| # why 20 frames? |
|
|
| ## Frame Extraction Parameter Justification |
|
|
| ### Dataset Analysis |
|
|
| Statistical analysis of the FaceForensics++ dataset (1,000 real and 1,000 manipulated sequences) reveals the following frame count distribution: |
|
|
| - **Minimum:** 287 frames |
| - **Median:** ~480 frames |
| - **Mean:** ~530 frames |
| - **Approximate 80th percentile range:** 295–800 frames |
|
|
| ### Selected Parameter: 20 Frames per Video |
|
|
| After evaluating the dataset characteristics and the architectural requirements of the proposed CNN+LSTM model, a uniform extraction of **20 evenly-spaced frames per video** is selected as the optimal parameter. |
|
|
| ### Justification |
|
|
| **1. Dataset Compatibility** |
| Every video in the dataset contains a minimum of 287 frames, ensuring that a uniform extraction of 20 frames is feasible across the entire dataset without special-case handling or padding. This guarantees consistent input dimensionality for all samples. |
|
|
| **2. Architectural Alignment** |
| The proposed model employs a CNN+LSTM architecture where the CNN extracts spatial features per frame and the LSTM models temporal dependencies across the sequence. Deepfake detection via this architecture relies primarily on spatial inconsistencies in facial regions — blending artifacts, texture anomalies, and boundary irregularities — which are present throughout the video rather than being temporally sparse. A sequence length of 20 provides sufficient temporal context for the LSTM to learn inter-frame patterns without requiring high frame density. |
|
|
| **3. Computational Feasibility** |
| At 20 frames per video across 2,000 videos, the total extracted frame count is 40,000. This is computationally manageable for training on consumer-grade hardware. Increasing this parameter to 40 or 60 frames would double or triple memory consumption and training time with negligible accuracy gain at the current dataset scale of 2,000 videos. |
|
|
| **4. Uniform Temporal Coverage** |
| Frames are sampled at evenly-spaced intervals across the full video duration rather than sequentially from the beginning. This approach ensures coverage of the entire temporal span of each video, accounting for cases where facial manipulation may intensify or vary throughout the sequence. |
|
|
| ### Conclusion |
|
|
| A value of 20 uniformly-spaced frames per video satisfies dataset constraints, aligns with the temporal modelling requirements of the CNN+LSTM architecture, and remains computationally tractable for the scope of this project. |
|
|
|
|
|
|
| --- |
|
|
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python model_1M_params.py |
| DeepFakeDetector( |
| (cnn): CNNBlock( |
| (block1): Sequential( |
| (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
| (1): ReLU() |
| (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
| ) |
| (block2): Sequential( |
| (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
| (1): ReLU() |
| (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
| ) |
| (block3): Sequential( |
| (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
| (1): ReLU() |
| ) |
| (gap): AdaptiveAvgPool2d(output_size=(1, 1)) |
| ) |
| (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.3) |
| (classifier): Sequential( |
| (0): Linear(in_features=256, out_features=256, bias=True) |
| (1): ReLU() |
| (2): Dropout(p=0.4, inplace=False) |
| (3): Linear(in_features=256, out_features=128, bias=True) |
| (4): ReLU() |
| (5): Dropout(p=0.3, inplace=False) |
| (6): Linear(in_features=128, out_features=64, bias=True) |
| (7): ReLU() |
| (8): Linear(in_features=64, out_features=2, bias=True) |
| ) |
| ) |
| |
| --- Parameter Count --- |
| Total parameters : 1,530,562 |
| Trainable parameters : 1,530,562 |
|
|
| Input shape : torch.Size([2, 20, 3, 224, 224]) |
| Output shape : torch.Size([2, 2]) |
| Architecture check passed. |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python train_model.py |
| Total videos found: 2000 |
| Training videos: 1800 |
| Testing videos : 200 |
| Using device: cuda |
| Epoch 1/1: 100%|█████████████████████████████████████████████████████████████████████████████████████| 450/450 [31:44<00:00, 4.23s/it, acc=50.06%, loss=0.694] |
| |
| Epoch 1/1 |
| Training Loss: 312.2466 |
| Training Accuracy: 50.06% |
| |
| |
| --- |
| |
| |
| |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python model_3LK_params.py |
| DeepFakeDetector( |
| (cnn): CNNBlock( |
| (block1): Sequential( |
| (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
| (1): ReLU() |
| (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
| ) |
| (block2): Sequential( |
| (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
| (1): ReLU() |
| (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) |
| ) |
| (block3): Sequential( |
| (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
| (1): ReLU() |
| ) |
| (gap): AdaptiveAvgPool2d(output_size=(1, 1)) |
| ) |
| (lstm): LSTM(128, 128, num_layers=2, batch_first=True, dropout=0.3) |
| (classifier): Sequential( |
| (0): Linear(in_features=128, out_features=128, bias=True) |
| (1): ReLU() |
| (2): Dropout(p=0.4, inplace=False) |
| (3): Linear(in_features=128, out_features=64, bias=True) |
| (4): ReLU() |
| (5): Dropout(p=0.3, inplace=False) |
| (6): Linear(in_features=64, out_features=32, bias=True) |
| (7): ReLU() |
| (8): Linear(in_features=32, out_features=2, bias=True) |
| ) |
| ) |
| |
| --- Parameter Count --- |
|
|
| --- Parameter Count --- |
| Total parameters : 384,354 |
| Trainable parameters : 384,354 |
|
|
| Input shape : torch.Size([2, 20, 3, 224, 224]) |
| Output shape : torch.Size([2, 2]) |
| Architecture check passed. |
| Total parameters : 384,354 |
| Trainable parameters : 384,354 |
|
|
| Input shape : torch.Size([2, 20, 3, 224, 224]) |
| Output shape : torch.Size([2, 2]) |
| Architecture check passed. |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python train_model.py |
| Output shape : torch.Size([2, 2]) |
| Architecture check passed. |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python train_model.py |
| Total videos found: 2000 |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python train_model.py |
| Total videos found: 2000 |
| Training videos: 1800 |
| Total videos found: 2000 |
| Training videos: 1800 |
| Testing videos : 200 |
| Using device: cuda |
| Training videos: 1800 |
| Testing videos : 200 |
| Using device: cuda |
| Epoch 1/15: 100%|███████████████████████████████████████████████████████████████████| 450/450 [02:36<00:00, 2.88it/s, acc=49.78%, loss=0.703] |
| Testing videos : 200 |
| Using device: cuda |
| Epoch 1/15: 100%|███████████████████████████████████████████████████████████████████| 450/450 [02:36<00:00, 2.88it/s, acc=49.78%, loss=0.703] |
| |
| Epoch 1/15 |
| Epoch 1/15: 100%|███████████████████████████████████████████████████████████████████| 450/450 [02:36<00:00, 2.88it/s, acc=49.78%, loss=0.703] |
| |
| Epoch 1/15 |
| Training Loss: 313.7062 |
| |
| Epoch 1/15 |
| Training Loss: 313.7062 |
| Training Loss: 313.7062 |
| Training Accuracy: 49.78% |
| |
| |
| --- |
| |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python model.py |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_xception. |
| model = create_fn( |
| DeepFakeDetector( |
| (cnn): CNNBlock( |
| (xception): Xception( |
| (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False) |
| (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act1): ReLU(inplace=True) |
| (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), bias=False) |
| (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act2): ReLU(inplace=True) |
| (block1): Block( |
| (skip): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): SeparableConv2d( |
| (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False) |
| (pointwise): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (2): ReLU(inplace=True) |
| (3): SeparableConv2d( |
| (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False) |
| (pointwise): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (5): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block2): Block( |
| (skip): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False) |
| (pointwise): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False) |
| (pointwise): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block3): Block( |
| (skip): Conv2d(256, 728, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False) |
| (pointwise): Conv2d(256, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block4): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block5): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block6): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block7): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block8): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block9): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block10): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block11): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block12): Block( |
| (skip): Conv2d(728, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (conv3): SeparableConv2d( |
| (conv1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024, bias=False) |
| (pointwise): Conv2d(1024, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (bn3): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act3): ReLU(inplace=True) |
| (conv4): SeparableConv2d( |
| (conv1): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False) |
| (pointwise): Conv2d(1536, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (bn4): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act4): ReLU(inplace=True) |
| (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1)) |
| (fc): Linear(in_features=2048, out_features=1000, bias=True) |
| ) |
| (gap): AdaptiveAvgPool2d(output_size=(1, 1)) |
| (fc): Linear(in_features=2048, out_features=256, bias=True) |
| ) |
| (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.3) |
| (classifier): Sequential( |
| (0): Linear(in_features=256, out_features=256, bias=True) |
| (1): ReLU() |
| (2): Dropout(p=0.4, inplace=False) |
| (3): Linear(in_features=256, out_features=128, bias=True) |
| (4): ReLU() |
| (5): Dropout(p=0.3, inplace=False) |
| (6): Linear(in_features=128, out_features=64, bias=True) |
| (7): ReLU() |
| (8): Linear(in_features=64, out_features=2, bias=True) |
| ) |
| ) |
| |
| --- Parameter Count --- |
| Total parameters : 24,540,242 |
| Trainable parameters : 1,684,290 |
|
|
| Input shape : torch.Size([2, 20, 3, 224, 224]) |
| Output shape : torch.Size([2, 2]) |
| Architecture check passed. |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python train_model.py |
| Total videos found: 2000 |
| Training videos: 1800 |
| Testing videos : 200 |
| Using device: cuda |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_xception. |
| model = create_fn( |
| Epoch 1/15: 100%|███████████████████████████████████████████████████████████████████| 450/450 [04:12<00:00, 1.78it/s, acc=53.17%, loss=0.637] |
|
|
| Epoch 1/15 |
| Training Loss: 311.3695 |
| Training Accuracy: 53.17% |
|
|
| --- |
|
|
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python model.py |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_xception. |
| model = create_fn( |
| DeepFakeDetector( |
| (cnn): CNNBlock( |
| (xception): Xception( |
| (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False) |
| (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act1): ReLU(inplace=True) |
| (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), bias=False) |
| (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act2): ReLU(inplace=True) |
| (block1): Block( |
| (skip): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): SeparableConv2d( |
| (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False) |
| (pointwise): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (2): ReLU(inplace=True) |
| (3): SeparableConv2d( |
| (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False) |
| (pointwise): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (5): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block2): Block( |
| (skip): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False) |
| (pointwise): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False) |
| (pointwise): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block3): Block( |
| (skip): Conv2d(256, 728, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False) |
| (pointwise): Conv2d(256, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block4): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block5): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block6): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block7): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block8): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block9): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block10): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block11): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block12): Block( |
| (skip): Conv2d(728, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (conv3): SeparableConv2d( |
| (conv1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024, bias=False) |
| (pointwise): Conv2d(1024, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (bn3): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act3): ReLU(inplace=True) |
| (conv4): SeparableConv2d( |
| (conv1): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False) |
| (pointwise): Conv2d(1536, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (bn4): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act4): ReLU(inplace=True) |
| (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1)) |
| (fc): Linear(in_features=2048, out_features=1000, bias=True) |
| ) |
| (gap): AdaptiveAvgPool2d(output_size=(1, 1)) |
| (fc): Linear(in_features=2048, out_features=256, bias=True) |
| ) |
| (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.3) |
| (classifier): Sequential( |
| (0): Linear(in_features=256, out_features=256, bias=True) |
| (1): ReLU() |
| (2): Dropout(p=0.4, inplace=False) |
| (3): Linear(in_features=256, out_features=128, bias=True) |
| (4): ReLU() |
| (5): Dropout(p=0.3, inplace=False) |
| (6): Linear(in_features=128, out_features=64, bias=True) |
| (7): ReLU() |
| (8): Linear(in_features=64, out_features=2, bias=True) |
| ) |
| ) |
| |
| --- Parameter Count --- |
| Total parameters : 24,540,242 |
| Trainable parameters : 1,684,290 |
|
|
| Input shape : torch.Size([2, 20, 3, 224, 224]) |
| Output shape : torch.Size([2, 2]) |
| Architecture check passed. |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> python train_model.py |
| Total videos found: 2000 |
| Training videos: 1800 |
| Testing videos : 200 |
| Using device: cuda |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_xception. |
| model = create_fn( |
| Epoch 1/10: 100%|████████████████████████████████████████████████████████████████████████████████████| 450/450 [04:11<00:00, 1.79it/s, acc=48.72%, loss=0.691] |
|
|
| Epoch 1/10 |
| Training Loss: 312.4615 |
| Training Accuracy: 48.72% |
| Model saved to saved_models/deepfake_model_epoch_10.pth |
| Epoch 2/10: 100%|████████████████████████████████████████████████████████████████████████████████████| 450/450 [04:10<00:00, 1.80it/s, acc=48.89%, loss=0.693] |
|
|
| Epoch 2/10 |
| Training Loss: 312.3502 |
| Training Accuracy: 48.89% |
| Model saved to saved_models/deepfake_model_epoch_10.pth |
| Epoch 3/10: 100%|████████████████████████████████████████████████████████████████████████████████████| 450/450 [04:11<00:00, 1.79it/s, acc=49.17%, loss=0.692] |
|
|
| Epoch 3/10 |
| Training Loss: 312.2474 |
| Training Accuracy: 49.17% |
| Model saved to saved_models/deepfake_model_epoch_10.pth |
| Epoch 4/10: 100%|████████████████████████████████████████████████████████████████████████████████████| 450/450 [04:10<00:00, 1.80it/s, acc=48.67%, loss=0.691] |
|
|
| Epoch 4/10 |
| Training Loss: 312.0824 |
| Training Accuracy: 48.67% |
| Model saved to saved_models/deepfake_model_epoch_10.pth |
| Epoch 5/10: 3%|██▍ | 13/450 [00:22<12:44, 1.75s/it, acc=36.54%, loss=0.701] |
| Traceback (most recent call last): |
| File "C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM\train_model.py", line 265, in <module> |
| main() |
| ~~~~^^ |
| File "C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM\train_model.py", line 193, in main |
| running_loss += loss.item() |
| ~~~~~~~~~^^ |
| KeyboardInterrupt |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN+LSTM> |
| |
|
|
|
|
|
|
| --- |
|
|
| # XceptioNet+LSTM(freeze all) terminal : |
|
|
| --- |
|
|
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN_LSTM> python model.py |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_xception. |
| model = create_fn( |
| DeepFakeDetector( |
| (cnn): CNNBlock( |
| (xception): Xception( |
| (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False) |
| (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act1): ReLU(inplace=True) |
| (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), bias=False) |
| (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act2): ReLU(inplace=True) |
| (block1): Block( |
| (skip): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): SeparableConv2d( |
| (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False) |
| (pointwise): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (2): ReLU(inplace=True) |
| (3): SeparableConv2d( |
| (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False) |
| (pointwise): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (5): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block2): Block( |
| (skip): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False) |
| (pointwise): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False) |
| (pointwise): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block3): Block( |
| (skip): Conv2d(256, 728, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False) |
| (pointwise): Conv2d(256, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (block4): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block5): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block6): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block7): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block8): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block9): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block10): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block11): Block( |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): ReLU(inplace=True) |
| (7): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (8): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| ) |
| ) |
| (block12): Block( |
| (skip): Conv2d(728, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False) |
| (skipbn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (rep): Sequential( |
| (0): ReLU() |
| (1): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 728, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (2): BatchNorm2d(728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (3): ReLU(inplace=True) |
| (4): SeparableConv2d( |
| (conv1): Conv2d(728, 728, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=728, bias=False) |
| (pointwise): Conv2d(728, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (6): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) |
| ) |
| ) |
| (conv3): SeparableConv2d( |
| (conv1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024, bias=False) |
| (pointwise): Conv2d(1024, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (bn3): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act3): ReLU(inplace=True) |
| (conv4): SeparableConv2d( |
| (conv1): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False) |
| (pointwise): Conv2d(1536, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) |
| ) |
| (bn4): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
| (act4): ReLU(inplace=True) |
| (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1)) |
| (fc): Linear(in_features=2048, out_features=1000, bias=True) |
| ) |
| (gap): AdaptiveAvgPool2d(output_size=(1, 1)) |
| (fc): Linear(in_features=2048, out_features=256, bias=True) |
| ) |
| (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.3) |
| (classifier): Sequential( |
| (0): Linear(in_features=256, out_features=256, bias=True) |
| (1): ReLU() |
| (2): Dropout(p=0.4, inplace=False) |
| (3): Linear(in_features=256, out_features=128, bias=True) |
| (4): ReLU() |
| (5): Dropout(p=0.3, inplace=False) |
| (6): Linear(in_features=128, out_features=64, bias=True) |
| (7): ReLU() |
| (8): Linear(in_features=64, out_features=2, bias=True) |
| ) |
| ) |
| |
| --- Parameter Count --- |
| Total parameters : 24,540,242 |
| Trainable parameters : 1,684,290 |
|
|
| Input shape : torch.Size([2, 20, 3, 224, 224]) |
| Output shape : torch.Size([2, 2]) |
| Architecture check passed. |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN_LSTM> python train_model.py |
|
|
| === Path Verification === |
| Train/Test Root: ✓ Found → E:\dataset\Deepfake Detection Dataset\3. frames_dataset |
| real: 1000 video folders |
| fake: 1000 video folders |
| Validation Root: ✓ Found → E:\dataset\Deepfake Detection Dataset\5. Validation_Dataset_Frames |
| real: 500 video folders |
| fake: 500 video folders |
| |
| === Building Splits === |
| Train : 1800 videos |
| Test : 200 videos |
| Val : 1000 videos |
| Train → real: 900, fake: 900 |
| Test → real: 100, fake: 100 |
| Val → real: 500, fake: 500 |
| |
| Train batches : 450 |
| Test batches : 50 |
| Val batches : 250 |
| |
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_xception. |
| Test batches : 50 |
| Val batches : 250 |
| |
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_Test batches : 50 |
| Val batches : 250 |
| |
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| Test batches : 50 |
| Val batches : 250 |
| |
| Using device : cuda |
| Test batches : 50 |
| Val batches : 250 |
| |
| Using device : cuda |
| Test batches : 50 |
| Val batches : 250 |
| |
| Using device : cuda |
| Test batches : 50 |
| Val batches : 250 |
| Test batches : 50 |
| Val batches : 250 |
| Test batches : 50 |
| Val batches : 250 |
| |
| Test batches : 50 |
| Val batches : 250 |
| |
| Test batches : 50 |
| Test batches : 50 |
| Val batches : 250 |
| Test batches : 50 |
| Val batches : 250 |
| Test batches : 50 |
| Val batches : 250 |
| Test batches : 50 |
| Val batches : 250 |
| |
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_xception. |
| model = create_fn( |
| Trainable params : 1,684,290 / 24,540,242 |
|
|
| === Training === |
| |
| Epoch 1/20 |
| Train → Loss: 0.6934 Acc: 50.78% |
| Val → Loss: 0.6985 Acc: 50.00% |
| ✓ Best model saved (val_loss: 0.6985) |
| |
| Epoch 2/20 |
| Train → Loss: 0.6943 Acc: 50.33% |
| Val → Loss: 0.6933 Acc: 49.70% |
| ✓ Best model saved (val_loss: 0.6933) |
| |
| Epoch 3/20 |
| Train → Loss: 0.6937 Acc: 47.61% |
| Val → Loss: 0.6930 Acc: 50.00% |
| No improvement. Patience: 1/5 |
| |
| Epoch 4/20 |
| Train → Loss: 0.6936 Acc: 49.78% |
| Val → Loss: 0.6931 Acc: 50.00% |
| No improvement. Patience: 2/5 |
| |
| Epoch 5/20 |
| Train → Loss: 0.6935 Acc: 50.39% |
| Val → Loss: 0.6931 Acc: 50.00% |
| No improvement. Patience: 3/5 |
| |
| Epoch 6/20 |
| Train → Loss: 0.6938 Acc: 50.17% |
| Val → Loss: 0.6932 Acc: 50.00% |
| No improvement. Patience: 4/5 |
| |
| Epoch 7/20 |
| Train → Loss: 0.6936 Acc: 49.11% |
| Val → Loss: 0.6931 Acc: 50.00% |
| No improvement. Patience: 5/5 |
| |
| ⚡ Early stopping triggered at epoch 7 |
| |
| ✓ Training complete — best val loss: 0.6933 |
| |
| === Loading Best Model === |
| ✓ Loaded from epoch 2 (val_loss: 0.6933) |
|
|
| === Evaluation === |
| Evaluating Test Set: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:42<00:00, 1.19it/s] |
| Evaluating Val Set: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 250/250 [01:49<00:00, 2.29it/s] |
|
|
| ================================================== |
| TEST SET — Classification Report |
| ================================================== |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| precision recall f1-score support |
| |
| Real 0.50 1.00 0.67 100 |
| Fake 0.00 0.00 0.00 100 |
| |
| accuracy 0.50 200 |
| macro avg 0.25 0.50 0.33 200 |
| weighted avg 0.25 0.50 0.33 200 |
| |
| ================================================== |
| VALIDATION SET — Classification Report |
| ================================================== |
| precision recall f1-score support |
| |
| Real 0.50 0.99 0.66 500 |
| Fake 0.00 0.00 0.00 500 |
| |
| accuracy 0.50 1000 |
| macro avg 0.25 0.50 0.33 1000 |
| weighted avg 0.25 0.50 0.33 1000 |
| |
|
|
| === Saving Plots === |
| Saved → saved_models\training_curves.png |
| Saved → saved_models\Confusion_Matrix_-_Test_Set.png |
| Saved → saved_models\Confusion_Matrix_-_Val_Set.png |
| Saved → saved_models\ROC_Curve_-_Test_Set.png |
| Saved → saved_models\ROC_Curve_-_Val_Set.png |
|
|
| Test AUC : 0.4625 |
| Val AUC : 0.4310 |
|
|
| ✓ All plots saved to saved_models |
| |
| |
| --- |
| # XceptionNet+LSTM (unfreeze some layer) terminals: |
| PS C:\Users\ASUS\Desktop\Mini Project\Deepfake-video-Detection\ML model\CNN_LSTM> python train_model.py |
| |
| === Path Verification === |
| Train/Test Root: ✓ Found → E:\dataset\Deepfake Detection Dataset\3. frames_dataset |
| real: 1000 video folders |
| fake: 1000 video folders |
| Validation Root: ✓ Found → E:\dataset\Deepfake Detection Dataset\5. Validation_Dataset_Frames |
| real: 500 video folders |
| fake: 500 video folders |
|
|
| === Building Splits === |
| Train : 1800 videos |
| Test : 200 videos |
| Val : 1000 videos |
| Train → real: 900, fake: 900 |
| Test → real: 100, fake: 100 |
| Val → real: 500, fake: 500 |
|
|
| Train batches : 450 |
| Test batches : 50 |
| Val batches : 250 |
|
|
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\timm\models\_factory.py:138: UserWarning: Mapping deprecated model name xception to current legacy_xception. |
| model = create_fn( |
| Xception — frozen: 94, unfrozen: 62 / 156 params groups |
| Trainable params : 15,902,322 / 24,540,242 |
| |
| === Training === |
| |
| Epoch 1/20 |
| Train → Loss: 0.6939 Acc: 49.89% |
| Val → Loss: 0.6935 Acc: 50.00% |
| ✓ Best model saved (val_loss: 0.6935) |
| |
| Epoch 2/20 |
| Train → Loss: 0.6938 Acc: 49.22% |
| Val → Loss: 0.6927 Acc: 50.00% |
| No improvement. Patience: 1/5 |
| |
| Epoch 3/20 |
| Train → Loss: 0.6934 Acc: 49.83% |
| Val → Loss: 0.6935 Acc: 50.00% |
| No improvement. Patience: 2/5 |
|
|
| Epoch 4/20 |
| Train → Loss: 0.6933 Acc: 51.28% |
| Val → Loss: 0.6938 Acc: 50.00% |
| No improvement. Patience: 3/5 |
|
|
| Epoch 5/20 |
| Train → Loss: 0.6938 Acc: 50.22% |
| Val → Loss: 0.6938 Acc: 50.00% |
| No improvement. Patience: 4/5 |
|
|
| Epoch 6/20 |
| Train → Loss: 0.6938 Acc: 48.72% |
| Val → Loss: 0.6929 Acc: 50.70% |
| No improvement. Patience: 5/5 |
|
|
| ⚡ Early stopping triggered at epoch 6 |
|
|
| ✓ Training complete — best val loss: 0.6935 |
|
|
| === Loading Best Model === |
| ✓ Loaded from epoch 1 (val_loss: 0.6935) |
| |
| === Evaluation === |
| Evaluating Test Set: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:51<00:00, 1.04s/it] |
| Evaluating Val Set: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 250/250 [01:49<00:00, 2.29it/s] |
| |
| ================================================== |
| TEST SET — Classification Report |
| ================================================== |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| precision recall f1-score support |
| |
| Real 0.50 1.00 0.67 100 |
| Fake 0.00 0.00 0.00 100 |
| |
| accuracy 0.50 200 |
| macro avg 0.25 0.50 0.33 200 |
| weighted avg 0.25 0.50 0.33 200 |
| |
| ================================================== |
| VALIDATION SET — Classification Report |
| ================================================== |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| C:\Users\ASUS\AppData\Roaming\Python\Python313\site-packages\sklearn\metrics\_classification.py:1833: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
| _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) |
| precision recall f1-score support |
| |
| Real 0.50 1.00 0.67 500 |
| Fake 0.00 0.00 0.00 500 |
| |
| accuracy 0.50 1000 |
| macro avg 0.25 0.50 0.33 1000 |
| weighted avg 0.25 0.50 0.33 1000 |
| |
|
|
| === Saving Plots === |
| Saved → saved_models\training_curves.png |
| Saved → saved_models\Confusion_Matrix_-_Test_Set.png |
| Saved → saved_models\Confusion_Matrix_-_Val_Set.png |
| Saved → saved_models\ROC_Curve_-_Test_Set.png |
| Saved → saved_models\ROC_Curve_-_Val_Set.png |
|
|
| Test AUC : 0.5139 |
| Val AUC : 0.3664 |
|
|
| ✓ All plots saved to saved_models |
| |
| |
| --- |
| EfficientNet-B1 training terminal :- |
| |
| === Path Verification === |
| Train/Test Root: ✓ Found → E:\dataset\Deepfake Detection Dataset\3. frames_dataset |
| real: 1000 video folders |
| fake: 1000 video folders |
| Validation Root: ✓ Found → E:\dataset\Deepfake Detection Dataset\5. Validation_Dataset_Frames |
| real: 500 video folders |
| fake: 500 video folders |
|
|
| === Building Splits === |
| Train : 1800 videos |
| Test : 200 videos |
| Val : 1000 videos |
| Train → real: 900, fake: 900 |
| Test → real: 100, fake: 100 |
| Val → real: 500, fake: 500 |
|
|
| Train batches : 450 |
| Test batches : 50 |
| Val batches : 250 |
|
|
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| EfficientNet-B1 — frozen: 180, unfrozen: 119 / 299 |
| Test → real: 100, fake: 100 |
| Val → real: 500, fake: 500 |
|
|
| Train batches : 450 |
| Test batches : 50 |
| Val batches : 250 |
|
|
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| EfficientNet-B1 — frozen: 180, unfrozen: 119 / 299 |
|
|
| Train batches : 450 |
| Test batches : 50 |
| Val batches : 250 |
|
|
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| EfficientNet-B1 — frozen: 180, unfrozen: 119 / 299 |
| Test batches : 50 |
| Val batches : 250 |
|
|
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| EfficientNet-B1 — frozen: 180, unfrozen: 119 / 299 |
|
|
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| EfficientNet-B1 — frozen: 180, unfrozen: 119 / 299 |
| Using device : cuda |
| GPU : NVIDIA GeForce RTX 2050 |
| EfficientNet-B1 — frozen: 180, unfrozen: 119 / 299 |
| GPU : NVIDIA GeForce RTX 2050 |
| EfficientNet-B1 — frozen: 180, unfrozen: 119 / 299 |
| EfficientNet-B1 — frozen: 180, unfrozen: 119 / 299 |
| Trainable params : 7,136,854 / 8,000,866 |
|
|
| === Training === |
|
|
| Epoch 1/20 |
| Train → Loss: 0.6965 Acc: 50.00% |
| Val → Loss: 0.6962 Acc: 50.00% |
| ✓ Best model saved (val_loss: 0.6962) |
| |
| Epoch 2/20 |
| Train → Loss: 0.6949 Acc: 49.94% |
| Val → Loss: 0.6875 Acc: 50.00% |
| ✓ Best model saved (val_loss: 0.6875) |
|
|
| Epoch 3/20 |
| Train → Loss: 0.6622 Acc: 64.33% |
| Val → Loss: 0.5967 Acc: 77.60% |
| ✓ Best model saved (val_loss: 0.5967) |
| |
| Epoch 4/20 |
| Train → Loss: 0.6276 Acc: 69.94% |
| Val → Loss: 0.5649 Acc: 79.30% |
| ✓ Best model saved (val_loss: 0.5649) |
|
|
| Epoch 5/20 |
| Train → Loss: 0.6048 Acc: 72.11% |
| Val → Loss: 0.5258 Acc: 78.30% |
| ✓ Best model saved (val_loss: 0.5258) |
| |
| Epoch 6/20 |
| Train → Loss: 0.5571 Acc: 74.06% |
| Val → Loss: 0.4761 Acc: 79.30% |
| ✓ Best model saved (val_loss: 0.4761) |
|
|
| Epoch 7/20 |
| Train → Loss: 0.5250 Acc: 76.17% |
| Val → Loss: 0.5161 Acc: 76.20% |
| No improvement. Patience: 1/5 |
|
|
| Epoch 8/20 |
| Train → Loss: 0.5162 Acc: 75.39% |
| Val → Loss: 0.4988 Acc: 76.00% |
| No improvement. Patience: 2/5 |
|
|
| Epoch 9/20 |
| Train → Loss: 0.4903 Acc: 78.56% |
| Val → Loss: 0.4555 Acc: 79.50% |
| ✓ Best model saved (val_loss: 0.4555) |
| |
| Epoch 10/20 |
| Train → Loss: 0.4533 Acc: 79.56% |
| Val → Loss: 0.5358 Acc: 73.60% |
| No improvement. Patience: 1/5 |
| |
| Epoch 11/20 |
| Train → Loss: 0.4353 Acc: 81.56% |
| Val → Loss: 0.5443 Acc: 72.40% |
| No improvement. Patience: 2/5 |
| |
| Epoch 12/20 |
| Train → Loss: 0.3911 Acc: 83.78% |
| Val → Loss: 0.4415 Acc: 77.60% |
| ✓ Best model saved (val_loss: 0.4415) |
|
|
| Epoch 13/20 |
| Train → Loss: 0.3830 Acc: 83.17% |
| Val → Loss: 0.5870 Acc: 69.40% |
| No improvement. Patience: 1/5 |
|
|
| Epoch 14/20 |
| Train → Loss: 0.3429 Acc: 86.22% |
| Val → Loss: 0.7223 Acc: 64.20% |
| No improvement. Patience: 2/5 |
|
|
| Epoch 15/20 |
| Train → Loss: 0.3190 Acc: 86.83% |
| Val → Loss: 0.6290 Acc: 68.20% |
| No improvement. Patience: 3/5 |
|
|
| Epoch 16/20 |
| Train → Loss: 0.3195 Acc: 86.83% |
| Val → Loss: 0.5972 Acc: 71.40% |
| No improvement. Patience: 4/5 |
|
|
| Epoch 17/20 |
| Train → Loss: 0.2944 Acc: 87.50% |
| Val → Loss: 0.6218 Acc: 68.70% |
| No improvement. Patience: 5/5 |
|
|
| ⚡ Early stopping triggered at epoch 17 |
|
|
| ✓ Training complete — best val loss: 0.4415 |
|
|
| === Loading Best Model === |
| ✓ Loaded from epoch 12 (val_loss: 0.4415) |
| |
| === Evaluation === |
| Evaluating Test Set: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:36<00:00, 1.36it/s] |
| Evaluating Val Set: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 250/250 [01:17<00:00, 3.22it/s] |
| |
| ================================================== |
| TEST SET — Classification Report |
| ================================================== |
| precision recall f1-score support |
| |
| Real 0.59 0.68 0.63 100 |
| Fake 0.62 0.53 0.57 100 |
| |
| accuracy 0.60 200 |
| macro avg 0.61 0.60 0.60 200 |
| weighted avg 0.61 0.60 0.60 200 |
| |
| ================================================== |
| VALIDATION SET — Classification Report |
| ================================================== |
| precision recall f1-score support |
| |
| Real 0.73 0.88 0.80 500 |
| Fake 0.85 0.67 0.75 500 |
| |
| accuracy 0.78 1000 |
| macro avg 0.79 0.78 0.77 1000 |
| weighted avg 0.79 0.78 0.77 1000 |
| |
| |
| === Saving Plots === |
| Saved → saved_models\training_curves.png |
| Saved → saved_models\Confusion_Matrix_-_Test_Set.png |
| Saved → saved_models\Confusion_Matrix_-_Val_Set.png |
| Saved → saved_models\ROC_Curve_-_Test_Set.png |
| Saved → saved_models\ROC_Curve_-_Val_Set.png |
| |
| Test AUC : 0.6472 |
| Val AUC : 0.8974 |
| |
| ✓ All plots saved to saved_models |
|
|
|
|
|
|
| --- |
| 🔍 So What Did the Original Author Do? |
|
|
| There are only 3 possible ways they handled videos: |
|
|
| 🔹 1. Frame-wise Prediction + Aggregation (MOST COMMON ✅) |
|
|
| Pipeline: |
|
|
| Video → Frames → CNN → Predictions per frame → Aggregate → Final output |
| Example: |
| Frame1 → 0.91 (fake) |
| Frame2 → 0.85 (fake) |
| Frame3 → 0.40 (real) |
| ... |
|
|
| Then: |
|
|
| Average |
| Majority vote |
| Max confidence |
|
|
| 👉 Final decision = video-level prediction |
|
|
| 🔹 2. Frame Sampling (Single Frame) |
| Video → pick 1 frame → CNN → output |
|
|
| ❌ Less accurate |
| ✔ Simple |
|
|
| 🔹 3. Feature Aggregation (Advanced) |
| Frames → CNN features → LSTM/Pooling → Classifier |
|
|
| 👉 This is what YOU did with: |
|
|
| EfficientNet + LSTM ✅ |
|
|
| --- |
