image_classification

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: 1.2386
• Accuracy: 0.5625

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: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0874	1.0	10	2.0621	0.2313
2.036	2.0	20	2.0392	0.2375
1.9297	3.0	30	1.9592	0.3
1.7723	4.0	40	1.7877	0.3937
1.6184	5.0	50	1.6475	0.45
1.5407	6.0	60	1.5514	0.4875
1.4197	7.0	70	1.4967	0.4938
1.3092	8.0	80	1.4332	0.4813
1.1251	9.0	90	1.4457	0.4688
1.2081	10.0	100	1.3603	0.4938
0.9803	11.0	110	1.3501	0.5188
1.0105	12.0	120	1.3212	0.55
0.9264	13.0	130	1.2895	0.575
0.9229	14.0	140	1.2882	0.5188
0.9397	15.0	150	1.4027	0.475
0.8322	16.0	160	1.2824	0.5312
0.8185	17.0	170	1.3025	0.5
0.7592	18.0	180	1.3629	0.475
0.7416	19.0	190	1.3221	0.5437
0.6323	20.0	200	1.2714	0.5563
0.6453	21.0	210	1.3015	0.4938
0.6049	22.0	220	1.3065	0.5375
0.5919	23.0	230	1.2579	0.5375
0.5354	24.0	240	1.2428	0.55
0.6379	25.0	250	1.2884	0.5375
0.5681	26.0	260	1.2201	0.5938
0.4275	27.0	270	1.3199	0.4875
0.4791	28.0	280	1.3027	0.5312
0.4693	29.0	290	1.3737	0.4813
0.5528	30.0	300	1.3342	0.4688

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1