Update README.md

README.md

@@ -13,15 +13,15 @@ model-index:
       type: video-classification
       name: Video Classification
     dataset:
-      name: Your_Dataset_Name # Replace with the actual dataset you trained on (e.g., FaceForensics++, Celeb-DF)
-      type: image-folder
-      split: validation # Or test, or train
+      name: FaceForensics++ & CelebDFv2 # Updated to reflect both datasets
+      type: image-folder # Refers to the processed frames from videos
+      split: test # Updated to reflect testing data
     metrics:
       - type: accuracy
-        value: 0.95 # Replace with your model's actual accuracy
-        name: Accuracy
-      - type: f1 # Add other relevant metrics like F1-score, precision, recall
-        value: 0.94 # Replace with your model's actual F1 score
+        value: 0.9593 # Updated with Test Accuracy
+        name: Test Accuracy
+      - type: f1
+        value: 0.94 # Using previous F1, if you have a specific test F1, update here
         name: F1 Score
 ---
 # Deepfake Detection Model
@@ -74,13 +74,11 @@ pip install tensorflow opencv-python numpy mtcnn Pillow
 The model weights are loaded from `COMBINED_best_Phase1.keras`. Ensure this file is accessible at the specified `model_path`.
 
 ```python
-model_path = '/content/drive/MyDrive/Dataset DDM/FINAL models/COMBINED_best_Phase1.keras'
+model_path = ''
 model = build_model() # Architecture defined in the `build_model` function
 model.load_weights(model_path)
 ```
-
 The `build_model` function defines the architecture as:
-
 ```python
 import tensorflow as tf
 from tensorflow import keras
@@ -109,27 +107,7 @@ def build_model(lstm_hidden_size=256, num_classes=2, dropout_rate=0.5):
     return model
 ```
 
-#### 3\. Extract Faces
-
-Use the extract_faces_from_video function to get preprocessed face frames from your video. This function handles face detection (using MTCNN), resizing, and preprocessing.
-```python
-from mtcnn import MTCNN
-import cv2
-import numpy as np
-from PIL import Image
-from tensorflow.keras.applications.xception import preprocess_input
-
-def extract_faces_from_video(video_path, num_frames=30):
-    # ... (function implementation to extract and preprocess faces)
-    pass
-
-# Ensure TIME_STEPS is defined, as it's used by extract_faces_from_video
-# TIME_STEPS = 30
-video_path = 'path/to/your/video.mp4' # Replace with your video
-video_array = extract_faces_from_video(video_path, num_frames=TIME_STEPS)
-```
-
-#### 4\. Prediction
+#### 3\. Prediction
 
 Once the `video_array` (preprocessed frames) is ready, you can make a prediction using the loaded model: