# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("sandesh2233/Deepfakes_detection")
model = AutoModelForImageClassification.from_pretrained("sandesh2233/Deepfakes_detection")Quick Links
Deepfakes_detection
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3242
- Accuracy: 0.9222
- Auc: 0.9998
- F1 Fake: 0.9278
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | F1 Fake |
|---|---|---|---|---|---|---|
| No log | 1.0 | 11 | 0.3630 | 0.8579 | 0.9527 | 0.8454 |
| No log | 2.0 | 22 | 0.2680 | 0.9114 | 0.9863 | 0.9166 |
| No log | 3.0 | 33 | 0.3072 | 0.9123 | 0.9879 | 0.9178 |
| No log | 4.0 | 44 | 0.2917 | 0.914 | 0.988 | 0.9193 |
| 0.0568 | 5.0 | 55 | 0.2840 | 0.9132 | 0.988 | 0.9182 |
Framework versions
- Transformers 5.5.4
- Pytorch 2.11.0+cu130
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for sandesh2233/Deepfakes_detection
Base model
google/vit-base-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sandesh2233/Deepfakes_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")