Explain PyTorch model decisions with attribution algorithms
Captum is an open-source Python library that helps developers understand why their AI models make specific predictions. It provides model interpretability for PyTorch models through various attribution methods, working with both vision and text models without requiring major code changes. Essential for AI builders who need to debug models and meet regulatory requirements for explainable AI.
Works with vision, text, and other data types
Integrates seamlessly with existing PyTorch models
Extensible platform for developing new algorithms
Install via conda (recommended) with 'conda install captum -c pytorch' or via pip with 'pip install captum'
Supports most PyTorch models with minimal modifications to original code
Provides model interpretability through attribution algorithms to understand model decisions
This tool is best for:
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Use Captum on your favorite device - available across multiple platforms for flexibility.
Recent data shows that Captum has 25.8K monthly visits (33.9% increase from the previous month), 40.0% bounce rate, and average 2.50 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 5 countries worldwide, led by United States contributing 25% of total traffic. SEO performance is shown by 5 tracked keywords, with "captum" being the top-performing keyword with 1.1K monthly searches. See below for more info.
Monthly Visits
25.8K
Pages per Visit
2.50
Bounce Rate
40.0%
Average Time on Site
39s
| SEO Keyword | Volume | CPC |
|---|---|---|
| captum | 1.1K | - |
| captum attribute | 140 | - |
| captim model interpretability | 0 | - |
| captum library | 220 | - |
| how does captum integrated gradients work | 70 | - |
| Source | Traffic Share |
|---|---|
| Direct | 38% |
| Search | 46% |
| Social | 4% |
| Referrals | 11% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 25% |
 | 15% |
 | 10% |
 | 9% |
 | 7% |
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