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README.md

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+# Breast Tissue ROI Object Detection Model
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+<!-- Provide a quick summary of what the model is/does. -->
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+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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+## Introduction:
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+The Breast Tissue ROI Object Detection Model is a specialized deep learning model designed to identify and locate regions of interest (ROIs) within mammographic images. This model is part of the Hugging Face Model Zoo and has been developed to assist in the early detection of breast cancer, a critical medical application that can significantly improve patient outcomes.
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+### 1. Purpose
+The primary purpose of the Breast Tissue ROI Object Detection Model is to accurately and efficiently identify regions of interest in mammographic images. These regions typically contain suspicious lesions, calcifications, or abnormalities that require further examination to determine the presence of breast cancer.
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+## 2. Deep Learning Architecture:
+This model is built on state-of-the-art deep learning architecture, leveraging Convolutional Neural Networks (CNNs) (with feature extraction). It utilizes a combination of convolutional layers, pooling layers, and fully connected layers to process mammographic images effectively.
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+- **Developed by:** https://github.com/RsGoksel
+- **Model type:** Pytorch (.pt)
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+### More Tools 
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+- **Repository:** https://github.com/RsGoksel/Breast-Tissue-Cropper-Tools
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