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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# 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
+- Fonte: [Marmal88's Skin Cancer Dataset on Hugging Face](https://huggingface.co/datasets/marmal88/skin_cancer)
+- 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