captum
SkillAI & modelsCaptum (PyTorch) — model interpretability and feature attribution. Integrated Gradients, DeepLIFT, SmoothGrad, Occlusion, SHAP approximation, and Layer-wise Relevance Propagation. For vision and text models.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the captum skill
What this skill tells your AI
The instructions your AI receives, as published by mkurman/zorai in skills/scientific-skills/captum/SKILL.md and read by ahel’s review.
Overview
Captum (Comprehension in PyTorch) provides model interpretability for PyTorch models. Implements Integrated Gradients, Gradient SHAP, DeepLIFT, Occlusion, Feature Ablation, and Layer Conductance. Supports computer vision, NLP, and tabular models.
Installation
uv pip install captum
Integrated Gradients
import torch
import torch.nn as nn
from captum.attr import IntegratedGradients
model = nn.Linear(10, 2)
input = torch.randn(1, 10)
baseline = torch.zeros(1, 10)
ig = IntegratedGradients(model)
attrs = ig.attribute(input, baseline, target=0)
print(f"Feature attributions: {attrs}")
Occlusion
from captum.attr import Occlusion
occ = Occlusion(model)
attrs = occ.attribute(input, target=0, sliding_window_shapes=(1,)) # 1D
print(attrs)
Visualization
from captum.attr import visualization as viz
_ = viz.visualize_image_attr(
attrs.squeeze().numpy(),
original_image=input.squeeze().numpy(),
method="heat_map",
sign="absolute_value",
show_colorbar=True,
)
References
Signals
- GitHub stars
- 324
- Forks
- 26
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
captum- Source
- github.com/mkurman/zorai