meat_calssify_fresh_crop_V_0_3

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: 1.1160
• Accuracy: 0.7161

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: 16
• 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.09	1.0	39	1.0822	0.4710
1.0215	2.0	78	0.9976	0.5419
0.9299	3.0	117	0.9265	0.5548
0.8981	4.0	156	0.8926	0.5935
0.8256	5.0	195	0.9415	0.5548
0.7763	6.0	234	0.9029	0.5935
0.6964	7.0	273	0.8402	0.5935
0.678	8.0	312	0.8272	0.6
0.6114	9.0	351	0.9511	0.5935
0.5694	10.0	390	0.7493	0.6645
0.5335	11.0	429	0.8895	0.6452
0.4437	12.0	468	0.7902	0.6774
0.4836	13.0	507	0.8206	0.6387
0.4167	14.0	546	0.8594	0.6710
0.3775	15.0	585	0.8840	0.6645
0.3132	16.0	624	0.7669	0.6710
0.3099	17.0	663	0.8012	0.6903
0.307	18.0	702	0.8098	0.6839
0.2905	19.0	741	0.7889	0.7226
0.2854	20.0	780	0.8555	0.6968
0.1875	21.0	819	0.8501	0.7097
0.2485	22.0	858	0.8381	0.7419
0.22	23.0	897	1.0090	0.6774
0.2283	24.0	936	0.9999	0.6323
0.1934	25.0	975	0.9455	0.7097
0.1841	26.0	1014	0.7737	0.7484
0.1711	27.0	1053	0.8872	0.7355
0.1579	28.0	1092	1.0535	0.6903
0.176	29.0	1131	0.9783	0.6968
0.2307	30.0	1170	0.8435	0.7226
0.1379	31.0	1209	0.9598	0.7097
0.1181	32.0	1248	0.9325	0.7419
0.1529	33.0	1287	1.0973	0.6839
0.1252	34.0	1326	0.8859	0.7484
0.1005	35.0	1365	0.9212	0.7613
0.1446	36.0	1404	0.7894	0.7806
0.0776	37.0	1443	0.9259	0.7484
0.1067	38.0	1482	1.0468	0.7226
0.0983	39.0	1521	0.9468	0.7355
0.1155	40.0	1560	1.0564	0.7226
0.1037	41.0	1599	1.0964	0.6968
0.102	42.0	1638	0.9690	0.7290
0.0904	43.0	1677	0.9662	0.7419
0.0577	44.0	1716	1.2786	0.6645
0.1086	45.0	1755	1.0993	0.7226
0.0698	46.0	1794	1.1927	0.7032
0.0532	47.0	1833	0.9616	0.7484
0.0705	48.0	1872	0.7846	0.7806
0.0611	49.0	1911	0.9952	0.7290
0.0769	50.0	1950	1.1160	0.7161

Framework versions

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