meat_calssify_fresh_crop_fixed_epoch100_V_0_2

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: 0.7219
• Accuracy: 0.7975

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
1.0968	1.0	10	1.0907	0.3797
1.0804	2.0	20	1.0759	0.3924
1.0578	3.0	30	1.0750	0.4241
1.0273	4.0	40	1.0443	0.4684
0.9866	5.0	50	1.0325	0.4747
0.9234	6.0	60	0.9837	0.5886
0.8597	7.0	70	0.9564	0.5443
0.8042	8.0	80	0.9315	0.5633
0.8463	9.0	90	0.9334	0.5380
0.7795	10.0	100	0.9305	0.5443
0.7375	11.0	110	0.8787	0.6076
0.6489	12.0	120	0.8685	0.6392
0.5958	13.0	130	0.8133	0.6582
0.5308	14.0	140	0.8563	0.6519
0.5206	15.0	150	0.7902	0.6709
0.4617	16.0	160	0.8114	0.6456
0.4338	17.0	170	0.8134	0.6646
0.454	18.0	180	0.7283	0.6772
0.5094	19.0	190	0.7035	0.6962
0.4133	20.0	200	0.7652	0.6835
0.3504	21.0	210	0.7225	0.7089
0.3602	22.0	220	0.8140	0.6582
0.32	23.0	230	0.7057	0.7278
0.2849	24.0	240	0.7051	0.6899
0.3051	25.0	250	0.7805	0.7025
0.3099	26.0	260	0.7456	0.6772
0.3305	27.0	270	0.7802	0.6646
0.2508	28.0	280	0.7222	0.7152
0.2842	29.0	290	0.6745	0.7278
0.2584	30.0	300	0.6029	0.7658
0.2324	31.0	310	0.6066	0.7911
0.3014	32.0	320	0.7253	0.7215
0.2279	33.0	330	0.7050	0.7089
0.2363	34.0	340	0.7361	0.7785
0.2085	35.0	350	0.6596	0.7658
0.1808	36.0	360	0.7104	0.7532
0.2051	37.0	370	0.7471	0.7152
0.1911	38.0	380	0.8262	0.7025
0.2027	39.0	390	0.7785	0.7532
0.1944	40.0	400	0.8136	0.6835
0.1627	41.0	410	0.8254	0.7152
0.1619	42.0	420	0.8766	0.6772
0.1619	43.0	430	0.6940	0.7405
0.1635	44.0	440	0.8477	0.7215
0.1323	45.0	450	0.6644	0.7848
0.1253	46.0	460	0.7747	0.7468
0.1254	47.0	470	0.9075	0.6962
0.1494	48.0	480	0.8104	0.7405
0.1702	49.0	490	0.7167	0.7532
0.1591	50.0	500	0.8214	0.6962
0.1105	51.0	510	0.9359	0.7152
0.1354	52.0	520	0.7214	0.7342
0.119	53.0	530	0.7825	0.7342
0.0841	54.0	540	0.7528	0.7595
0.12	55.0	550	0.7002	0.7658
0.1096	56.0	560	0.7747	0.7785
0.1192	57.0	570	0.7368	0.7532
0.1268	58.0	580	0.7098	0.7722
0.1351	59.0	590	0.6097	0.7848
0.1248	60.0	600	0.8102	0.7215
0.1378	61.0	610	0.6786	0.7405
0.1208	62.0	620	0.5467	0.8101
0.0786	63.0	630	0.7059	0.7785
0.1048	64.0	640	0.7945	0.7278
0.0954	65.0	650	0.8258	0.7278
0.121	66.0	660	0.7267	0.7532
0.0921	67.0	670	0.5914	0.7911
0.092	68.0	680	0.6923	0.7722
0.1153	69.0	690	0.6655	0.8038
0.0987	70.0	700	0.6774	0.7722
0.0797	71.0	710	0.6143	0.7975
0.0842	72.0	720	0.7301	0.7595
0.0707	73.0	730	0.7614	0.7405
0.0848	74.0	740	0.7578	0.7785
0.0853	75.0	750	0.7785	0.7405
0.0761	76.0	760	0.8719	0.7532
0.1019	77.0	770	0.5698	0.8165
0.0747	78.0	780	0.7956	0.7278
0.0657	79.0	790	0.5792	0.7975
0.0969	80.0	800	0.5721	0.8101
0.0597	81.0	810	0.7171	0.7785
0.0787	82.0	820	0.7493	0.7595
0.0823	83.0	830	0.6758	0.8038
0.0828	84.0	840	0.8082	0.7722
0.0693	85.0	850	0.7310	0.7911
0.074	86.0	860	0.6492	0.8228
0.0736	87.0	870	0.7373	0.7785
0.0763	88.0	880	0.7254	0.7848
0.0823	89.0	890	0.8261	0.7785
0.0614	90.0	900	0.6919	0.7911
0.0916	91.0	910	0.5884	0.7975
0.0539	92.0	920	0.6960	0.7658
0.0604	93.0	930	0.6502	0.7975
0.0596	94.0	940	0.6058	0.7975
0.0599	95.0	950	0.7166	0.7785
0.0452	96.0	960	0.8093	0.7658
0.0556	97.0	970	0.6589	0.8354
0.0675	98.0	980	0.7471	0.8101
0.0581	99.0	990	0.6568	0.8038
0.0515	100.0	1000	0.7219	0.7975

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0
• Datasets 2.19.2
• Tokenizers 0.19.1