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-dropout-0.4
    results: []

vit-dropout-0.4

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: 0.4924
Accuracy: 0.8291
Precision: 0.8250
Recall: 0.8291
F1: 0.8244

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: 1219
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.6459	1.0	321	0.8160	0.7292	0.7119	0.7292	0.6910
1.0644	2.0	642	0.6323	0.7514	0.7687	0.7514	0.7575
1.0213	3.0	963	0.6378	0.7635	0.7718	0.7635	0.7422
0.9727	4.0	1284	0.6045	0.7438	0.7972	0.7438	0.7594
0.9062	5.0	1605	0.5953	0.7902	0.7965	0.7902	0.7802
0.8719	6.0	1926	0.6095	0.7743	0.8084	0.7743	0.7839
0.7537	7.0	2247	0.5970	0.7639	0.8125	0.7639	0.7778
0.677	8.0	2568	0.7108	0.7074	0.8148	0.7074	0.7301
0.6638	9.0	2889	0.4924	0.8291	0.8250	0.8291	0.8244
0.5787	10.0	3210	0.5415	0.8162	0.8406	0.8162	0.8222
0.5373	11.0	3531	0.5298	0.8103	0.8409	0.8103	0.8189
0.4923	12.0	3852	0.5428	0.8117	0.8444	0.8117	0.8213
0.3798	13.0	4173	0.4968	0.8499	0.8470	0.8499	0.8467
0.3912	14.0	4494	0.5339	0.8443	0.8531	0.8443	0.8460
0.3002	15.0	4815	0.5219	0.8450	0.8548	0.8450	0.8481
0.2744	16.0	5136	0.6369	0.8204	0.8482	0.8204	0.8280
0.2251	17.0	5457	0.5156	0.8571	0.8561	0.8571	0.8556
0.2187	18.0	5778	0.5825	0.8457	0.8550	0.8457	0.8491
0.1767	19.0	6099	0.5693	0.8526	0.8605	0.8526	0.8551

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2