970	0.5253	0.8608
0.0704	98.0	980	0.4443	0.8608
0.0883	99.0	990	0.5571	0.8165
0.064	100.0	1000	0.7047	0.7911
0.0547	101.0	1010	0.6558	0.8101
0.0686	102.0	1020	0.6330	0.8165
0.0806	103.0	1030	0.5754	0.8354
0.0481	104.0	1040	0.5074	0.8544
0.0499	105.0	1050	0.6701	0.8165
0.0703	106.0	1060	0.6151	0.8291
0.0921	107.0	1070	0.5935	0.8354
0.0426	108.0	1080	0.6534	0.7975
0.0618	109.0	1090	0.5265	0.8165
0.0597	110.0	1100	0.5604	0.8354
0.0471	111.0	1110	0.5451	0.8354
0.0541	112.0	1120	0.5182	0.8544
0.0369	113.0	1130	0.5276	0.8291
0.0571	114.0	1140	0.4766	0.8354
0.0469	115.0	1150	0.6508	0.8101
0.0877	116.0	1160	0.5894	0.8418
0.0681	117.0	1170	0.4952	0.8418
0.0303	118.0	1180	0.5804	0.8418
0.0536	119.0	1190	0.7055	0.8101
0.0576	120.0	1200	0.5969	0.8165

Framework versions

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