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README.md

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+### Model Card: **PRNet 3D Face Reconstruction**
+
+**Model Name**: `PRNet_3D`
+
+#### Model Description:
+PRNet is a deep learning model for **3D face reconstruction** from a single 2D image. This model regresses a 3D position map and reconstructs dense facial landmarks from 2D inputs. The fine-tuned version of PRNet has been optimized to handle facial images more robustly in the provided domain.
+
+- **Architecture**: Position Map Regression Network (PRNet)
+- **Base Model**: Pre-trained PRNet (before fine-tuning)
+- **Training Data**: Custom dataset of 2D facial images and corresponding 3D meshes.
+- **Purpose**: The model is used for forensic investigations, facial recognition, and 3D modeling from 2D images.
+
+#### Model Details:
+- **Training**: Fine-tuned using Google Colab with TensorFlow 1.x and the PRNet architecture. The model was trained on a specific dataset of 2D face images and optimized for 3D face reconstruction.
+- **Outputs**: The model outputs a `.obj` file that contains the 3D mesh representation of the input 2D image.
+
+#### Usage:
+This model is intended for **3D face reconstruction** tasks. It takes a 2D facial image and outputs a 3D `.obj` file of the reconstructed face.
+
+##### Example:
+```python
+from your_project_module import PRN
+import numpy as np
+from skimage.io import imread
+
+prn = PRN(is_dlib=False)  # Initialize the model without dlib
+image = imread('path_to_image.jpg')
+image = resize(image, (256, 256))  # Resize image to 256x256
+
+# Process and generate 3D vertices
+pos = prn.net_forward(image / 255.0)
+vertices = prn.get_vertices(pos)
+```
+
+#### Intended Use:
+- **Forensic Investigations**: Reconstruction of faces from low-quality images for law enforcement or identification purposes.
+- **3D Modeling**: Generates 3D models from 2D images for entertainment, games, or medical applications.
+- **Facial Recognition**: Can be used for generating 3D facial profiles for use in recognition systems.
+
+#### Limitations and Risks:
+- **Accuracy in Reconstruction**: The accuracy of the 3D reconstruction depends heavily on the quality and resolution of the input 2D image.
+- **Bias and Dataset Limitations**: Since the model is fine-tuned on a specific dataset, there may be biases or limitations when applied to other types of facial structures or ethnicities.
+- **Sensitivity to Image Quality**: Low-quality images may produce less accurate 3D models or fail entirely to reconstruct.
+
+#### How to Cite:
+If you use this model, please cite the original PRNet authors and mention the fine-tuned adjustments:
+```
+@misc{prnet_3d_finetuned,
+  title={PRNet 3D Face Reconstruction Finetuned Model},
+  author={Mostafa Aly},
+  year={2024},
+  howpublished={\url{https://huggingface.co/your-hf-username/PRNet_3D}},
+}
+```
+
+---
+
+### How to Use These Model Cards:
+1. **Create a Hugging Face Space** for each of your models if you haven't already.
+2. **Upload the model files** and include these model cards as markdown files (`README.md`) in the repository.
+3. **Customize the links** and placeholders like `"your-hf-username"` and `"path_to_image.jpg"` to your own project details.
+
+Let me know if you need further assistance with publishing or tweaking the cards!