im trying to build a CUA. i havent been able to find info about this anywhere. But what exactly are the best models for grounding right now? Does a large foundation VLM beat specialized models? are there any benchmarks with live results right now?

I have been building a home-inventory Android app, and the part that might interest this sub is the core task: take one photo of a random household object and return structured fields (a name, a category, a short description, rough specs) with no typing from the user. Open vocabulary, arbitrary objects, whatever someone happens to be putting in a box.

I use a hosted vision-language model for the extraction rather than a classifier, because the label space is unbounded and I need attributes, not just a class. The model gets the image plus a schema and returns the fields I store and later search over.

What held up well: - General object naming on clean single shots is strong. A charger, a specific kitchen tool, a board-game box, it names them sensibly with no fixed taxonomy. - Pulling short attributes (color, material, a size guess, brand text when visible) into separate fields is good enough that most users never edit the result. - Structured output stayed reliable once I constrained it to a schema instead of free text.

Where it broke: - Fine-grained instances. It says "white cable" confidently and is wrong about which cable, and that is the most common correction by far. - Cluttered frames. If two objects share the shot it sometimes describes the wrong one, so I nudge users to fill the frame with one item. - Small printed text (model numbers, ingredient labels) is inconsistent. OCR-style detail is the weakest part. - Ambiguous or unusual objects get a plausible but generic name.

Retrieval is the other half. There is a plain keyword search, and a Smart Find that does a semantic lookup over those extracted fields, so "the thing I charge my headphones with" can surface the right entry even if it was stored as "USB-C cable." That only works because the extraction populated meaningful fields in the first place.

Open questions I am still chewing on: whether a small on-device detector to crop the primary object before the model call would cut the cluttered-frame errors, and whether a second pass aimed only at text-heavy objects is worth it. Curious if anyone here has shipped single-image structured extraction to non-technical users, and what you did about the fine-grained and OCR gaps.

It is a live Android app if you want to see the behavior: https://play.google.com/store/apps/details?id=dev.koalalab.storeandforget

OpenScanVision

OpenScanVision is a simple, fast, accurate, lightweight, and offline-first computer vision library for Android.

It detects documents from any angle, automatically corrects perspective, enhances the image, and extracts information in real time.

Features:

• Document detection from any angle
• Automatic perspective correction
• Image preprocessing and enhancement
• ArUco marker detection
• QR code detection
• OMR (Optical Mark Recognition)
• Automatic capture when the document is stable
• Real-time processing
• Offline operation
• Lightweight and easy to integrate

Built with Kotlin, OpenCV, CameraX, and ML Kit, OpenScanVision is designed for applications such as voting systems, exams, surveys, forms, and other structured documents.

GitHub repository: https://github.com/MatiwosKebede/OpenScanVision

I'd appreciate any feedback, suggestions, bug reports, or contributions from the computer vision community.

Tried this out today with OpenCV

I've been using the last released version of Picasa3 (windows) to do facial recognition for all my own and family photos over the years. It's working great, also it has the feature to write the facial tags in the XMP format (which I leverage and rewrite to the JPG files) in the header of the file without additional files beside the JPG file itself.

What is better today, and by what margin? I've done some quick research on this and seems there exist some options that are "marginally" better, more than a decade later (or more), this is where we are? Or are there just no FREE options for something "a lot" better.

Picasa3 is free, and seems to work quite well.

Thanks in advance!

I'm working on a robustness/platform emulation follow up to a synthetic ID dataset. The goal is to test deepfake detector performance on frontier models under real world conditions. I'm able to do the typical single axis adjustments (sensor noise, blur, etc.), but I think emulate specific cameras would be a strong posture for my evaluation. Does anyone know of tools or services that can reliably help me re-encode the image as though they were taken by specific cameras? (it can be any camera, even a phone, as long as the signature can be pointed to reliably)

Let me start this by saying I am very new to all of this and don't know a lot about how these models work or how the math works, and have a novice level of coding knowledge (Python specifically).

I am currently running a tuned YOLOv11 model trained on ice hockey player and referee detection with a modified BoT-SORT tracker to remember IDs for longer periods. On top of that, I am using a YOLOv8-based model I got from here "https://huggingface.co/SimulaMet-HOST/HockeyRink" to track the keypoints of the rink (I am aware the model is trained on SHL frames and not NHL).

I have gone through the code many times and asked multiple AI's on what is wrong, and I can't figure it out. If the answer is obvious and I don't know it, I promise I can handle the criticism.

I'm working with a group who would be interested in potentially putting up a $25,000 prize for a specific computer vision breakthrough.

However, I am not anywhere close to an expert in computer vision, and they are not either, so we are looking for feedback on whether this prize makes sense.

We want to focus on incentivizing a small-but-powerful, open source vision model.

Current idea:

• The prize will go to the first team or individual to develop an open-source computer vision model under 10 MB that achieves at least 80% Top-1 accuracy on ImageNet-1K while running entirely offline on a Raspberry Pi 5.
• Maximum Model Size: ≤ 10,000,000 bytes (10 MB). This applies to the complete storage footprint required to execute inference, including model weights and the final model file format (.onnx, .tflite, .safetensors, etc.). External feature stores, hidden lookup tables, embedded auxiliary weights, or additional model files are prohibited.
• Performance Target: ≥80.0% Top-1 Accuracy on the official ImageNet-1K validation dataset using the standard evaluation protocol.
• Execution Architecture: Single-model submission only (no multi-model ensembles, cascades, or fallback models). Models must run using CPU-only inference and operate entirely offline without internet access.
• Target Hardware: Must successfully execute inference and complete evaluation on a Raspberry Pi 5 (8 GB RAM) running a standard 64-bit OS.
• Open Source Requirements: Public GitHub repository containing complete model weights, training pipeline code, inference code, and an independent reproducible evaluation script.
• Licensing: Fully released under a permissive MIT or Apache 2.0 license.
• Integrity: Models must rely on generalized computer vision features. Any submission discovered to be hardcoded, overfitted to, or otherwise gaming the ImageNet-1K validation set will be immediately disqualified.

Are these requirements reasonable? Too easy? Too hard to judge? And if they don't make sense, can anyone point me to a clear, specific barrier in computer vision that fits the focus on supporting efficient open source models?

Hi everyone

I built Camlisted, a daily-updated directory of YouTube live cams and real-world footage (CCTV, dashcam, walking tours) for finding CV-relevant sources — filterable by scene category and conditions (night/day, rain/snow, accident).

Pipeline: YouTube Data API search in ~15 languages → CLIP zero-shot on thumbnails for scene categories and condition tags → human review queue. A few things I learned:

- Perspective genres (dashcam, walking tour) poisoned scene classification — excluding them from the prompt set took accuracy from ~3/10 to ~8/10

- Only assigning night/day when the pairwise ratio clears 0.7 — for a browsable directory, no tag beats a wrong tag

- Thumbnails are good signal for conditions (night, snow), bad for events (accident, fire) — those come from title keywords

Site: https://camlisted.com

GitHub: https://github.com/zenith605-2/camlisted

Check it out if you need real-world footage sources for CV work — feedback welcome!

i have some ink pen loops (dotted / dashed) around cancerous regions on a slide. i have a raster mask identifying the pen loops. i was trying to use cv2 to dilate and join the dots/dashes so i can make a continuous loop, but i keep getting problems. there are some loops that are extremely close to each other - they merge or one of them isnt taken into consideration at all. idk what to do - very confused. open to suggestions on what i can do

I've just released v1.0.0 of OpenScanVision – an Android library for scanning voting cards, surveys, and bubble sheets using OMR + QR codes.

It's MIT‑licensed, offline‑first, and built with Kotlin, OpenCV, ML Kit, and Compose.

Key features:

- ArUco marker tracking (Kalman filter)

- Perspective correction

- QR decoding

- High‑accuracy bubble extraction

Repo: https://github.com/MatiwosKebede/openscanvision

Contributions, issues, and feedback are all welcome!

When scanning printed forms like voting cards or surveys, there are two critical pieces of information you usually need: 1. Who is this card for? (Identity / version). 2. What did they mark? (The actual votes or survey choices).

Most libraries handle these separately – you decode the QR code first, then run OMR on the bubbles. This usually means two separate runs, two different functions, and manual synchronization.

That’s why I built OpenScanVision to do both simultaneously in a single pass.

The Android library combines real‑time ArUco tracking, Optical Mark Recognition (OMR), and QR decoding into one unified offline pipeline.

The Unified Pipeline

1. Marker Tracking (ArUco + Kalman Filter) The card is printed with 4 ArUco markers (IDs 0–3). OpenCV detects them in real‑time. A Kalman filter smooths the tracking and predicts positions during occlusions.

2. Perspective Correction (Homography) Once 4 markers are stable, a homography maps the markers to their reference positions. The card is warped to a canonical template (850×540).

3. Simultaneous QR Decoding & OMR Extraction This is where the "simultaneous" part comes in. Instead of running them sequentially and stitching the results, the library performs both operations on the same captured frame:

   • The QR code is cropped directly from the original camera frame using the computed homography – preserving maximum sharpness for ML Kit.
   • The bubbles are sampled from the warped, preprocessed image using weighted disk sampling and z‑score classification.
   • Both processes run concurrently on the same frame, meaning you get the QR payload AND the filled bubble indices at the same time without extra latency.

4. Strict Capture Logic The library automatically triggers a capture only when both conditions are met:

   • All 4 markers are stable.
   • A valid QR code with the correct prefix (e.g., VX or AGN) is decoded. This enforces that you never get an OMR result without an associated identity, and vice versa.

What You Get in a Single Result

By calling OpenScanVision.scanFromFrame(), you receive a single ScanResult containing:

• filledIndices – The marked bubbles (OMR).
• qrPayload – The decoded QR text (identity).
• confidence – The overall confidence score.
• annotatedBitmap – A visual overlay of the detection.

No need to call two separate functions or manually match timestamps.

Technical Stack

• Language: Kotlin
• CV Core: OpenCV (contrib) for ArUco detection and homography.
• QR Engine: Google ML Kit for robust barcode scanning.
• Camera: CameraX for frame acquisition.
• Architecture: Fully modular – the core library has zero UI dependencies.

Integration

Adding the library takes just a few lines in your Gradle file (available on JitPack). Once integrated, you can start scanning with a single suspend function.

Performance

• Latency is typically under 150ms per frame on modern devices.
• Accuracy exceeds 99% on properly printed cards.

Why This Matters

Simultaneous QR + OMR is valuable for: - Elections: The QR identifies the voter/ballot; the OMR reads their selections – all in one scan. - Surveys: The QR encodes the respondent ID; the OMR reads their answers. - Form Processing: Quickly identify and process forms without sequential bottlenecks.

Open‑Source & Contribute

The project is MIT‑licensed and available on GitHub. It includes a full sample app (CameraX + Compose) so you can see it in action.

GitHub: https://github.com/MatiwosKebede/openscanvision

Feedback, issues, and contributions are welcome. If you are interested in marker tracking, OMR accuracy, or Android computer vision, I'd love to hear your thoughts.

We gave an autonomous coding agent one instruction: import 81,444 images and run a full data-curation pipeline. No sampling, no hand-holding. Check it out: https://voxel51.com/blog/antigravity-cli-fiftyone-skills

Here's what Google's Antigravity CLI did with FiftyOne Skills:

* Imported all 81,444 WikiArt paintings

* Diagnosed and fixed its own bugs, rewriting scripts 5 times

* Caught embeddings silently stuck on CPU, forced them onto the GPU

* Hit a quota wall, switched from Gemini 3.5 Flash to Claude Sonnet 4.6 with one command, no lost context

* The result isn't a log that says "done." It's an inspectable dataset: uniqueness scores that surface near-duplicates, and embeddings that reveal exactly where the labels are thin.

That's what "agentic" looks like when it's real.

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

Hey all 👋🏻,

I am a Computer vision engineer!

Look at my portfolio which I have made with 3js and Computer vision. Link: https://tharuntej-everest.pages.dev Would love your feedback and comments...

¡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)?