vit_itri_gerd

This model is a fine-tuned version of google/vit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.8160
• Accuracy: 0.8802
• Precision: 0.8811
• Recall: 0.8802
• F1: 0.8802

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: 0.0001
• train_batch_size: 24
• eval_batch_size: 4
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6915	1.0	63	0.4305	0.7904	0.7926	0.7904	0.7901
0.455	2.0	126	0.7307	0.7605	0.7836	0.7605	0.7552
0.372	3.0	189	0.4026	0.8024	0.8123	0.8024	0.8007
0.3159	4.0	252	0.3805	0.8323	0.8340	0.8323	0.8321
0.2906	5.0	315	0.4334	0.8323	0.8326	0.8323	0.8323
0.2589	6.0	378	0.4235	0.8084	0.8232	0.8084	0.8060
0.2024	7.0	441	0.4003	0.8503	0.8516	0.8503	0.8502
0.1218	8.0	504	0.6308	0.8204	0.8270	0.8204	0.8193
0.1226	9.0	567	0.5468	0.8323	0.8353	0.8323	0.8319
0.0627	10.0	630	0.7390	0.8263	0.8286	0.8263	0.8260
0.0374	11.0	693	0.8669	0.8503	0.8503	0.8503	0.8503
0.0389	12.0	756	0.6790	0.8623	0.8627	0.8623	0.8622
0.0122	13.0	819	0.8346	0.8683	0.8701	0.8683	0.8681
0.0064	14.0	882	0.7985	0.8802	0.8804	0.8802	0.8802
0.0071	15.0	945	0.8160	0.8802	0.8811	0.8802	0.8802

Framework versions

• Transformers 4.53.0.dev0
• Pytorch 2.7.1+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1