meat_calssify_fresh_crop_fixed_epoch100_V_0_4

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: 0.5888
• Accuracy: 0.8418

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0919	1.0	10	1.0799	0.3481
1.0793	2.0	20	1.0692	0.4051
1.0529	3.0	30	1.0517	0.4620
1.0236	4.0	40	1.0159	0.5
0.9714	5.0	50	0.9742	0.5506
0.9373	6.0	60	0.9423	0.5949
0.8579	7.0	70	0.9193	0.5443
0.8111	8.0	80	0.8724	0.6329
0.7735	9.0	90	0.8477	0.6329
0.7259	10.0	100	0.8027	0.6329
0.6595	11.0	110	0.7633	0.6646
0.6254	12.0	120	0.7865	0.6582
0.5348	13.0	130	0.7159	0.7025
0.5617	14.0	140	0.8024	0.6519
0.577	15.0	150	0.8290	0.6582
0.48	16.0	160	0.8198	0.6139
0.4578	17.0	170	0.7113	0.7215
0.4349	18.0	180	0.7722	0.6835
0.3965	19.0	190	0.6600	0.7152
0.3775	20.0	200	0.7357	0.7089
0.3396	21.0	210	0.7999	0.6392
0.3559	22.0	220	0.7464	0.7342
0.3271	23.0	230	0.7684	0.6709
0.3522	24.0	240	0.6659	0.7025
0.2631	25.0	250	0.7284	0.6899
0.2456	26.0	260	0.5699	0.7848
0.2557	27.0	270	0.6237	0.7722
0.2807	28.0	280	0.7020	0.7152
0.2434	29.0	290	0.8912	0.6519
0.2591	30.0	300	0.6464	0.7658
0.2519	31.0	310	0.7927	0.6899
0.2253	32.0	320	0.6960	0.7468
0.1842	33.0	330	0.6849	0.7278
0.2762	34.0	340	0.6524	0.7532
0.1925	35.0	350	0.6154	0.7532
0.175	36.0	360	0.7486	0.7215
0.2422	37.0	370	0.7183	0.7342
0.1908	38.0	380	0.5739	0.7595
0.1694	39.0	390	0.7914	0.7152
0.1955	40.0	400	0.7209	0.7089
0.2208	41.0	410	0.9125	0.6709
0.2009	42.0	420	0.6512	0.7595
0.2089	43.0	430	0.7874	0.7152
0.1622	44.0	440	0.6903	0.7342
0.1685	45.0	450	0.6869	0.7658
0.1322	46.0	460	0.7294	0.7722
0.1475	47.0	470	0.6547	0.7848
0.155	48.0	480	0.6317	0.8038
0.1491	49.0	490	0.8127	0.7152
0.1534	50.0	500	0.7171	0.7595
0.1721	51.0	510	0.6840	0.7658
0.1408	52.0	520	0.6608	0.7785
0.1512	53.0	530	0.7649	0.7405
0.1279	54.0	540	0.6965	0.7595
0.1338	55.0	550	0.7497	0.7342
0.1249	56.0	560	0.6094	0.8038
0.1358	57.0	570	0.6515	0.7785
0.1105	58.0	580	0.5354	0.8038
0.1222	59.0	590	0.6427	0.7848
0.1137	60.0	600	0.5940	0.7975
0.0942	61.0	610	0.8780	0.7278
0.1082	62.0	620	0.6781	0.7785
0.1017	63.0	630	0.6201	0.7848
0.0803	64.0	640	0.6029	0.7785
0.111	65.0	650	0.7350	0.7532
0.1146	66.0	660	0.4995	0.8228
0.115	67.0	670	0.6887	0.7911
0.1038	68.0	680	0.4541	0.8418
0.0999	69.0	690	0.6961	0.7722
0.0891	70.0	700	0.4988	0.8228
0.0928	71.0	710	0.5811	0.7848
0.0926	72.0	720	0.5424	0.8354
0.0798	73.0	730	0.6707	0.7975
0.0724	74.0	740	0.5846	0.8038
0.0853	75.0	750	0.5979	0.8101
0.0822	76.0	760	0.6966	0.7595
0.0805	77.0	770	0.6735	0.7595
0.1131	78.0	780	0.7966	0.7532
0.0709	79.0	790	0.6752	0.7975
0.0726	80.0	800	0.5556	0.8038
0.1127	81.0	810	0.5609	0.7975
0.0558	82.0	820	0.6215	0.8101
0.1057	83.0	830	0.6139	0.7848
0.0702	84.0	840	0.6602	0.7975
0.0641	85.0	850	0.5445	0.8354
0.0511	86.0	860	0.6324	0.8228
0.0728	87.0	870	0.6474	0.8101
0.0724	88.0	880	0.6492	0.7848
0.0552	89.0	890	0.5549	0.8291
0.0587	90.0	900	0.5619	0.8228
0.0539	91.0	910	0.5222	0.8481
0.0524	92.0	920	0.6652	0.7911
0.0499	93.0	930	0.7568	0.7658
0.0717	94.0	940	0.6916	0.7911
0.0481	95.0	950	0.5951	0.8228
0.0599	96.0	960	0.5053	0.8418
0.0655	97.0	970	0.6685	0.7785
0.0621	98.0	980	0.5767	0.8291
0.0543	99.0	990	0.5964	0.8101
0.0451	100.0	1000	0.5888	0.8418

Framework versions

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