Deploy vision models with confidence
Understand your models, predict failure modes, and prevent bias with the Leap Interpretability Engine.
Understand what your model has learned
Enable targeted
fine-tuning
Predict behaviour
on unseen data
Understand any vision model
The Interpretability Engine works with all differentiable image model architectures, from convolutional networks to vision transformers. It requires no data, and you don't need to upload your model!
Best of all, it's free.
Efficiently improve performance
Debugging neural networks is a famously dark art – still mostly conducted with trial and error, fiddling with hyperparameters, and throwing more (possibly expensive) data at the problem. Knowing exactly what the model has learned makes it easier to figure out why it’s not performing well and fix it, with data augmentation specifically targeted to address the model's weaknesses.
Predict and prevent mistakes
Typically you’d try to predict the deployment performance of a model by looking at various metrics over its test performance. But, there are no guarantees that the test set contains all possible edge cases or failure modes. We allow you to see exactly what a model has learned, enabling the prevention of embarrassing or dangerous failure modes – even if they're not captured in your test data.
Mitigate bias
As AI use becomes more and more prevalent, it's all too easy for powerful models to encode and reinforce existing biases from the data they're trained on – and often in pernicious ways that are hard for humans to identify from the data alone. Our interpretability can help you catch these biases and fix them before deployment, preventing hidden discrimination.
The Interpretability Engine
See what your model has learned.
Discover learned features, validate coherence, and identify biases.
Identify where and why your model is confused.
Predict confusion, isolate entangled features, and quantify misclassification risk.
Understand predictions on individual samples.
Validate predictive features and understand how your model makes predictions.
Easy integrations
from leap_ie.vision import engine
config = {
"leap_api_key": "your_leap_api_key",
"wandb_entity": "your_wandb_entity",
"wandb_api_key": "your_wandb_api_key",
}
df_results, dict_results = engine.generate(
project_name="your_wandb_project_name",
model=your_model,
class_list=["hotdog", "not_hotdog"],
config=config,
)
Case Studies
Tank Detection
Leap's Interpretability Engine uncovers a dangerous bias in a tank detection model, despite it outwardly exhibiting high accuracy on test data. We show how prototype generation can also be used during training, to guide effective data augmentation and hyperparameter selection. Using these insights we strategically retrain the model, optimising for robust, generalisable feature learning, to enable safer deployment in high-stakes scenarios.