# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sreejith782/Dermacare_Skin_Lesion_classification", dtype="auto")Quick Links
Dermacare Skin Lesion Classification
Dermacare is a skin lesion classification model built using Keras. It classifies dermatoscopic images into various types of skin lesions, aiding in the early detection of skin cancer.
Model Architecture
The model is a Convolutional Neural Network (CNN) trained on the HAM10000 dataset with the following key specifications:
- Input: 224x224 RGB images
- Architecture: Keras-based CNN
- Output: 7-class classification for different types of skin lesions
Usage Example
To use the model for predictions, send an image to the inference endpoint as shown below:
import requests
API_URL = "https://api-inference.huggingface.co/models/sreejith782/Dermacare_Skin_Lesion_classification"
headers = {"Authorization": "Bearer YOUR_HUGGING_FACE_TOKEN"}
response = requests.post(API_URL, headers=headers, files={"inputs": open("path_to_image.jpg", "rb")})
print(response.json())
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sreejith782/Dermacare_Skin_Lesion_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")