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README.md

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 ---
-title: Skin Cancer Classification
-emoji: 📚
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.35.0
-app_file: app.py
-pinned: false
-license: mit
+license: apache-2.0
 ---
+# Skin Cancer Image Classification Model
+
+## Introduction
+
+This model is designed for the classification of skin cancer images into various categories including benign keratosis-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, melanocytic nevi, melanoma, and dermatofibroma.
+
+## Model Overview
+
+- Model Architecture: Vision Transformer (ViT)
+- Pre-trained Model: Google's ViT with 16x16 patch size and trained on ImageNet21k dataset
+- Modified Classification Head: The classification head has been replaced to adapt the model to the skin cancer classification task.
+
+## Dataset
+
+- Dataset Name: Skin Cancer Dataset
+- Source: [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
+
+- Epoch 1/5, Train Loss: 0.7168, Train Accuracy: 0.7586, Val Loss: 0.4994, Val Accuracy: 0.8355
+- Epoch 2/5, Train Loss: 0.4550, Train Accuracy: 0.8466, Val Loss: 0.3237, Val Accuracy: 0.8973
+- Epoch 3/5, Train Loss: 0.2959, Train Accuracy: 0.9028, Val Loss: 0.1790, Val Accuracy: 0.9530
+- Epoch 4/5, Train Loss: 0.1595, Train Accuracy: 0.9482, Val Loss: 0.1498, Val Accuracy: 0.9555
+- Epoch 5/5, Train Loss: 0.1208, Train Accuracy: 0.9614, Val Loss: 0.1000, Val Accuracy: 0.9695
+## Conclusion
+
+The model demonstrates good performance in classifying skin cancer images into various categories. Further fine-tuning or experimentation may improve performance on this task.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

config.json

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+{
+  "_name_or_path": "google/vit-base-patch16-224-in21k",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "benign_keratosis-like_lesions",
+    "1": "basal_cell_carcinoma",
+    "2": "actinic_keratoses",
+    "3": "vascular_lesions",
+    "4": "melanocytic_Nevi",
+    "5": "melanoma",
+    "6": "dermatofibroma"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "benign_keratosis-like_lesions": 0,
+    "basal_cell_carcinoma": 1,
+    "actinic_keratoses": 2,
+    "vascular_lesions": 3,
+    "melanocytic_Nevi": 4,
+    "melanoma": 5,
+    "dermatofibroma": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2"
+}

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preprocessor_config.json

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+{
+  "_name_or_path": "google/vit-base-patch16-224-in21k",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "benign_keratosis-like_lesions",
+    "1": "basal_cell_carcinoma",
+    "2": "actinic_keratoses",
+    "3": "vascular_lesions",
+    "4": "melanocytic_Nevi",
+    "5": "melanoma",
+    "6": "dermatofibroma"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "benign_keratosis-like_lesions": 0,
+    "basal_cell_carcinoma": 1,
+    "actinic_keratoses": 2,
+    "vascular_lesions": 3,
+    "melanocytic_Nevi": 4,
+    "melanoma": 5,
+    "dermatofibroma": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2"
+}