meat_calssify_fresh_crop_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: 1.3588
• Accuracy: 0.7935

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
1.0748	1.0	617	0.9992	0.5290
1.0075	2.0	1234	1.1657	0.4774
1.1387	3.0	1851	1.7110	0.3355
1.2272	4.0	2468	1.0090	0.5032
1.1872	5.0	3085	1.0040	0.5871
1.1936	6.0	3702	1.4017	0.5484
1.1596	7.0	4319	1.2642	0.5935
1.1932	8.0	4936	1.5967	0.5161
1.1215	9.0	5553	1.4047	0.6194
1.097	10.0	6170	1.2934	0.5806
1.0044	11.0	6787	1.3448	0.6323
0.8965	12.0	7404	1.3799	0.6645
0.9943	13.0	8021	1.3963	0.6581
0.9558	14.0	8638	1.7787	0.5742
0.8692	15.0	9255	1.4618	0.6516
0.8126	16.0	9872	1.2486	0.7097
0.8093	17.0	10489	1.1204	0.7226
0.6681	18.0	11106	1.7933	0.6452
0.5894	19.0	11723	1.2285	0.7355
0.4993	20.0	12340	1.7193	0.6516
0.4736	21.0	12957	1.7766	0.6774
0.4952	22.0	13574	1.8535	0.6516
0.5312	23.0	14191	1.7282	0.6774
0.3942	24.0	14808	1.9881	0.6516
0.4166	25.0	15425	1.6196	0.6903
0.4656	26.0	16042	1.6721	0.6774
0.326	27.0	16659	2.1897	0.6387
0.3692	28.0	17276	1.6734	0.6968
0.3956	29.0	17893	1.6272	0.7290
0.3108	30.0	18510	1.6487	0.7548
0.2318	31.0	19127	1.8501	0.6968
0.3835	32.0	19744	1.7877	0.6774
0.3779	33.0	20361	1.4747	0.7355
0.2913	34.0	20978	1.6122	0.7355
0.275	35.0	21595	1.9130	0.6839
0.2483	36.0	22212	1.9305	0.6839
0.2389	37.0	22829	1.5973	0.7419
0.3375	38.0	23446	1.2684	0.7742
0.1867	39.0	24063	1.7777	0.7484
0.2066	40.0	24680	1.5134	0.7613
0.2555	41.0	25297	1.9315	0.6839
0.1508	42.0	25914	1.6165	0.7484
0.1262	43.0	26531	1.8465	0.7290
0.1113	44.0	27148	2.0645	0.7161
0.1692	45.0	27765	1.7539	0.7484
0.0973	46.0	28382	2.0104	0.7161
0.166	47.0	28999	1.6435	0.7742
0.1476	48.0	29616	1.6865	0.7677
0.1616	49.0	30233	2.1759	0.6903
0.1715	50.0	30850	1.3588	0.7935

Framework versions

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