---
license: apache-2.0
pipeline_tag: image-classification
---
# Skin Cancer Image Classification Model

## Introduction

Esse modelo classifica imagens de pele em várias categorias, com o objetivo de detectar lesões cancerígenas

## Model Overview

- Arquitetura: Vision Transformer (ViT)
- Modelo Pré-treinado: Google's ViT 16x16 treinado no dataset ImageNet21k
- Classification Head Modificada: A classification head foi trocado para adaptar melhor o modelo à nova tarefa

## Dataset

- Nome do Dataset: Skin Cancer Dataset HAM10000
- Classes: Benign keratosis-like lesions, Basal cell carcinoma, Actinic keratoses, Vascular lesions, Melanocytic nevi, Melanoma, Dermatofibroma

## Training

- Optimizer: Adam optimizer with a learning rate of 1e-4
- Loss Function: Cross-Entropy Loss
- Batch Size: 32
- Number of Epochs: 5

## Evaluation Metrics

- Train Loss: Average loss over the training dataset
- Train Accuracy: Accuracy over the training dataset
- Validation Loss: Average loss over the validation dataset
- Validation Accuracy: Accuracy over the validation dataset

## Results

- Train Loss: 0.1208
- Train Accuracy: 0.9614
- Val Loss: 0.1000
- Val Accuracy: 0.9695