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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: diffusion-detection |
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results: [] |
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license: apache-2.0 |
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datasets: |
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- imagenet-1k |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# diffusion-detection |
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This model was trained to distinguish real world images (negative) from machine generated ones (postive). |
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## Model usage |
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```python |
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from transformers import BeitImageProcessor, BeitForImageClassification |
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from PIL import Image |
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processor = BeitImageProcessor.from_pretrained('TimKond/diffusion-detection') |
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model = BeitForImageClassification.from_pretrained('TimKond/diffusion-detection') |
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image = Image.open("2980_saltshaker.jpg") |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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``` |
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## Training and evaluation data |
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[BEiT-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) was loaded as a base model for further fine tuning: |
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As negatives a subsample of 10.000 images from [imagenet-1k](https://huggingface.co/datasets/imagenet-1k) was used. Complementary 10.000 positive images were generated using [Realistic_Vision_V1.4](https://huggingface.co/SG161222/Realistic_Vision_V1.4). |
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The labels from imagenet-1k were used as prompts for image generation. [GitHub reference](https://github.com/TimKond/diffusion-detection/blob/main/data/DatasetGeneration.py) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
