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FeatLens
Visualize feature maps from any vision model effortlessly.
Feature visualization is part of my daily routine as a computer vision researcher. But doing it across different model providers was always a mess. So this weekend, I finally fixed it 🧩
Every model lives somewhere different, torch.hub, timm, HuggingFace, some random GitHub repo, and each one needs its own glue code just to pull out and visualize feature maps. I got tired of rewriting that glue every single time. 🤖
So I built FeatLens: a model-agnostic framework to see what any vision model actually encodes.
🔹 Load from anywhere — timm, HuggingFace, torch.hub (like V-JEPA), an external repo, or your own nn.Module (yes, CNNs too)
🔹 Any layer, laid out as a clean model × layer grid
🔹 Color the features by PCA, cosine similarity, k-means, foreground, saliency, or attention-rollout
🔹 Match patches across two images, batch a whole folder, or sweep a video clip
Just swap the models, and watch the features change completely. It's oddly satisfying to look at.
This was a weekend project I built with my friend Claude, from core design to docs, tests, and CI.
It's open source, and you can just pip install featlens
⭐ GitHub: https://lnkd.in/dGvb7uNi
📖 Docs: https://lnkd.in/dQMV9-Bc
🤗 Live demo: https://lnkd.in/d6HeRJk6
If it's useful in your work, it's citable, and contributions are very welcome.
Which model would you want to peek inside first? 👀
curious how this handles models that aren't trained on imagenet, does the PCA still produce something visually meaningful or do you need to tweak the preprocessing per backbone
How does this handle really deep models like a 50 layer resnet without choking on memory, and can I export the visualizations as a video to watch how features evolve across layers?
the per-channel intensity scaling in the PCA-to-RGB output looks really well-tuned, colors stay distinct without blowing out even on deeper layers.
About FeatLens on Product Hunt
“Visualize feature maps from any vision model effortlessly.”
FeatLens was submitted on Product Hunt and earned 15 upvotes and 7 comments, placing #25 on the daily leaderboard. Model-agnostic feature-map visualization: PCA-to-RGB feature maps from any vision model and any layer.
FeatLens was featured in Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 204.7k products, making this a competitive space to launch in.
Who hunted FeatLens?
FeatLens was hunted by Turhan Can KARGIN. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Turhan Can KARGIN