Update README.md

README.md

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-# Deepfake Detection with Improved EfficientViT
-
-## Model Architecture
-
-![Model Architecture](assets/architecture.png)
-
-## Inference Pipeline
-
-![Inference Pipeline](assets/inference_pipeline.png)
-
-
-This repository contains a **PyTorch model for deepfake detection** based on an improved **EfficientViT** architecture, trained on video data.  
-
-The model predicts whether a video is **real (0)** or **fake (1)** using both visual information and temporal cues.
-
----
-
-## 🧩 Model Description
-
-**Architecture:** Improved EfficientViT  
-**Backbone:** EfficientNet-B0 for feature extraction  
-**Head:** Transformer-based temporal modeling with classification head  
-**Input:** Video frames (224×224 RGB images)  
-**Output:** Binary label (0=Real, 1=Fake) and frame-level probabilities  
-
-**Key Features:**
-
-- Extracts faces from frames using MTCNN  
-- Supports inference on raw video files  
-- Provides frame-level probabilities for fine-grained analysis  
-
----
-
-## 📁 Repository Structure
-
-```
-deepfake-efficientvit/
-│
-├── model.py                  # ImprovedEfficientViT class
-├── inference.py              # Functions to run inference on videos
-├── model.pth  # Trained weights
-├── config.json               # Optional model metadata
-├── requirements.txt          # Required packages
-├── README.md
-
-```
-
-## ⚡ Installation
-git clone https://huggingface.co/faisalishfaq2005/deepfake-detection-efficientnet-vit
-
-cd deepfake-detection-efficientnet-vit
-
-pip install -r requirements.txt
-
-## 🚀 Usage
-# 1.Programmatic Inference
-
-```python
-
-from huggingface_hub import hf_hub_download
-import torch
-from model import ImprovedEfficientViT
-from inference import predict_vedio  # your inference function
-
-# 1️⃣ Download the checkpoint from Hugging Face
-checkpoint_path = hf_hub_download(
-    repo_id="faisalishfaq2005/deepfake-detection-efficientnet-vit",  
-    filename="model.pth"
-)
-
-# 2️⃣ Load the model
-model = ImprovedEfficientViT()
-model.load_state_dict(torch.load(checkpoint_path, map_location="cpu"))
-model.eval()
-
-# 3️⃣ Run inference on a video
-video_path = "sample_video.mp4"
-result = predict_vedio(video_path, model)
-print(result)
-# Example Output: {'class': 1}
-
-
-```
-# 2. Manual Download
-
-Go to the Hugging Face model page
-
-Download:
-
-model.pth
-
-model.py
-
-inference.py
-
-Place them in the same folder locally.
-
-Install requirements and run predict_video().
-
-## 📄 License
-
-This model is released under the MIT License.
-You are free to use, modify, and distribute it, with attribution.
-
-## 📚 Citation
-
-If you use this model in your research, please cite:
-
-```bibtex
-@inproceedings{faisalishfaq2025efficientvit,
-  title={Deepfake Detection with Efficientnet and ViT},
-  author={Faisal Ishfaq},
-  year={2025}
-}
-```
-
-
+---
+language: 
+- en
+license: mit
+tags:
+- deepfake-detection
+- vision-transformer
+- efficientnet
+- multimodal
+- pytorch
+- computer-vision
+- model
+- image-classification
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+pipeline_tag: image-classification
+library_name: pytorch
+widget:
+- text: "sample_video.mp4"
+---
+
+# Deepfake Detection with Improved EfficientViT
+
+## Model Architecture
+
+![Model Architecture](assets/architecture.png)
+
+## Inference Pipeline
+
+![Inference Pipeline](assets/inference_pipeline.png)
+
+
+This repository contains a **PyTorch model for deepfake detection** based on an improved **EfficientViT** architecture, trained on video data.  
+
+The model predicts whether a video is **real (0)** or **fake (1)** using both visual information and temporal cues.
+
+---
+
+## 🧩 Model Description
+
+**Architecture:** Improved EfficientViT  
+**Backbone:** EfficientNet-B0 for feature extraction  
+**Head:** Transformer-based temporal modeling with classification head  
+**Input:** Video frames (224×224 RGB images)  
+**Output:** Binary label (0=Real, 1=Fake) and frame-level probabilities  
+
+**Key Features:**
+
+- Extracts faces from frames using MTCNN  
+- Supports inference on raw video files  
+- Provides frame-level probabilities for fine-grained analysis  
+
+---
+
+## 📁 Repository Structure
+
+```
+deepfake-efficientvit/
+│
+├── model.py                  # ImprovedEfficientViT class
+├── inference.py              # Functions to run inference on videos
+├── model.pth  # Trained weights
+├── config.json               # Optional model metadata
+├── requirements.txt          # Required packages
+├── README.md
+
+```
+
+## ⚡ Installation
+git clone https://huggingface.co/faisalishfaq2005/deepfake-detection-efficientnet-vit
+
+cd deepfake-detection-efficientnet-vit
+
+pip install -r requirements.txt
+
+## 🚀 Usage
+# 1.Programmatic Inference
+
+```python
+
+from huggingface_hub import hf_hub_download
+import torch
+from model import ImprovedEfficientViT
+from inference import predict_vedio  # your inference function
+
+# 1️⃣ Download the checkpoint from Hugging Face
+checkpoint_path = hf_hub_download(
+    repo_id="faisalishfaq2005/deepfake-detection-efficientnet-vit",  
+    filename="model.pth"
+)
+
+# 2️⃣ Load the model
+model = ImprovedEfficientViT()
+model.load_state_dict(torch.load(checkpoint_path, map_location="cpu"))
+model.eval()
+
+# 3️⃣ Run inference on a video
+video_path = "sample_video.mp4"
+result = predict_vedio(video_path, model)
+print(result)
+# Example Output: {'class': 1}
+
+
+```
+# 2. Manual Download
+
+Go to the Hugging Face model page
+
+Download:
+
+model.pth
+
+model.py
+
+inference.py
+
+Place them in the same folder locally.
+
+Install requirements and run predict_video().
+
+## 📄 License
+
+This model is released under the MIT License.
+You are free to use, modify, and distribute it, with attribution.
+
+## 📚 Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@inproceedings{faisalishfaq2025efficientvit,
+  title={Deepfake Detection with Efficientnet and ViT},
+  author={Faisal Ishfaq},
+  year={2025}
+}
+```
+
+