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: 1.2826
• Accuracy: 0.625

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.071	1.0	10	2.0532	0.2125
1.9763	2.0	20	1.9614	0.3312
1.8031	3.0	30	1.8326	0.4562
1.6168	4.0	40	1.7015	0.5125
1.4508	5.0	50	1.6065	0.5188
1.3037	6.0	60	1.5397	0.5375
1.1709	7.0	70	1.4836	0.55
1.0481	8.0	80	1.4248	0.5813
0.9441	9.0	90	1.3915	0.5625
0.8551	10.0	100	1.3586	0.6
0.7772	11.0	110	1.3315	0.6
0.7174	12.0	120	1.3057	0.6062
0.6721	13.0	130	1.2936	0.6188
0.642	14.0	140	1.2933	0.6
0.6252	15.0	150	1.2826	0.625

Framework versions

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