meat_calssify_fresh_crop_V_0_1

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: 1.5238
• Accuracy: 0.7742

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: 1
• 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
0.9291	1.0	617	1.0099	0.5613
1.0473	2.0	1234	1.2180	0.4323
0.7998	3.0	1851	1.0517	0.5548
1.21	4.0	2468	1.3208	0.4968
1.0814	5.0	3085	1.3423	0.5290
1.4674	6.0	3702	1.5941	0.5419
0.0633	7.0	4319	1.6778	0.5548
0.8345	8.0	4936	1.4949	0.5419
1.6888	9.0	5553	1.6784	0.4581
1.0997	10.0	6170	1.9437	0.5097
1.6235	11.0	6787	1.0796	0.6194
1.3159	12.0	7404	1.7363	0.5935
0.5513	13.0	8021	1.6918	0.6387
0.4463	14.0	8638	1.8225	0.5742
0.0455	15.0	9255	1.6463	0.6258
0.5906	16.0	9872	2.0199	0.6065
0.6373	17.0	10489	1.7233	0.6516
1.0226	18.0	11106	1.4137	0.6710
1.0566	19.0	11723	1.3841	0.7097
0.3117	20.0	12340	1.9925	0.6258
0.3041	21.0	12957	1.6802	0.6710
1.0978	22.0	13574	1.8269	0.6323
1.0631	23.0	14191	1.7333	0.6645
0.0039	24.0	14808	1.8621	0.6645
0.0018	25.0	15425	2.1279	0.6258
0.2373	26.0	16042	1.6896	0.7032
0.5993	27.0	16659	1.8411	0.6710
0.0021	28.0	17276	1.9047	0.6645
0.0401	29.0	17893	1.7597	0.7226
0.3687	30.0	18510	2.2599	0.6387
0.2841	31.0	19127	2.0735	0.6581
0.5707	32.0	19744	1.9316	0.6581
0.46	33.0	20361	2.1935	0.6452
0.7589	34.0	20978	1.5770	0.7355
0.0008	35.0	21595	1.7779	0.7226
0.0005	36.0	22212	2.0994	0.6774
0.0114	37.0	22829	1.7210	0.7161
0.4071	38.0	23446	2.2015	0.6839
0.0007	39.0	24063	1.8165	0.7161
0.0005	40.0	24680	1.8461	0.7032
0.0005	41.0	25297	1.9554	0.7032
0.7269	42.0	25914	1.6843	0.7355
0.0008	43.0	26531	1.9346	0.7032
0.5102	44.0	27148	1.7949	0.7161
1.1679	45.0	27765	1.7431	0.7484
0.151	46.0	28382	1.7899	0.7355
0.0004	47.0	28999	1.5098	0.7548
0.0003	48.0	29616	1.7388	0.7419
0.0003	49.0	30233	1.5232	0.7677
0.0003	50.0	30850	1.5238	0.7742

Framework versions

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