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: 0.5742
Accuracy: 0.8228

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
1.0985	1.0	10	1.1041	0.2975
1.0824	2.0	20	1.0871	0.3544
1.0624	3.0	30	1.0830	0.3418
1.0341	4.0	40	1.0578	0.3797
0.9842	5.0	50	1.0021	0.4873
0.9433	6.0	60	1.0362	0.4557
0.8904	7.0	70	0.9132	0.5886
0.8515	8.0	80	0.8612	0.6582
0.8094	9.0	90	0.8405	0.6646
0.732	10.0	100	0.8722	0.5886
0.7408	11.0	110	0.7310	0.7215
0.6286	12.0	120	0.7742	0.6456
0.58	13.0	130	0.8024	0.6646
0.5519	14.0	140	0.7325	0.6835
0.498	15.0	150	0.6873	0.7215
0.4682	16.0	160	0.6757	0.7152
0.4201	17.0	170	0.7193	0.7025
0.4186	18.0	180	0.6616	0.7278
0.3793	19.0	190	0.6906	0.7532
0.322	20.0	200	0.7668	0.7089
0.3441	21.0	210	0.6939	0.7468
0.3146	22.0	220	0.6748	0.7342
0.3451	23.0	230	0.6401	0.7658
0.3001	24.0	240	0.6490	0.7089
0.2884	25.0	250	0.6640	0.7405
0.251	26.0	260	0.6769	0.7532
0.2386	27.0	270	0.6259	0.7595
0.236	28.0	280	0.7949	0.6962
0.2493	29.0	290	0.7442	0.7405
0.2249	30.0	300	0.7024	0.7532
0.2959	31.0	310	0.6887	0.7595
0.2601	32.0	320	0.7209	0.7025
0.2116	33.0	330	0.6414	0.7975
0.1982	34.0	340	0.8802	0.6899
0.2018	35.0	350	0.6697	0.7468
0.2038	36.0	360	0.6632	0.7532
0.2074	37.0	370	0.6776	0.7911
0.1718	38.0	380	0.5189	0.7975
0.1699	39.0	390	0.6332	0.7532
0.1563	40.0	400	0.5616	0.8038
0.1703	41.0	410	0.6547	0.7658
0.212	42.0	420	0.8257	0.7215
0.2272	43.0	430	0.6542	0.7468
0.2264	44.0	440	0.7265	0.7278
0.1506	45.0	450	0.7796	0.7468
0.1459	46.0	460	0.7290	0.7468
0.171	47.0	470	0.6025	0.7975
0.1387	48.0	480	0.7577	0.7532
0.1591	49.0	490	0.7600	0.7658
0.1378	50.0	500	0.7683	0.7468
0.1364	51.0	510	0.7009	0.7658
0.1514	52.0	520	0.6590	0.7722
0.1638	53.0	530	0.6948	0.7785
0.1263	54.0	540	0.6051	0.7785
0.1391	55.0	550	0.7105	0.7785
0.1449	56.0	560	0.6240	0.7785
0.1065	57.0	570	0.6473	0.7911
0.1704	58.0	580	0.7020	0.7848
0.1323	59.0	590	0.7223	0.7468
0.1574	60.0	600	0.7592	0.7658
0.0914	61.0	610	0.6220	0.8038
0.1664	62.0	620	0.7561	0.7658
0.1293	63.0	630	0.6690	0.7848
0.0981	64.0	640	0.7014	0.7722
0.1098	65.0	650	0.6289	0.8038
0.1615	66.0	660	0.7278	0.7532
0.1164	67.0	670	0.7004	0.7658
0.1279	68.0	680	0.7258	0.7911
0.133	69.0	690	0.5725	0.8291
0.0848	70.0	700	0.4775	0.8544
0.1125	71.0	710	0.5514	0.8165
0.0869	72.0	720	0.5685	0.7848
0.0801	73.0	730	0.6424	0.7975
0.0954	74.0	740	0.6587	0.7848
0.078	75.0	750	0.5911	0.7975
0.0913	76.0	760	0.6705	0.7911
0.0712	77.0	770	0.7348	0.8038
0.0781	78.0	780	0.7332	0.7658
0.0898	79.0	790	0.6067	0.8101
0.0715	80.0	800	0.5991	0.8038
0.0713	81.0	810	0.6865	0.7975
0.0899	82.0	820	0.6586	0.7975
0.0739	83.0	830	0.7000	0.7975
0.0861	84.0	840	0.6194	0.8101
0.0752	85.0	850	0.6037	0.7911
0.0665	86.0	860	0.5696	0.8228
0.0693	87.0	870	0.5897	0.8291
0.0736	88.0	880	0.6536	0.8228
0.0618	89.0	890	0.6509	0.8038
0.0835	90.0	900	0.6343	0.8038
0.0826	91.0	910	0.7298	0.7722
0.0736	92.0	920	0.5925	0.8165
0.0656	93.0	930	0.6565	0.8228
0.0605	94.0	940	0.5389	0.8481
0.0451	95.0	950	0.4811	0.8481
0.0807	96.0	960	0.6439	0.7848
0.0579	97.0	970	0.7767	0.7468
0.0639	98.0	980	0.5558	0.8038
0.051	99.0	990	0.6174	0.8038
0.044	100.0	1000	0.5742	0.8228

Framework versions

Transformers 4.41.2
Pytorch 2.3.0
Datasets 2.19.2
Tokenizers 0.19.1

talli96123/meat_calssify_fresh_crop_fixed_epoch100_V_0_1

meat_calssify_fresh_crop_fixed_epoch100_V_0_1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

talli96123/
meat_calssify_fresh_crop_fixed_epoch100_V_0_1