SkinSense is a machine learning model for diagnosing skin diseases based on skin lesion images. It is built using the PyTorch framework and utilizes a fine-tuned ResNet101 architecture. The model can predict whether a skin lesion is benign or malignant, as well as provide a specific diagnosis for malignant lesions.

The SkinSense model is designed to assist medical professionals in diagnosing skin diseases by analyzing images of skin lesions. It was trained on a large dataset of skin lesion images with corresponding labels for diagnosis. The model is capable of differentiating between benign and malignant skin lesions and also provides a specific diagnosis for malignant cases.

The model will be uploaded later this week in .bin format and .tar.gz.

You can use the SkinSense model by installing the transformers library from Hugging Face:

You can access the pre-trained SkinSense model on Hugging Face Model Hub using the following link:

If you're interested in the details of the model training process, you can find the code and instructions in the model_training directory of the GitHub repository. The training data, data augmentation techniques, and the ResNet101 architecture are used during the training process. The model's performance metrics, including accuracy and loss, are logged during training.

The performance of the model has been evaluated on a separate test dataset to assess its accuracy and other metrics. You can find the evaluation results in the model_evaluation directory of the GitHub repository.

The inference speed of the SkinSense model depends on the hardware used for prediction. On a GPU, the model can process multiple images simultaneously, significantly improving performance. For faster inference times, we recommend using a GPU with at least 8GB of VRAM.

We welcome contributions to the SkinSense model. If you find any issues or want to enhance the model's performance, feel free to submit a pull request in the GitHub repository. Make sure to follow the code of conduct and provide clear documentation for your changes.

If you have any questions or inquiries related to the SkinSense model, you can reach out to:

The SkinSense model is released under the MIT License. See the LICENSE file for more details.