pre_CIDAUTv4

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0177
• Accuracy: 0.9918

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.8889	4	0.6742	0.5679
No log	2.0	9	0.3347	0.9218
0.6003	2.8889	13	0.1238	0.9753
0.6003	4.0	18	0.1298	0.9465
0.199	4.8889	22	0.0360	0.9877
0.199	6.0	27	0.1049	0.9671
0.0832	6.8889	31	0.0058	1.0
0.0832	8.0	36	0.0138	0.9918
0.0438	8.8889	40	0.0177	0.9918

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.20.0