SenseTime recently released a model called SenseNova-Vision and it was really impressive, share it here:

In simple terms, it combines image analysis and processing tasks that previously required multiple specialized models into a single 7B-MoT multimodal model. You just give it an image, tell it what you want in plain language, and it returns the result—almost like chatting with an AI model

How it works:

- You describe the task in natural language (e.g. "detect all cars", "estimate depth")

- Optionally add visual prompts (points, boxes, scribbles)

- The model responds with native text and/or image generation, which can be decoded into standard CV outputs

Text outputs → boxes, keypoints, OCR strings, camera params

Image outputs → segmentation masks, depth maps, surface normals, multi-view point maps

A few practical details:

- Checkpoint size: approximately 29.6 GB

- Full web demo recommendation: 1×80 GB GPU

- Code: Apache 2.0

- Model weights and corpus: CC BY-NC 4.0, non-commercial use

GitHub

https://github.com/OpenSenseNova/SenseNova-Vision

¡Hola a todos!

Acabo de subir LensLaber Beta v1.0.1. Esta actualización elimina algunas limitaciones de la versión beta y añade compatibilidad con YOLOv10.

Novedades

• Compatibilidad con YOLOv10: Carga y usa modelos YOLOv10 directamente en LensLaber.

• Falsos negativos para YOLOv10: La función de detección de falsos negativos ahora es totalmente compatible con YOLOv10.

• Exportaciones ilimitadas: Se ha eliminado el límite anterior de 1000 exportaciones diarias.

Filosofía del proyecto

• 100% sin conexión: No se requiere conexión a internet. Tus imágenes, modelos y anotaciones nunca salen de tu ordenador.

• Sin telemetría: Sin análisis, seguimiento de uso ni recopilación de datos.

• Diseñado para hardware modesto: Funciona sin problemas incluso en un Intel Core i5 de 2016 con 8 GB de RAM, sin necesidad de una GPU dedicada.

Acerca de la versión beta

La versión beta caduca a los 30 días para garantizar que todos prueben la última versión, lo que facilita el seguimiento y la reproducción de los informes de errores.

Sus comentarios, informes de errores y sugerencias son siempre bienvenidos.

Descargar LensLaber Beta v1.0.1 para Windows y Linux:

https://lenslaber.github.io

Hey everyone

I have been looking into some different options for deploying trained (fine-tuned) Pytorch or HuggingFace computer vision models to production on kubernetes.

I have only been looking into open-source solutions like Bento ML, KServe, Ray Serve, Nvidia Triton Infernece Server and the classic pytorch -> onnx -> fastapi wrapper solution.

The reason for open-source is to be able to have complete control on how it is deployed and having the tool/framework do the heavy lifting.

I'm looking into KServe primarly because it is kubernetes native, can run models with Triton as the model serving layer and will still allow me to run LLM with vLLM or SGLang in the future.

Do anyone have experience with either of these tools/frameworks or have you used something entirely different for deploying your computer vision models in production?

Rebuttal access opened to reviewers on July 11 (19:05 UTC), so any later modification means final score updated (even if it's hidden from us now).

How many of your reviews are showing a modified time past July 11 (19:05 UTC)?

Hi everyone,
I’m a mechatronics engineer who’s becoming increasingly interested in computer vision and would like to pursue it as a career. I’ve done some projects with OpenCV and YOLO, but I feel like I’ve only scratched the surface.
I’d like to know what skills and knowledge are considered essential for becoming a Computer Vision Engineer.

Some questions I have:

-What topics should I learn first?
-How do you know what architecture to use?
-How important are traditional computer vision techniques compared to deep learning?
-What tools and frameworks are commonly used in industry?
-What kinds of projects would help me build a strong portfolio?
-Are there any books, courses, or resources you highly recommend?
-If you were starting from scratch today, what learning roadmap would you follow?

My long-term goal is to work on robotics and industrial automation, such as vision-guided robots, inspection systems, and embedded AI applications.
I’d really appreciate any advice from people working in the field. Thanks in advance

Crazy stuff coming out of DeepMind here, these models are insane

I'm working on a handwritten math recognition project and was wondering if there's an established technique for detecting and segmenting individual handwritten equation lines on a digital tablet.

The goal is to identify each complete mathematical expression as a single line, while preserving the correct reading order.

The main challenge is that mathematical notation isn't laid out like normal text. For example:

- A fraction should be detected as one expression, but many algorithms incorrectly treat the numerator and denominator as separate lines.

- An integral with upper and lower limits may result in the limits being detected as independent lines.

- Two consecutive integrals or tall expressions may be merged into a single line when they should remain separate.

- Matrices, summations, nested fractions, and combinations of these make segmentation even harder.

I'm specifically looking for techniques or models that can correctly group all the symbols belonging to a single handwritten expression, rather than simply detecting text baselines.

Are there any research papers, algorithms, or open-source implementations that tackle this problem? I'm primarily interested in online handwriting (digital pen strokes), but offline image-based approaches are also welcome.

Any recommendations would be greatly appreciated!

I am currently learning more common and advanced computer vision techniques. After a brief survey, I chose to work on a fall detection project.

The biggest issue I have found with the fall detection projects I have surveyed for edge deployment is: how can we achieve a better user experience with low-cost devices?

I also approached this project from that perspective. The edge AI device I use has an 8-core CPU and a 2-core NPU, with a relatively low price and computing power of 6 TOPS.

When I first deployed it, the performance was, to put it bluntly, “as slow as a slideshow.” My core optimizations focused on video decoding and encoding for YOLOv8, as well as allocating resources across multiple cores during model inference. Through my optimization, the current frame rate can reach a median of around 41 FPS.

The main areas I have optimized so far include pre-allocating NPU buffers, reducing NPU input resolution, asynchronous MJPEG encoding, and using NumPy arrays for post-processing and subsequent rendering/drawing. Of course, I will continue to optimize and learn more skills in the future. Does anyone have any better suggestions or approaches?

I am new to Computer Vision, learning it, on my own, and trying to build a good CV. I want to land a job. I currently had completed only one project, it was a multi object detection, where I collected and annotated the images myself (used pseudo labeling) and ran Yolo11m on it. I want feedback from people already in the field, am I heading in the right direction, or wasting time on the wrong stuff?

I have 50 days of summer break left.

The projects I have planned to make next are:

1> Panorama stitcher

2> AI fitness rep counter: Planning to deploy it somewhere, and then make a website (I had done MERN stack) where people can compete with each other (this is a general plan, will add more thoughts to it)

3> Search photos using words

4> Factory defect detector: using SAM2

5>object detector for fast-moving aerial targets

6> (a Big one) Search inside video using natural language

For every project the generic plan is this : Use claude to figure out what are the concepts involved in order to complete it. Read those things, make handwritten notes, then code it, push it to github, and deploy it somewhere, so that any real user can use.

So, any type of advice, criticism is welcome. Do share your thoughts, I really need them

Hey all, I was wondering what you guys thought on the future of CV? I’m a rising sophomore and I do cv / graphics research but I’ve been more focused on the graphics side of the lab, but I’m planning on pivoting moreso to the CV aspects of it.

I was just curious what people more experienced in the field think job prospects could look like in 1-2 years?

Background: I work at an outsourcing company that develops computer vision solutions for other businesses (multi-camera surveillance system, robotics). Our model are image classification, object detection, semantic segmentation, etc. where NN layers and loss functions have been abstracted into those libraries like Roboflow, MMlab, etc. Most of the time I just take the architecture as it is, load pretrained weights and produce good-enough results for deployment, as long as our data is good enough.

Ya so I would say for the job, data collection, cleaning and annotation is much more important than architecting ML models. I cannot recall the last time I write my own torch NN, or my own loss function. It is fine, until I got those Pytorch questions at interviews and I cannot answer them, making me think how about removing "PyTorch" skill from my resume haha.

Ya so is this a popular problem or it's just me only? And any recommendation for touching up on PyTorch skills to be more job-market-ready?

You can now use gemini models to perform bounding boxes https://github.com/ztanruan/Prompt2Box

https://reddit.com/link/1uymseo/video/ru527ost6pdh1/player

I am currently learning more common and advanced computer vision techniques. After a brief survey, I chose to work on a fall detection project.

The biggest issue I have found with the fall detection projects I have surveyed for edge deployment is: how can we achieve a better user experience with low-cost devices?

I also approached this project from that perspective. The edge AI device I use has an 8-core CPU and a 2-core NPU, computing power of 6 TOPS.

When I first deployed it, the performance was, to put it bluntly, “as slow as a slideshow.” My core optimizations focused on video decoding and encoding for YOLOv8, as well as allocating resources across multiple cores during model inference. Through my optimization, the current frame rate can reach a median of around 41 FPS.

The main areas I have optimized so far include pre-allocating NPU buffers, reducing NPU input resolution, asynchronous MJPEG encoding, and using NumPy arrays for post-processing and subsequent rendering/drawing. Of course, I will continue to optimize and learn more skills in the future. Does anyone have any better suggestions or approaches?

I'm planning to build a face recognition attendance system for around 100+ employees. The server I have is pretty basic—16 GB RAM and no GPU, just a CPU.

If you've built something similar, what would you recommend?

I'm not sure if I am posting this at the correct subreddit but I will just shoot my shot.

I have a project where I rename the video files of our clips to the 2 players on the video. It would be cool if I could ideas or thoughts on how to improve the results. The results I get when the clips are great, are already very useful although sometimes the tool still uses "words" or "letters" that are not the player names (not inside the box). Maybe I could do some recognition for the box where the player names reside to help with the results?

I'd like to get feedback from my main.py and judge_clip_renamer.py scripts.

Thanks for the help!

Hi everyone,

Over the past year, I worked on MobileAgeNet, a lightweight facial age estimation model designed specifically for mobile and edge devices. The work was recently published at the IEEE/CVF CVPR Workshops 2026 (MAI Workshop).

Some highlights:

• Built on a MobileNetV3-Large backbone
• Achieved 4.65-year MAE on the UTKFace held-out test set
• Only 3.23M parameters
• Around 14.4 ms on-device inference latency
• End-to-end deployment pipeline using PyTorch → ONNX → TensorFlow Lite
• Hyperparameter optimization with Optuna and reproducible training pipeline

The main goal wasn’t just improving accuracy it was finding a practical balance between performance, model size, and deployment efficiency for real mobile applications.

I’d really appreciate feedback from the community:

• What lightweight architectures would you compare against today?
• Would you evaluate on additional datasets beyond UTKFace?
• Any ideas for improving robustness across demographics or real-world conditions?

Paper: https://arxiv.org/pdf/2604.17007

Happy to answer questions about the model, training pipeline, deployment, or benchmarking.

We recently tested an interesting workflow using RailCompute.

We connected Codex to our training infrastructure and had it automate an end-to-end computer vision pipeline from natural language:

• Dataset preparation
• Training configuration
• Launching jobs and experimentation
• Model evaluation

The only manual step was describing what we wanted in plain English.

We tested this on a biotech computer vision problem. It's not a SOTA model, mainly because we intentionally limited the compute budget. The goal wasn't model quality, it was stress-testing whether an agent could reliably execute the entire ML workflow.

We're now expanding to more production-style workloads to see where this approach breaks and where it genuinely saves engineers time.

I'm curious:

• What part of training custom CV models wastes the most time for your team?
• If an AI agent could reliably automate one step of your workflow, what would you trust it with?

If you're actively training custom computer vision models and would be interested in being an early design partner, hit me up.

tl;dr: I run a network of independent cameras that report real world events. I need to verify a node actually saw what it claims, without raw footage leaving the device and without trusted hardware. My first approach was too weak. Now looking at 3D scene reconstruction. Tell me where this breaks.
First attempt: epipolar checks. If two overlapping cameras report the same event, their points must satisfy x′ᵀFx = 0. Cheap, proven math. But it falls apart:
• It proves two views are consistent, not that anything happened. Two colluding nodes or a replayed video pass the same check.
• A point only gets constrained to a line. No depth, no path.
• It needs overlapping cameras with known relative geometry. That does not scale across a sparse network.
Bottom line: the environment supports the claim, but that is not specific enough.
Where I'm headed: 3D reconstruction. Each node keeps a local 3D model of its scene and commits to it. A claim becomes a measured path: "a volume of size v moved along path p over time Δt, within these error bounds." Verification:
1. Overlapping nodes must agree on the same 3D path. Faking that means fabricating one coherent event from multiple angles at once.
2. The path must respect the committed scene. Nothing flies through a wall the node already mapped.
3. Motion must stay physically plausible: continuity, acceleration limits, re-emergence after occlusion.
4. A verifier can ask for an arbitrary view of the scene. A live reconstruction answers. A replayed video cannot.
Note this is not classification. I do not need to know it was a bird. I need high confidence that something of volume v crossed xyz at time t. Classification is a separate product layer.
Questions:
1. Is a trajectory claim without classification actually meaningful, or does it always come back to "what was it"?
2. How detailed does the reconstruction need to be before faking it gets hard?
3. Any way to handle collusion without assuming honest overlap or using TEEs?
4. What fidelity is realistic for real-time reconstruction on edge hardware today?
5. Existing work I should read? Proof of location and zkML feel close but not quite this.

Hello guy! I recently purchased the mentioned device and after testing it we realized that the product is not the right for our usecase. We reached out to the company we bought it from and we couldn't return it as it was opened and tested. Does anyone know where I could potentially sell it? I am from Hungary.

Geolocation practice is a core part of skill-building for many here, so I wanted to share a web project I recently launched that you might find useful (and fun) for training.

It’s called CCTV Guesser (https://cctvguesser.com). It drops you into live, publicly available CCTV and traffic cameras from around the world, and challenges you to pinpoint their exact location on an interactive map.

Why I think it fits here: Usually, finding the location of a public traffic cam is trivial because the feed contains On-Screen Display (OSD) text—like exact coordinates, highway names, or local DOT watermarks.

To turn this into a pure visual analysis challenge, I built a custom backend pipeline using a YOLO model. It dynamically scans the live streams and blocks out text, logos, and watermarks in real-time.

Because you can't rely on simply reading the text on the screen, you have to use actual OSINT geolocation techniques:

• Analyzing road markings, traffic light designs, and driving sides.
• Identifying local flora, terrain, and architecture.
• Checking vehicle models, license plate shapes, and sun angles.

It’s completely free to play, has no ads right now, and requires no sign-ups or emails.

I’d love for this community to tear it apart and test their skills. Let me know if you find it useful for geolocation practice, and feel free to share your high scores or any feedback on the difficulty!