ZEzerojames·Oct 23, 2024Submission
Roboflow Notebooks: 60+ computer vision modeling notebooks
1 points · Open discussionroboflow
notebooks
Jupyter Notebook2023–2024
roboflow / notebooks
notebooks
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
3Distinct people
6Mentions
0Explicit recommenders
9.7KGitHub stars
What to know before exploring
What it is
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
- roboflow/inference · linked in 2 of the same discussions
- roboflow/supervision · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month6 dated mentions
Observed mentions: 2023-03-10 15:44:06 UTC to 2024-10-23 03:22:40 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 2023 | 2023-03-01 00:00:00 UTC | 2023-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2023 | 2023-04-01 00:00:00 UTC | 2023-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2023 | 2023-05-01 00:00:00 UTC | 2023-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2023 | 2023-06-01 00:00:00 UTC | 2023-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2023 | 2023-07-01 00:00:00 UTC | 2023-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2023 | 2023-08-01 00:00:00 UTC | 2023-09-01 00:00:00 UTC | 2 | 1 | 1 |
| Sep 2023 | 2023-09-01 00:00:00 UTC | 2023-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2023 | 2023-10-01 00:00:00 UTC | 2023-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2023 | 2023-11-01 00:00:00 UTC | 2023-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2023 | 2023-12-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2024 | 2024-01-01 00:00:00 UTC | 2024-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2024 | 2024-02-01 00:00:00 UTC | 2024-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2024 | 2024-03-01 00:00:00 UTC | 2024-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2024 | 2024-04-01 00:00:00 UTC | 2024-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2024 | 2024-05-01 00:00:00 UTC | 2024-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2024 | 2024-06-01 00:00:00 UTC | 2024-07-01 00:00:00 UTC | 1 | 0 | 1 |
| Jul 2024 | 2024-07-01 00:00:00 UTC | 2024-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2024 | 2024-08-01 00:00:00 UTC | 2024-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2024 | 2024-09-01 00:00:00 UTC | 2024-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2024 | 2024-10-01 00:00:00 UTC | 2024-11-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
6 sourcesFWIW you can use roboflow models on-device as well. detect.roboflow.com is just a hosted version of our inference server (if you run the docker somewhere you can swap out that URL for localhost or wherever your self-hosted one is running). Behind the scenes it’s an http interface for our inference[1] Python package which you can run natively if your app is in Python as well.
Pi inference is pretty slow (probably ~1 fps without an accelerator). Usually folks are using CUDA acceleration with a Jetson for these types of projects if they want to run faster locally.
Some benefits are that there are over 100k pre-trained models others have already published to Roboflow Universe[2] you can start from, supports many of the latest SOTA models (with an extensive library[3] of custom training notebooks), tight integration with the dataset/annotati
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on tools that are at the core of Roboflow for creating custom models, and good support for common downstream tasks via supervision[4].
[1] https://github.com/roboflow/inference
[2] https://universe.roboflow.com
[3] https://github.com/roboflow/notebooks
[4] https://github.com/roboflow/supervision
Yeah, inference[1] is our open source package for running locally (either directly in Python or via a Docker container). It works with all the models on Universe, models you train yourself (assuming we support the architecture; we have a bunch of notebooks available[2]), or train in our platform, plus several more general foundation models[3] (for things like embeddings, zero-shot detection, question answering, OCR, etc).
We also have a hosted API[4] you can hit for most models we support (except some of the large vision models that are really GPU-heavy) if you prefer.
[1] https://github.com/roboflow/inference
[2] https://github.com/roboflow/notebooks
[3] https://inference.roboflow.com/foundation/about/
[4] https://docs.roboflow.com/deploy/hosted-api
In Supervision: Reusable Computer VisionShow HN: How to Notebooks for computer vision models and techniques
3 points · Open discussionIt’s an easy to use inference server for computer vision models.
The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface).
It’s backed by a bunch of component pieces:
* a server (so you don’t have to reimplement things like image processing & prediction visualization on every project)
* standardized APIs for computer vision tasks (so switching out the model weights and architecture can be done independently of your application code)
* model architecture implementations (which implement the tensor parsing glue between images & predictions) for supervised models that you've fine-tuned to perform custom tasks
* foundation model implementations (like CLIP & SAM) that tend to chain well with fi
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ne-tuned models
* reusable utils to make adding support for new models easier
* a model registry (so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights)
* data management integrations (so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild)
* ecosystem (there are tens of thousands of fine-tuned models shared by users that you can use off the shelf via Roboflow Universe[1], it plays nicely with our other open source tools like Autodistill[2] and Supervision[3], and there is a ton of content around fine-tuning models you can use with it[4])
Additionally, since it's focused on computer vision, it has specific CV-focused features (like direct camera stream input) and makes some different tradeoffs than other more general ML solutions (namely, optimized for small, fast models that run at the edge & need support for running on many different devices like NVIDIA Jetsons and Raspberry Pis in addition to beefy cloud servers).
[1] https://universe.roboflow.com
[2] https://github.com/autodistill/autodistill
[3] https://github.com/roboflow/supervision
[4] https://github.com/roboflow/notebooks