meat_calssify_fresh_crop_fixed_epoch100_V_0_5

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: 0.5296
• Accuracy: 0.8291

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
1.0932	1.0	10	1.0897	0.4241
1.0783	2.0	20	1.0809	0.4494
1.0601	3.0	30	1.0457	0.5253
1.0246	4.0	40	1.0220	0.5316
0.9789	5.0	50	0.9556	0.5759
0.9271	6.0	60	0.8963	0.6076
0.8912	7.0	70	0.8881	0.6266
0.8307	8.0	80	0.7793	0.6646
0.7731	9.0	90	0.8021	0.6392
0.7522	10.0	100	0.7848	0.6582
0.6969	11.0	110	0.7099	0.6899
0.6233	12.0	120	0.6720	0.7532
0.5671	13.0	130	0.6675	0.7215
0.5255	14.0	140	0.6834	0.7152
0.5494	15.0	150	0.6656	0.7405
0.5031	16.0	160	0.5975	0.7532
0.4578	17.0	170	0.6668	0.7342
0.4514	18.0	180	0.6415	0.7658
0.3764	19.0	190	0.6383	0.7152
0.373	20.0	200	0.5907	0.7658
0.4071	21.0	210	0.5909	0.7532
0.3669	22.0	220	0.6417	0.7595
0.3172	23.0	230	0.6713	0.7342
0.3585	24.0	240	0.6214	0.7532
0.3713	25.0	250	0.7105	0.7152
0.3773	26.0	260	0.5745	0.7785
0.3167	27.0	270	0.5214	0.8038
0.3192	28.0	280	0.6045	0.7532
0.2735	29.0	290	0.5424	0.7975
0.2327	30.0	300	0.5455	0.7911
0.2561	31.0	310	0.5763	0.7532
0.233	32.0	320	0.5876	0.7595
0.2188	33.0	330	0.4700	0.8101
0.2528	34.0	340	0.5753	0.7848
0.2115	35.0	350	0.4716	0.8165
0.2103	36.0	360	0.5390	0.7975
0.193	37.0	370	0.5002	0.8038
0.1899	38.0	380	0.6283	0.7722
0.2473	39.0	390	0.5941	0.7911
0.177	40.0	400	0.4720	0.8544
0.1926	41.0	410	0.5397	0.8038
0.1558	42.0	420	0.5941	0.7722
0.1821	43.0	430	0.4703	0.8038
0.1507	44.0	440	0.5470	0.8228
0.1871	45.0	450	0.4939	0.8038
0.2069	46.0	460	0.4735	0.8228
0.1558	47.0	470	0.4094	0.8418
0.1604	48.0	480	0.5314	0.8038
0.168	49.0	490	0.5669	0.7975
0.1274	50.0	500	0.5027	0.8291
0.157	51.0	510	0.5210	0.8165
0.1574	52.0	520	0.5325	0.8038
0.113	53.0	530	0.5049	0.8165
0.1184	54.0	540	0.5178	0.8228
0.0908	55.0	550	0.6050	0.8038
0.1298	56.0	560	0.5167	0.8291
0.129	57.0	570	0.6349	0.7848
0.1896	58.0	580	0.5775	0.8228
0.1204	59.0	590	0.5537	0.8101
0.1285	60.0	600	0.6127	0.7722
0.1187	61.0	610	0.5656	0.8038
0.1234	62.0	620	0.5230	0.8101
0.1172	63.0	630	0.5435	0.8165
0.0906	64.0	640	0.4562	0.8734
0.0917	65.0	650	0.4852	0.8101
0.1097	66.0	660	0.5314	0.8228
0.134	67.0	670	0.5456	0.8228
0.0823	68.0	680	0.4863	0.8354
0.0997	69.0	690	0.5733	0.8228
0.1118	70.0	700	0.5084	0.8291
0.1505	71.0	710	0.4201	0.8734
0.1071	72.0	720	0.5167	0.8165
0.1006	73.0	730	0.4861	0.8101
0.0904	74.0	740	0.4193	0.8608
0.0825	75.0	750	0.5001	0.8418
0.086	76.0	760	0.3372	0.8797
0.0727	77.0	770	0.4712	0.8544
0.0779	78.0	780	0.5063	0.8418
0.0858	79.0	790	0.5910	0.8354
0.104	80.0	800	0.4938	0.8544
0.085	81.0	810	0.6679	0.7785
0.0782	82.0	820	0.5140	0.8481
0.0729	83.0	830	0.4257	0.8354
0.0712	84.0	840	0.5314	0.8101
0.0663	85.0	850	0.5001	0.8291
0.0683	86.0	860	0.3963	0.8924
0.0854	87.0	870	0.5039	0.8481
0.077	88.0	880	0.4131	0.8608
0.0683	89.0	890	0.4037	0.8671
0.0598	90.0	900	0.3973	0.8797
0.0692	91.0	910	0.4297	0.8734
0.0808	92.0	920	0.5091	0.8481
0.0529	93.0	930	0.4877	0.8418
0.0597	94.0	940	0.5055	0.8418
0.0551	95.0	950	0.4669	0.8481
0.0548	96.0	960	0.4825	0.8608
0.0922	97.0	970	0.4931	0.8481
0.0665	98.0	980	0.4109	0.8734
0.0471	99.0	990	0.4905	0.8544
0.0479	100.0	1000	0.5296	0.8291

Framework versions

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