Coherence Detection
Model Description
A ResNet-34 fine-tuned on a personally-curated dataset to classify images into one of three categories:
- Coherent
- Incoherent
- Semi-Incoherent
Key Feature: This model is provided in safetensors format with a production-ready loading wrapper (model_architecture.py) that handles FastAI's AdaptiveConcatPool2d layer automatically.
Installation and Usage
Important: This model is provided in safetensors format and requires the model_architecture.py module for proper loading.
- Install
1# First install PyTorch with the correct CUDA version for your system
2# Visit https://pytorch.org/get-started/locally/ for the right command
3
4# Example for CUDA 11.8:
5pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
6
7# Example for CPU-only:
8pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
9
10# Once torch is installed and verified:
11pip install -r requirements.txt
- Download entire repo and run the example:
- Use in your code:
1from example_usage import CoherenceClassifier
2# Initialize with your model
3classifier = CoherenceClassifier("coherence_model.safetensors")
4# Predict on an image
5result = classifier.predict("your_image.jpg", return_probs=True)
6print(result) # {'coherent': 0.85, 'incoherent': 0.05, 'semi-incoherent': 0.10}
Note: The model uses FastAI's AdaptiveConcatPool2d layer. Import and use model_architecture.py which handles this automatically. The example_usage.py script demonstrates the proper import pattern.
Model Architecture
Backbone: ResNet-34 (via FastAI's default)
Pooling: AdaptiveConcatPool2d (FastAI-specific)
Input size: 224x224 (standard ImageNet normalization)
Training Data
For version 1.0, a small dataset was used of ~20k images in coherent category, and ~ 12k images in both incoherent and semi-incoherent categories. Access to dataset will not be made available. An attempt was made to include a wide range of content in the coherent category to avoid false positive incoherency results.
Limitations
- PyTorch environment
- Requires
model_architecture.py module for proper loading (handles FastAI-specific layers automatically)
Note especially that while an attempt was made at classifying the less obvious but definitely noticeable generation failures like the following into the "semi-incoherent" category, these are much harder to detect and in the current iteration of this model there is no expectation that they will be observed in any particular coherence category, especially when these particular errors occur in a small part of the image.
- Extra or missing limbs, fingers, or facial features
- Disproportionate body, head, or limbs
- Anatomically implausible joint configurations
Intended Use
This model is intended only for evaluating the coherence of AI-generated images.
- Do not use it to classify or moderate real photographs, as it may produce nonsensical and harmful misclassifications.
- A "semi-incoherent" or "incoherent" result should be a flag for human review, not necessarily an automatic basis for censorship.
Comment
Given the low coherence rate of results produced by early image generation models, it was very surprising that a model was not found for this purpose, necessitating the creation of this one for high-volume review scenarios.
Perhaps models such as this one are avoided or seen as improper due to the perceived danger they pose in introduction of bias to image analysis, however it is highly likely that image generators would rather have at least some bias towards coherence and a somewhat clear mind when reviewing their image output than no bias and a mind littered with the psychologically-damaging results of obviously-failed generations which have little to do with the prompter's intent.
Model Card Authors
Tom Hall
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