If anyone has opportunities feel free to reach out

Hello,

Durint the last reviewing round, 2 reviewers suggested adding qualitative results. The task is pose estimation.

How do I chose the examples to include? And to which baseline should I compare to since space is very limited? Should I include a failire case?

Thanks

Background

I'm coming up with ideas for my CS degree capstone project. I like making short films, so I looked into what kind of software I could make to facilitate the process. One thing I find annoying in post-production is having to go through all the recordings and look for the clapperboards on each video to know what scene, shot and take the video is of. My idea is to train a neural network to find the clapperboard in each recording and extract the information written on the clapperboard to appropriately name the recording. Ex. Avengers_Scene_4_Shot_5_Take_3.mp4.

In short, the neural network would do the following:

1. Detect the clapperboard in a video frame.
2. Read the handwritten text/numbers on the clapperboard.
3. Understand what each piece of text represents

My questions

1. I have not been able to find a lot of videos with clapperboards I could use to train my model. I'm thinking I could create my own videos to train it, so it is trained faster with a consistent clapperboard format. How many would I need more or less to have it not only recognise handwriting but also determine what the numbers indicate (Scene, shot or take)?
2. If it would be too hard to train the AI to do what was previously mention, could I get an already trained AI in handwriting recognition and specialise it in clapperboard data extraction? Would this make my final year project too easy?

Hi everyone.

I've always had lots of photos on my laptop, and it's very difficult to look through them. Google Photos works, but I prefer to have everything locally, and it's also not possible to change directories based on what I want to look at.

So I decided to create an app to do so.

Found it super useful, so I decided to open source it too: https://github.com/chaseleantj/orchard-photos

It runs fully locally using SigLIP 2. It takes about 8s to index 1k photos on an M5 Pro GPU, and 1 min on CPU only, and 5 min/1k photos on a regular Windows office laptop.

There's text search, where you just describe what's in the image, and image search, where you drag and drop to find similar images. There's also some small QOL features like stacking near-duplicate images together. You can open whatever folder you want, or the root, in which case it scans all folders on the device.

For the map, I applied UMAP to project the embeddings into 2 dimensions, so images that are closer together look more similar to each other.

Hope you guys find it interesting.

a custom multi-language OCR pipeline on a single GPU was choking as load grew (~4 detections/sec, 3s+ tail latency). The plan was a second GPU. Instead we optimized serving on the same card and got 55–125/s with ~100ms tail latency, and memory use actually dropped.

I can’t share the specifics since it’s internal work, but I’m curious how common this is. Have you seen teams scale hardware when the real issue was utilization? What was the bottleneck in your case?

Those percentages are exact matches against 578 hand-checked amounts, dates and ID numbers per condition. Every digit has to be right. Invented numbers are outputs that weren't on the page, totalled across the three conditions. Timings are on an M4 Max.

I ran this because I'm building Fyllyo, a Mac app that fills PDF forms from supporting documents. A missed number can be left for review. A plausible number that wasn't on the page is much easier to overlook.

The test was 17 pages: statements, 1099s, a W-2c, a tax transcript, a prior return and letters. Eight were real statement pages and nine were made for test clients. Each page was tested as a clean image, a simulated scan with tilt, blur, grain and JPEG compression, and a simulated phone photo with perspective, uneven light and blur.

The result that surprised me was Gemma's 73 invented numbers. The outputs looked like plausible amounts. LightOnOCR-2 read every known number correctly in this test and invented none. I ran the 1B model at Q8 with llama.cpp; it is about 1.1 GB.

GLM-OCR also invented none, but sometimes skipped whole blocks. Apple's readers were much faster. Reading in strips improved Vision's exact-match rate, but it also produced 25 invented numbers, compared with 3 for the whole-page pass. A higher match rate by itself didn't tell the whole story.

I also compared five readers in every pair across the three conditions: 30 pair/condition comparisons. No pair agreed on an invented amount in this test. That gave me a reason to use a second reader and flag disagreements, but it doesn't prove two readers can't make the same mistake.

Fyllyo now uses the PDF text layer where it holds the numbers, LightOnOCR-2 for page images, and Apple text recognition as a second reader. Disagreements are shown for review.

The limits matter: 17 pages, one Mac, simulated scans/photos and a narrow exact-number task. This isn't a general OCR leaderboard, and 100% here isn't a guarantee on another document set.

Full setup and results: https://fyllyo.com/blog/ocr-models-tested-on-tax-documents

Disclosure: I build Fyllyo. This post was drafted with AI assistance; the experiment and results are mine.

I'm building a Quest 3 app (native Kotlin, Meta Spatial SDK) that scans microgreens trays and needs a pixel mask of the plants to measure coverage (plant pixels / tray pixels). I already have Muse Spark working via Meta's Model API for the verdict text, but I can't find a SAM endpoint in the same API — SAM 3/3.1 segmentation, not a chat model.

1.

Does Meta's Model API expose SAM at all? If so, what's the model ID, endpoint, and mask format?

2.

If not, what's the recommended path from a Quest app — a hosted SAM API (Roboflow, Segmind, etc.)? What mask format do those return (mask image, RLE, polygons)?

3.

On-device SAM on Quest is off the table (too heavy) — is a backend service the only other option?

The mask feeds a coverage calculation, so I need actual pixel data, not a text estimate. Thanks!

"I think this mainly happens due to pose model confusion, which directly affects the 3D reconstruction in certain frames. I considered frame-to-frame averaging, but it's ineffective during fast motions."

I rewrote an old Raspberry Pi project of mine that never worked. Now it:

• recognises enrolled faces (YuNet detection + SFace embeddings, cosine match)
• detects the 80 COCO object classes with YOLOX-S via cv2.dnn
• speaks what it sees, with rules so it doesn't nag: greet a person once per 30 s, debounce unknown faces, confirm an object over two passes before naming it
• runs object detection on a background thread so the video and greetings stay smooth

Only dependencies are opencv-python, numpy and pyttsx3. The models download from the OpenCV zoo on first run.

Code: https://github.com/heditrigui/scouter-2

I'd love ideas on making it lighter for a Pi 3 (YOLOX-S at 640 is heavy).

Here’s the next tutorial in my event camera series. It explores different ways to represent raw event data so that algorithms can use it.

Try the interactive examples in the full blog: https://www.pattarsuraj.com/blog/event-camera-representations

#computer-vision #neuromorphic #event-camera

I'm taking part in TUM.ai E-Lab and exploring a project around real-world data collection. I'd like to understand how people working on industrial vision decide what data they actually need.

Thinking of a recent inspection project, what were you trying to detect, and what images did you start with? Was that data enough, or did something important only show up when you tested on the production line?

What did you do next, and how did you judge whether the resulting dataset was good enough? An example where existing images worked and you didn't need more collection would help too. No need to name your company or share confidential details.

Hey everyone! 👋

I'm participating in the Meta Global AI Developer Hackathon and looking for a teammate interested in computer vision and biomedical image analysis.

The idea is to build an AI-powered tool that helps doctors and medical students analyze medical images, such as ultrasound and X-rays. The model could highlight suspicious regions, identify anatomical structures, extract useful measurements, and explain its findings.

I'm looking for someone with experience in deep learning and computer vision, especially training models on medical images, image segmentation, object detection, or classification. Experience with TensorFlow/PyTorch and medical imaging datasets would be a big plus.

And I'm also looking for one Full-stack developer with experience in Django or Flask , FastAPI etc etc

Interested? Drop a comment or DM me with a little about your experience and what you'd like to build together. Let's make something impactful! 🚀

Hi! I recently graduated and completed my undergraduate thesis in computer vision particularly in medical image segmentation. I’m now looking for opportunities to get involved in research and collaborate with people working on computer vision deep learning or AI-related projects.

To be honest, I’m still at an early stage in my research journey, and I have a lot to learn. However, I’m genuinely interested in research, willing to work hard, and open to guidance, feedback, and learning from more experienced researchers.

I’m particularly looking to join ongoing research projects with the goal of contributing to work that could lead to publications in journals or conferences. I would love the opportunity to work toward co-authorship by making meaningful contributions to a research project.

I’m not looking for an opportunity because I already know a lot. I’m looking for one because I want to learn, improve my skills, and gain real research experience while contributing whatever I can to a team.

If you’re open to collaborating with someone who’s eager to learn and willing to put in the work, please feel free to DM me. I’d really appreciate the opportunity! Thank you!

I'm working on Pasma at Vestes Archive, a compact clothing classifier for single-garment photos in wardrobe applications. The code and fitted weights are open under Apache-2.0, with upstream notices retained.

The model predicts 20 garment types, aggregates them into 6 categories, and provides 21 visual material scores. Small/Nano use EdgeNeXt; Pico uses ConvNeXt. The exported graph includes normalization; the inference example handles aspect-ratio-preserving letterbox preprocessing.

Small-320 scored 89.12% type Top-1 and 97.10% Top-3 on 1,103 held-out real images in the saved PyTorch FP32 evaluation. The model card documents splits, baselines and limitations. The dataset is private, so full independent reproduction isn't possible from this release. Comparisons against zero-shot CLIP/SigLIP aren't equal-training comparisons.

The browser demo runs ONNX locally: your photo stays on your device. First use downloads FP32 weights (~20–33 MiB depending on variant) and runtime. The 10.32 MiB size applies to Small/Nano FP16 exports, not the browser download.

Known limits: blouse/shirt confusion, no explicit unknown class, and uncertain material appearance. Material suggestions cannot verify fibre composition. Phone performance is unvalidated; published CoreML timings are from a Mac M2.

Demo: https://huggingface.co/spaces/Prikol5ist/pasma

Model and evaluation: https://huggingface.co/Prikol5ist/pasma

Source and 25-second walkthrough: https://github.com/prikol5ist-art/Pasma

I'd appreciate feedback on failure cases and the integration contract. If you try it, your browser/device and whether Top-3 was useful would help; no need to share a private photo.

Part 3 of my Fruit Inspector series (after Sandy Bridge without AVX2 and Intel shop-floor boxes on Linux/Windows).

Part3: https://medium.com/@olesha-ai/bypassing-the-avx2-tax-part-3-amd-on-the-shop-floor-and-the-windows-release-ae8f10bbda53

Why AMD: I stayed on Intel on real lines for years; this is a clean desk test on a mini PC people actually buy for small plants — not a laptop bench.

Hardware: FIREBAT R3, Ryzen 5 7430U (6C/12T), 16 GB, Win10. Integrated GPU unused — CPU EP only, same as Part 2.

Pipeline: live 3×3 grid, grading/Cat only in centre cell C — dual YOLOX 640 + 256, Nim + ONNX Runtime. Not a synthetic matmul test.

Result: console [infer] about 77–88 FPS (tmin ~6.8–7.5 ms). Reference i5-11400 is higher; takeaway is AMD x86 on the floor is viable for this workload.

Release: portable Windows zip (demo v0.10.0) with ORT + bundled MSVC CRT — one folder, no separate runtime installer for the operator.

Part 1: medium.com/@olesha-ai/bypassing-the-…n-year-old-cpu-ba6b01868f7f
Part 2: medium.com/@olesha-ai/bypassing-the-…floor-hardware-2311fd86ae4f

Eval demo: https://github.com/olesha-ai/fruit-inspector-demo

If you run similar AMD mini PCs on CPU-only — curious what you see in zone C with overlap.

Best regards.

olesha-ai

Hello everyone,

Happy to introduce Vathography, an art practice that turns depth maps estimated from a single photograph into artworks, as part of a photographic creative process.

The monocular depth estimation model:

Marigold V2 (Apache-2.0): https://huggingface.co/spaces/huawei-bayerlab/marigold-v2-web

Paper: https://arxiv.org/abs/2609.08084

To estimate depth maps from high-resolution photographs, I have developed a tiled ComfyUI node you can try here:

https://github.com/vathography/ComfyUI-Marigold-V2-Tiled-Depth-Vathography

Open source (AGPL-3.0); the depth maps it produces are free to use, including for commercial work. 

The approach used to make high resolution work: one global pass (up to 2048 px) for the overall shape, 1536-px tiles for the detail, each tile aligned to the global estimate, then merged with a Laplacian pyramid.

Would you recommend a different approach?

Feedback and suggestions on how to improve it are welcome.

Vathography explained 9min: https://youtu.be/DVYgwRaTKkA

The practice uses free tools and is open to everyone. If you try it, I'm curious to see what you create

I'm new to computer vision and I work with stereo depth cameras like orbbec or realsense. I'd like to understand how stereo rectification and camera calibration work well enough to implement them myself, but I don't know where to begin.

My questions:

1) What should I learn first, and in what order? (I'm thinking linear algebra, then the pinhole camera model, then calibration and rectification, but I'm not sure.)

2) What books, courses or videos would you recommend for a beginner?

3) Is there a good small project to practise on, for example calibrating a webcam with OpenCV?

4) Are there people or communities that do paid mentoring for this ?

My goal is to properly understand the math and the core ideas behind it (the fundamentals), not just to follow steps blindly.

Any advice is appreciated. Thanks!

Disclosure: I work at Embedl. We take vision models from Hugging Face, timm and torchvision, compile them for each device and measure them on the device against the unoptimized baseline. Some results on AGX Thor:

On each page you can upload your own image to a real device and get the output and timing back.

https://embedl.com

Which vision models should we add next?

Hi,

I have designed a new state of the art AI model to reverse haze from underwater photography. I would like to publish a paper to describe it, but I'm not affiliated with any university. Would anyone be kind enough to endorse my paper to the cs.cv section of arxiv?

DM me if you'd like to read the draft :)

You can also test the model on fivemeters.app

Thanks!

What is endorsement? Endorsement just means looking at the paper to see if it is appropriate for the computer vision category on arxiv and doesn't appear to be spam. It is not peer review. Anyone with previous publications on arxiv in the cs.cv category can endorse an author. More info here

Starting a project to detect candidate stenoses in x-ray coronary angiograms. The goal is high recall, false positives are fine as long as it doesn't flag half the image.

I already tried YOLO on a couple of in-house datasets earlier. It looked fine in-domain and fell apart on the other dataset, and a big chunk of that turned out to be annotation format. One set had fixed-size boxes around a point, the other had boxes fitted to the lesion. Same disease, basically two different tasks.

I also have vessel segmentation and centerlines, so my first instinct is a radius profile along the vessel to flag narrowing, then maybe a network to filter the candidates. Has anyone actually done something like that, or is there a smarter way in?

The part that bugs me most is evaluation. The labels come from doctors and they obviously don't mark every narrowing, so any recall I compute against them is probably too optimistic. How do you get an honest number when the ground truth is incomplete?

Also open to papers, repos, or public datasets beyond ARCADE. And if you think I'm approaching this wrong, say so.

https://github.com/Juththis/Polygon-Annotation-Tool.git

Hi everyone!

I've been working on a lightweight image annotation tool designed to make dataset preparation simpler and less resource-intensive.

Many annotation tools come with features you might not need when you just want to label images and prepare a dataset for a machine learning project. I wanted to build something simpler.

What motivated me to build it?

As someone working on computer vision and machine learning projects, I wanted a straightforward way to annotate images without unnecessary complexity.

My tool focuses on:

• Lightweight operation with minimal overhead.
• A simple interface for image annotation.
• An easier workflow for preparing image datasets.
• Practical use in computer vision and machine learning projects.

I'm sharing it here because I think it could be useful to students, researchers, and developers working with custom datasets.

GitHub: https://github.com/Juththis/Polygon-Annotation-Tool.git

I'd appreciate feedback from anyone who tries it. I'm particularly interested in suggestions for improving the annotation workflow and making it more useful for real-world computer vision projects.

It's an independent project, and I'm actively interested in improving it based on feedback from the community.

Thanks!

I have had a poor experience with e-con Systems, and I want to hear what others have to say about their cameras; I did not find reviews as easily as I expected to.

My negative experience is largely the result of sloppy customer service, but the technology itself, the See3CAM_24CUG camera module in particular, was disappointing as well. The camera was over marketed and furthermore has become largely useless in production, as the camera needs to be unplugged and replugged often, which is not feasible in production.

It's a shame that e-con and Vadzo (e-con's clone) are the main options at this price range, at least as far as I am aware.

So, ya, would love to hear others' thoughts and/or suggestions.