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-epsilon-1e-9
    results: []

vit-epsilon-1e-9

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: 0.5143
Accuracy: 0.8131
Precision: 0.8359
Recall: 0.8131
F1: 0.8206

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=(0.9,0.99) and epsilon=1e-09
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1733
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.7127	1.0	321	0.9579	0.6987	0.6581	0.6987	0.6331
1.1596	2.0	642	0.7026	0.7299	0.7442	0.7299	0.7299
1.0337	3.0	963	0.6560	0.7549	0.7647	0.7549	0.7356
0.9695	4.0	1284	0.5708	0.7656	0.7963	0.7656	0.7758
0.9137	5.0	1605	0.6460	0.7611	0.7828	0.7611	0.7625
1.0053	6.0	1926	0.6020	0.7673	0.8049	0.7673	0.7797
0.896	7.0	2247	0.7087	0.7271	0.8055	0.7271	0.7477
0.7646	8.0	2568	0.6615	0.7441	0.8134	0.7441	0.7622
0.7262	9.0	2889	0.5611	0.7975	0.8190	0.7975	0.7985
0.7025	10.0	3210	0.5338	0.7975	0.8278	0.7975	0.8058
0.6138	11.0	3531	0.5143	0.8131	0.8359	0.8131	0.8206
0.5582	12.0	3852	0.6157	0.7864	0.8253	0.7864	0.7992
0.4736	13.0	4173	0.6899	0.8117	0.8253	0.8117	0.8007
0.4581	14.0	4494	0.6062	0.8128	0.8447	0.8128	0.8199
0.407	15.0	4815	0.5317	0.8308	0.8410	0.8308	0.8339
0.369	16.0	5136	0.6475	0.8197	0.8414	0.8197	0.8270
0.2855	17.0	5457	0.5153	0.8617	0.8572	0.8617	0.8578
0.2545	18.0	5778	0.5455	0.8436	0.8555	0.8436	0.8473
0.2221	19.0	6099	0.5955	0.8471	0.8624	0.8471	0.8516
0.2143	20.0	6420	0.5772	0.8575	0.8604	0.8575	0.8582
0.1619	21.0	6741	0.6021	0.8627	0.8567	0.8627	0.8572

Framework versions

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