diffusion-detection
This model was trained to distinguish real world images (negative) from machine generated ones (postive).
Model usage
from transformers import BeitImageProcessor, BeitForImageClassification
from PIL import Image
processor = BeitImageProcessor.from_pretrained('TimKond/diffusion-detection')
model = BeitForImageClassification.from_pretrained('TimKond/diffusion-detection')
image = Image.open("2980_saltshaker.jpg")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
Training and evaluation data
BEiT-base-patch16-224-pt22k was loaded as a base model for further fine tuning:
As negatives a subsample of 10.000 images from imagenet-1k was used. Complementary 10.000 positive images were generated using Realistic_Vision_V1.4.
The labels from imagenet-1k were used as prompts for image generation. GitHub reference
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.29.2
- Pytorch 1.11.0+cu113
- Datasets 2.12.0
- Tokenizers 0.13.3