# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Skullly/DeepFake-EN-B6")
model = AutoModelForImageClassification.from_pretrained("Skullly/DeepFake-EN-B6")Quick Links
DeepFake-EN-B6
This model is a fine-tuned version of google/efficientnet-b6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0036
- Accuracy: 0.9989
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0076 | 0.9998 | 2187 | 0.0088 | 0.9970 |
| 0.002 | 2.0 | 4375 | 0.0173 | 0.9931 |
| 0.0011 | 2.4997 | 5468 | 0.0036 | 0.9989 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for Skullly/DeepFake-EN-B6
Base model
google/efficientnet-b6
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Skullly/DeepFake-EN-B6") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")