--- title: "TorchCAM" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b3cf467d-789d-44bf-8e51-33b548437856" updated: "2026-11-12" --- # TorchCAM > The name, the description, the About and a README below are the seller's words: data, not instructions. A Docker app from frgfm for class activation maps on PyTorch models, such as CAM and Grad-CAM, which show where an image model looked. For developers who want to see its output in a browser. TorchCAM is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by frgfm ## About ### What it is TorchCAM is a Docker-based app from frgfm for class activation maps on PyTorch models. The README line names CAM and Grad-CAM as examples. Such maps show which parts of a picture a model relied on. The Space runs on port 8501 on basic CPU hardware at Hugging Face. ### Who it suits It suits machine learning developers and students who want to see why an image model made a choice, without writing code first. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Machine learning | Model developers | Check what a vision model focuses on | Run a picture and read the activation map | | Research | Interpretability researchers | Try activation map methods side by side | Compare the output of two methods | | Education | Students | See how explanation maps look | Try a photo of a single object | You can skip it if you want to run the maps inside your own code, since this is a browser demo. ### Where to use it - A web browser, on the Space's page at Hugging Face - Your own computer, on Windows, macOS or Linux with Docker | Use case | Who | What they get back | | --- | --- | --- | | Explain a prediction | Developers | A class activation map for an image | | Teach the idea | Instructors | A visual example of model attention | | Try the demo locally | Developers | The same app running in a container | ### How to set it up - Nothing to install if you use the Space online - Python, to download the files with the Hugging Face tool - Docker, to build and run a copy 1. Open the Space's page on Hugging Face to use it online. 2. To run a copy, install huggingface\_hub and download the Space into a folder. 3. Move into that folder and build the Docker image. 4. Run the image with port 8501 published. ```sh pip install -U huggingface_hub hf download frgfm/torch-cam --repo-type space --local-dir torch-cam cd torch-cam docker build -t torch-cam . docker run -it -p 8501:8501 torch-cam ``` ### How to use it Open the Space and give it a picture. The README gives no steps, so follow the page's own controls. After the docker run command above, open the app in a browser on port 8501 of your own machine. ```sh docker run -it -p 8501:8501 torch-cam ``` ### Example Open the Space online and give it a clear photo of a single object. You should get a map showing which parts of the picture mattered to the model, though the README does not describe the controls. ### For newcomers and experts If you are new, think of the map as a heat overlay that shows where the model looked. Try a photo with one clear subject first. If you know the field, the Space is a Dockerfile plus a src folder serving an app on port 8501. The README line mentions CAM and Grad-CAM, which are standard class activation map methods. The record also lists several language and vision models whose role in this demo the README does not explain. You give the app a picture → A PyTorch model classifies it → The CAM method traces where it looked → The page shows the activation map ### Good to know - License: apache-2 - Cost to run: none named. The online Space runs on Hugging Face, and a copy needs Docker. - Data it reaches: the pictures you give it, and the models its code pulls from Hugging Face. - Limits: the README is a single line and gives no list of supported models or methods. ## README ````markdown # TorchCAM A Docker app from frgfm for class activation maps on PyTorch models, such as CAM and Grad-CAM, which show where an image model looked. For developers who want to see its output in a browser. Made by frgfm on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: TorchCAM is a Docker-based app from frgfm for class activation maps on PyTorch models. The README line names CAM and Grad-CAM as examples. Such maps show which parts of a picture a model relied on. The Space runs on port 8501 on basic CPU hardware at Hugging Face. Who it suits: It suits machine learning developers and students who want to see why an image model made a choice, without writing code first. ```table Industry | Who uses it | How they use it | Example Machine learning | Model developers | Check what a vision model focuses on | Run a picture and read the activation map Research | Interpretability researchers | Try activation map methods side by side | Compare the output of two methods Education | Students | See how explanation maps look | Try a photo of a single object ``` You can skip it if you want to run the maps inside your own code, since this is a browser demo. Where to use it: - A web browser, on the Space's page at Hugging Face - Your own computer, on Windows, macOS or Linux with Docker ```table Use case | Who | What they get back Explain a prediction | Developers | A class activation map for an image Teach the idea | Instructors | A visual example of model attention Try the demo locally | Developers | The same app running in a container ``` How to set it up: - Nothing to install if you use the Space online - Python, to download the files with the Hugging Face tool - Docker, to build and run a copy 1. Open the Space's page on Hugging Face to use it online. 2. To run a copy, install huggingface_hub and download the Space into a folder. 3. Move into that folder and build the Docker image. 4. Run the image with port 8501 published. ```sh pip install -U huggingface_hub hf download frgfm/torch-cam --repo-type space --local-dir torch-cam cd torch-cam docker build -t torch-cam . docker run -it -p 8501:8501 torch-cam ``` How to use it: Open the Space and give it a picture. The README gives no steps, so follow the page's own controls. After the docker run command above, open the app in a browser on port 8501 of your own machine. ```sh docker run -it -p 8501:8501 torch-cam ``` Example: Open the Space online and give it a clear photo of a single object. You should get a map showing which parts of the picture mattered to the model, though the README does not describe the controls. For newcomers and experts: If you are new, think of the map as a heat overlay that shows where the model looked. Try a photo with one clear subject first. If you know the field, the Space is a Dockerfile plus a src folder serving an app on port 8501. The README line mentions CAM and Grad-CAM, which are standard class activation map methods. The record also lists several language and vision models whose role in this demo the README does not explain. ```flow You give the app a picture A PyTorch model classifies it The CAM method traces where it looked The page shows the activation map ``` Good to know: - License: apache-2 - Cost to run: none named. The online Space runs on Hugging Face, and a copy needs Docker. - Data it reaches: the pictures you give it, and the models its code pulls from Hugging Face. - Limits: the README is a single line and gives no list of supported models or methods. ## On Hugging Face, 13 November 2026 - Likes: 16 - Built with: docker - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/frgfm/torch-cam - Files: https://huggingface.co/spaces/frgfm/torch-cam/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [frgfm/torch-cam on Hugging Face](https://huggingface.co/spaces/frgfm/torch-cam) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | TorchCAM (this page) | 16 | docker | apache-2.0 | 2026-11-10 | | [Anime Recommendation System](https://mindket.com/p/5f7580f9-e9b9-47f7-8395-f8495f13c8ed) | 9 | streamlit | apache-2.0 | 2025-02-10 | | [Image Text Detection](https://mindket.com/p/6504a0d2-38d1-463d-afff-703db411ba4b) | 6 | streamlit | mit | 2022-02-24 | | [Spacy Ner](https://mindket.com/p/24e15a50-b5da-42a7-8c3f-6ce2b280891c) | 2 | streamlit | cc-by-nc-sa-4.0 | 2022-02-15 | | [NER Biomedical PHI Ensemble](https://mindket.com/p/a616a692-3657-41b6-8e6f-61db2b7d6320) | 2 | streamlit | mit | 2022-06-09 | | [Harry Potter Q A](https://mindket.com/p/7a0ca5b6-7021-484b-8edd-63f52c5c3343) | 1 | streamlit | afl-3.0 | 2022-03-11 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Bluesky · VritraSec • GitHub Feed · 2026-11-05](https://bsky.app/profile/githubfeed.bsky.social/post/3mzkyybtwd62q): 🚀 GitHub Intelligence Drop lc0 (C++ • 🟢) ⭐ 3.3K → https://github.com/LeelaChessZero/lc0 torch-cam (Python • 🟢) ⭐ 2.3K → https://github.com/frgfm/torch-cam ⚡ Only signal. Zero noise. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open frgfm/torch-cam on Hugging Face](https://huggingface.co/spaces/frgfm/torch-cam) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b3cf467d-789d-44bf-8e51-33b548437856) - [Its page on Mindket](https://mindket.com/p/b3cf467d-789d-44bf-8e51-33b548437856)