metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - image-classification
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: vit-lr-cosine-warmup
    results: []

vit-lr-cosine-warmup

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

Loss: 0.4736
Accuracy: 0.8395
Precision: 0.8318
Recall: 0.8395
F1: 0.8308

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 770
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.86	1.0	321	0.5250	0.8141	0.8100	0.8141	0.8011
0.4517	2.0	642	0.5117	0.8221	0.8347	0.8221	0.8100
0.3512	3.0	963	0.4736	0.8395	0.8318	0.8395	0.8308
0.2184	4.0	1284	0.4797	0.8568	0.8536	0.8568	0.8505
0.1264	5.0	1605	0.6212	0.8547	0.8552	0.8547	0.8530
0.0687	6.0	1926	0.7659	0.8464	0.8476	0.8464	0.8402
0.0463	7.0	2247	0.8237	0.8519	0.8546	0.8519	0.8469
0.0373	8.0	2568	0.8712	0.8377	0.8493	0.8377	0.8415
0.0347	9.0	2889	0.8181	0.8568	0.8550	0.8568	0.8534
0.0263	10.0	3210	1.0705	0.8447	0.8389	0.8447	0.8308
0.0289	11.0	3531	0.9376	0.8589	0.8606	0.8589	0.8550
0.0164	12.0	3852	0.9714	0.8634	0.8611	0.8634	0.8611
0.0077	13.0	4173	1.2992	0.8398	0.8396	0.8398	0.8243

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2