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license: apache-2.0 |
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pipeline_tag: image-classification |
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# Skin Cancer Image Classification Model |
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## Introduction |
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Esse modelo classifica imagens de pele em várias categorias, com o objetivo de detectar lesões cancerígenas |
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## Model Overview |
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- Arquitetura: Vision Transformer (ViT) |
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- Modelo Pré-treinado: Google's ViT 16x16 treinado no dataset ImageNet21k |
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- Classification Head Modificada: A classification head foi trocado para adaptar melhor o modelo à nova tarefa |
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## Dataset |
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- Nome do Dataset: Skin Cancer Dataset HAM10000 |
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- Classes: Benign keratosis-like lesions, Basal cell carcinoma, Actinic keratoses, Vascular lesions, Melanocytic nevi, Melanoma, Dermatofibroma |
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## Training |
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- Optimizer: Adam optimizer with a learning rate of 1e-4 |
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- Loss Function: Cross-Entropy Loss |
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- Batch Size: 32 |
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- Number of Epochs: 5 |
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## Evaluation Metrics |
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- Train Loss: Average loss over the training dataset |
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- Train Accuracy: Accuracy over the training dataset |
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- Validation Loss: Average loss over the validation dataset |
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- Validation Accuracy: Accuracy over the validation dataset |
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## Results |
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- Train Loss: 0.1208 |
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- Train Accuracy: 0.9614 |
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- Val Loss: 0.1000 |
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- Val Accuracy: 0.9695 |
