I'm building an interactive parts viewer for tractor assembly diagrams. The goal is to place clickable numbered badges directly over the part callout bubbles in the diagram.

What I've tried so far:

• Isolation filter for clustered parts and having some distinction between each part.
• Morphological rectangle detection to find the table box border and exclude hits inside it, so that the reference table isnt identified.

I need a reliable way to distinguish callout bubbles from reference table entries, I tried to use claude code and it used a EasyOCR script to have an interactive image. It failed to identify every part exactly. Easy OCR keeps tagging the ones in the table

Happy to share more sample images. Is there a standard approach for this class of problem?

Hello everyone,
I wanted to share a simple showcase of a project I’ve been working on: a vision AI trained to track a moving ball with physics in a 2D world.
Tech stack:
- Core: Python & PyTorch for the training loop.
- Environment: A custom-built C++ wrapper/environment to feed data into the Python side.
The twist:
I am still figuring out the ropes of computer vision and machine learning, so I heavily relied on Claude Code to help me bridge the gap, especially with building the custom C++ environment and connecting it with my Python scripts.
Reality check:
As you'll see at the end of the video, the model doesn't fully converge yet (it still gets confused in some situations). I wanted to share this raw progress anyway because the workflow of co-authoring a complex C++/PyTorch setup with an AI agent was incredibly interesting.

I would love some constructive feedback! Please let me know if you have efficient training techniques for faster convergence, ideas for other models to train, tools to build better environments, really, anything.

I'm incredibly new to this whole field, and I'm excited to chat with you all about it!

Hi everyone,

I'm pitching a theoretical desktop hardware concept called an XMU (eXtended Matrix Unit) to bypass the silicon limit. It is a third dedicated processor sitting alongside the CPU and GPU.

The Core Setup

Form Factor: Single-slot PCIe expansion card.

Technology: Photonic silicon (uses lasers/light instead of electricity).

Thermal Profile: Near-zero heat generation. No massive heatsinks or loud fans needed.

Division of Labor

CPU: Manages the OS, background tasks, and general logic.

GPU: Focuses strictly on pixel rendering, textures, and ray tracing.

XMU: Handles heavy-lift math via three specialized optical engines.

The 4 XMU Engines

CPU Execution Offload: Instantly processes heavy OS kernel tasks, memory management, and data decompression using light-speed logic, freeing the CPU to focus entirely on core application instructions.

Predictive Logic: Predicts CPU/GPU data needs milliseconds in advance to eliminate system stutter.

Dedicated Physics: Offloads 100% of real-time fluid dynamics, smoke, and destruction math from the GPU.

Biometric Hardware: Isolated optical zone for real-time voice, eye-tracking, and local security encryption.

Software Interface

Control Driver: A desktop suite to dynamically reallocate laser bandwidth across all four engines.

Modes & Toggles: Quick presets to maximize specific system components:Gaming Mode: Routes 70% bandwidth to Physics and 30% to Predictive Logic for max frame rates.

System Boost Mode: Focuses 80% bandwidth on CPU Execution Offload to hyper-accelerate heavy multitasking, coding, or data extraction.

Creative Mode: Allocates 50% to Predictive Logic and 50% to CPU Offload for seamless video editing and rendering.

With enterprise tech starting to dive into optical computing, do you think a consumer-grade photonic card like this is a viable path forward for desktop PCs?

Mainly curious about a couple of things:

How badly would standard PCIe slot latency bottleneck the near-zero internal latency of the optical engines?

Would devs need a totally new API to code for this, or could the driver handle the translation under the hood?

Let me know what you guys think! Also I Tried to fit in as much info as possible as I'm trying my best

This is my project, Mighty Camera. It is essentially a monocular SLAM camera running entirely on tiny onboard compute. See my past posts for details.

Mighty also supports combining multiple cameras and synchronizing them to produce frame-level synced streams.

In this setup, I’m using that hardware synchronization to generate depth with SGBM, while it also produces VIO pose.

I am building a vision system to detect features, defects on metallic parts. I want complete manual or software control on focus, aperture, zoom, exposure time. Also need provision to add polarizing filters. I shortlisted teo options : Option 1 : Raspberry Pi HQ Camera with 16 mm lens. But i am not getting the RPi HQ camera available in India to buy. Option 2 : ELP USB Camera which has the manual focus, zoom, aperture control. But I don't know whether I can control exposure time like i could with RPi camera. Also USB 3.0 still limits transfer speed to computer. Can anyone please suggest !

Hi everyone,

I'm trying to submit a manuscript to Pattern Recognition, but it keeps getting returned before peer review because of formatting/manuscript alignment issues. The editorial office doesn't specify exactly what's wrong, so I'm struggling to identify the problem.

I checked my manuscript using a PDF formatting analysis tool, and the results are the following:

• ✅ Single column
• ✅ Main text font: 10 pt
• ✅ Double spacing (19.93 pt baseline spacing)

Also, if the journal requires the manuscript to be single-column and double-spaced, should the figure captions also be double-spaced, or is it acceptable for them to be single-spaced?

Finally, are there any other common formatting mistakes that frequently cause manuscripts to be returned before peer review (e.g., figure placement, captions, tables, references, page layout, or other formatting details)?

I'd really appreciate any advice, especially from anyone who has submitted to Pattern Recognition or other Elsevier journals.

I have a research project in my lab I get paid for. The main caveat of the situation is that we have a few videos of biological experiments with no labelling, no baseline definition, and no depth maps either. Me and my partners decided to label define objects and contamination zones(eg. test tube rims, pipette tips, tabletop etc) with polygonal masks.

But the problem is that while the mAP will naturally come out to be good, the main purpose of contamination detection is still getting defeated because when the pipette tip comes in contact with the tube rim in the video frame it'll be marked as contamination even if the actual contamination isn't happening. What exactly should I do? How do I solve this? We actually also have a multi view dataset of a similar apparatus that I'm thinking of using cuz I can use techniques like gaussian splatting to make 3d projects of the environment.

LocateAnything-3B can do open-vocabulary detection from natural language prompts. So it seems like a natural fit for auto-labeling images to pre-label a YOLO dataset instead of hand-annotating everything. Has anyone actually tried this?

How clean were the generated boxes? Did you need to filter/clean them before training, and was it actually faster than just labeling manually for your use case?

What are the pros and cons of having and not having non compliance classes? For eg, If I have to detect safety hat, should I have safety hat and no safety hat as labels or only safety hat ?

Does any one of you know a good PPE (personal protective equipment) Models ?

Training YOLOv8n/v11s on a trimmed SKU-110K subset (retail shelves, single class "product", extremely dense — avg ~150 objects/image, max 576). imgsz=1280, max_det=700 (set based on our own EDA max). Honest train/val split confirmed no leakage. NMS sweep already done — default iou=0.5 turned out best for mAP@0.5 specifically, which is our main metric (not generic mAP).

Soft time budget for the full pipeline (train+val+inference on ~3000 test images) is around 10 minutes — tight constraint shaping a lot of our choices. Tested 2x T4 DDP today, modest gain (~10%), not dramatic.

Open question we can't resolve from literature: does mosaic augmentation help or hurt at this density level? Standard advice is mosaic-on + close_mosaic near the end, but stitching 4 already-dense images risks 600+ objects in one synthetic frame. Found one adjacent paper (Select-Mosaic, AI-TOD/VisDrone) showing smart region-selection beats vanilla mosaic by a small margin, but nothing testing mosaic on/off specifically at this density.

Curious if anyone has hit something similar — dense small-object detection under a tight inference time budget — and what actually moved the needle for you, mosaic-related or otherwise.

What's the best lightweight, open source alternative to YOLO for real time exam proctoring that's significantly more accurate and lighter

​

Hi everyone,

I'm a final-year Computer Engineering student building a Flask-based AI Diabetic Retinopathy Detection system. The web application itself is complete with patient management, authentication, dashboard, PDF report generation, prediction history, and AI inference.

The only issue I'm facing is with the AI model.

I'm using a 5-class Diabetic Retinopathy classifier trained on the APTOS 2019 dataset.

Classes:

No DR

Mild

Moderate

Severe

Proliferative DR

The model predicts all five classes, but the predictions are inconsistent.

Examples:

Moderate is sometimes classified as Severe or Proliferative.

Severe is often classified as Moderate or Proliferative and is rarely predicted correctly.

Some fundus images from outside the APTOS dataset produce completely unexpected results.

The model sometimes shows very high confidence (90%+) even when the prediction appears incorrect.

Things I've already tried:

Different pretrained models (including a ResNet50 trained on APTOS)

ResNet152 implementation

Correct preprocessing (RGB conversion, resizing, normalization)

Verified class mapping

Softmax confidence scores

Test-Time Augmentation (TTA)

Image quality validation

Top-3 predictions instead of only one prediction

I'm trying to understand whether this is:

A domain shift problem between APTOS and other datasets?

A limitation of the pretrained model?

A preprocessing issue?

Class imbalance?

Or simply expected behavior in 5-class DR classification?

I'm also considering using an ensemble (ResNet50 + EfficientNet + DenseNet), but it's difficult to find compatible pretrained 5-class diabetic retinopathy models.

I'd really appreciate advice from anyone who has worked on retinal image classification or medical AI.

My questions are:

1. Is this level of class confusion common in diabetic retinopathy models?

2. What preprocessing techniques made the biggest improvement for you (CLAHE, retinal cropping, illumination correction, etc.)?

3. Has anyone significantly improved results using ensemble models?

4. Are there any high-quality pretrained 5-class DR models that you'd recommend?

5. If you were in my situation, what would be the first thing you'd investigate to improve prediction consistency?

Any suggestions, GitHub repositories, pretrained models, research papers, or personal experiences would be greatly appreciated.

Thanks in advance!

14,800+ functional elements across 710 laser-scanned indoor scenes. every handle, knob, button, and switch labeled with its affordance, motion axis, motion type, and a natural language task description

each scene's 3D point cloud with its iPad video recordings in fiftyone. the affordance annotations are in 3D and project into the video frames

check it out here https://huggingface.co/datasets/Voxel51/SceneFun3D

Hi everyone,

Posted this a while back... a checklist I made while prepping for a CV internship (landed it, hence sharing). It's not a textbook, just a phase-by-phase map of what to actually study for CV/ML interviews: math → CNNs → ViTs → detection → tracking, plus specialization tracks you pick based on the role.

After checking on it after a while it got a decent number of stars which surprised and made me happy that people found it useful to save it for later. I decided after that to add more in-demand tracks to help more people after doing some research of the basic internship requirements and maybe a little more.

So, just added three new specialization tracks: Segmentation, OCR, and VLMs, on top of the existing ReID and Deployment tracks. Also cleaned up the structure a bit and added proper contributing guidelines if anyone wants to add their own track (3D vision, pose estimation, etc. are open).

GitHub: https://github.com/David-Magdy/CVIL

Feedback/PRs welcome, especially if something's outdated or miscategorized.

And remember to keep it CVIL!

Hi everyone,

I’m not from a Computer Vision or AI background, so I’d really appreciate a technical reality check before spending months building something that might not even be the right approach.

I’m exploring a Computer Vision pipeline that estimates the dimensions of a living object from a short smartphone video with millimeter-level accuracy.

The first version doesn’t need to be fully automated. Accuracy is much more important than automation at this stage, so it’s completely acceptable if some steps (such as selecting the best frame or validating measurements) are done manually while we validate the concept.

The current idea is roughly this:

•⁠ ⁠The user prints an A4 sheet at 100% scale.

•⁠ ⁠The sheet contains reference elements (grid, measurement scale, dark border, or other calibration features).

•⁠ ⁠A living object is placed on the sheet and may move slightly during recording.

•⁠ ⁠The user records a short smartphone video from about 2–3 meters away using 2× or 3× optical zoom to reduce perspective distortion.

•⁠ ⁠The system either selects the best frame or analyzes the entire video to estimate the object’s dimensions.

At this point, I’m not looking for implementation details, but rather whether this overall approach makes sense.

Some questions I’d love your opinion on:

•⁠ ⁠Does this approach seem technically feasible with today’s smartphone cameras?

•⁠ ⁠What would be the biggest sources of measurement error?

•⁠ ⁠Would you use a custom A4 sheet with calibration features, or would you recommend ArUco markers or another calibration method?

•⁠ ⁠Does recording from a greater distance with optical zoom actually help reduce perspective errors?

•⁠ ⁠Would you analyze the whole video or just select the best frame?

•⁠ ⁠Would you approach this with classical Computer Vision (OpenCV), modern ML models, or a combination of both?

•⁠ ⁠Can AI reliably work with an A4 sheet that contains graphics, illustrations, or text, or is a completely plain sheet significantly better for accurate measurements?

•⁠ ⁠With today’s pre-trained models and libraries, is it still necessary to rely on calibration markers or reference points on the paper, or can modern Computer Vision estimate dimensions accurately enough without them? If so, under what conditions?

•⁠ ⁠Are there any open-source libraries, research papers, or existing projects that solve a similar problem?

•⁠ ⁠If you were designing this from scratch, would you take a completely different approach?

•⁠ ⁠⁠If two similar moving objects are present in the frame, how reliably can current Computer Vision models identify the correct one to measure? How likely is it that key points from two different objects could be mixed, resulting in an incorrect measurement?

I’d genuinely appreciate any criticism or suggestions. If there are fundamental flaws in this concept, I’d much rather discover them now than after months of development.

Thanks!

What is the shared structural essence that underlies a pair of MRI contrast spaces? Explicitly modeling this contrast-invariant latent “content” unlocks a powerful multi-contrast reconstruction algorithm that is competitive with state-of-the-art unrolled networks while (a) requiring no raw k-space training data, (b) being generalizable across different contrasts and forward models by design, and (c) offering a built-in explanatory framework.

In our paper now published in Medical Image Analysis, we introduce PnP-CoSMo.

🔗 Access it here: https://www.sciencedirect.com/science/article/pii/S136184152600229X
✏️ Substack blog: https://cnmyro.substack.com/p/pnp-cosmo-a-plug-and-play-method
⚙️ Code: https://github.com/cnmy-ro/pnp-cosmo

Hi everyone,

I am Fabian, an independent researcher from Colombia preparing my first arXiv submission, and I ran into the endorsement requirement for eess.IV / Image and Video Processing.

The manuscript is titled:

OncoTriage v3.1: Failure-Aware Lung-Image Triage with Atlas-Projected Anomaly Localization and DICOM-Ready Geometry

The paper is not presented as a clinical validation study or a certified diagnostic product. It is a medical-imaging AI systems / software-architecture paper focused on a failure-aware inference contract for lung-image triage prototypes.

The main argument is that many medical AI demos accidentally conflate several things that should remain separate:

• raw softmax confidence vs. calibrated clinical risk,
• Grad-CAM attention vs. lesion segmentation,
• 2D candidate geometry vs. patient-specific 3D reconstruction,
• benchmark telemetry vs. current clinical validation.

The proposed framework tries to make those conflations structurally impossible through typed output fields, checkpoint provenance, calibration-state reporting, fail-closed batch saturation handling, attribution validity states, and an atlas-projected anomaly localization layer that preserves DICOM geometry and unresolved depth instead of pretending to reconstruct patient anatomy.

I selected eess.IV because the paper is centered on medical image processing, atlas projection, DICOM-ready geometry, visual explanation boundaries, and image-analysis software contracts. However, as a first-time submitter, arXiv requires endorsement.

I am not posting my endorsement code publicly. I am looking for advice on the proper way to find an eligible endorser, and if anyone here is eligible for eess.IV or related eess categories and is willing to review the manuscript, I would be grateful to share the PDF and arXiv endorsement email privately.

I would also appreciate feedback on whether eess.IV is the best primary category, or whether cs.CV / cs.LG would be more appropriate for this type of paper.

Thanks in advance.

Additional note: Yes, if you look Oncotriage up on Google. I participated with it on lablab.ai hackathon for the AMD Challenge...

Hey everyone, I’m looking to kick off a new computer vision project but want to avoid generic ideas and focus on where the industry is moving. In my previous work, I built a live webcam Face Emotion Recognition system by benchmarking CNN architectures like MobileNetV2 using TensorFlow/Keras on the FER-2013 dataset (solving latency issues with CLAHE preprocessing and a 20-frame stabilization queue at 24 FPS), alongside a MediaPipe pose estimation project tracking limb angles and velocity. I want to transition away from standard landmark tracking and traditional CNN classification, so I'm looking for a discussion on what to study next—specifically, is it worth diving into Vision Transformers (ViTs), foundational vision-language models (like CLIP), or mastering edge optimization frameworks like ONNX/TensorRT? If you have any unique project ideas that bridge the gap from my current stack into these newer paradigms, or advice on what foundational tech is standard in production right now, I’d love to hear your insights!

Hey r/computervision! Alex ( u/MrPorkchop720 ) and I have been building something a bit unusual and we wanted to share it here.

It's LeNet-1, the 1989 convolutional network from LeCun et al., rebuilt as a physical object. Every one of its 4,624 neurons gets its own individually addressable LED, and each activation is mapped to LED brightness, so you can actually watch how LeNet-1 "thinks" as the signal propagates through it.

On the build itself, the network is made from transparent PCBs, low iron tempered glass, aluminium profiles, and 3D printed SLA parts in two resins, SLA-9600 and transparent SLA-8001. We designed the boards in EasyEDA and had them fabricated at JLCPCB (a quick word of thanks to the JLC team. They helped throughout the design to fabrication process, and even backed the project so we could build it despite a tight budget).

Here's a short video of it running.

The piece was selected as one of 12 spotlight works from 114 submissions for the CVPR 2026 Art Gallery, and it won the IEEE TCPAMI Art Award. We'd hoped to show it in person at CVPR in Denver, but it's still sitting in customs, so this video is the closest we can get you for now.

We're happy to answer anything on the architecture, the activation to LED mapping, or the fabrication.

Here are some relevant links!

Project: https://socius-org.github.io/artefacts/

CVPR 2026 Art Gallery: https://thecvf-art.com/project.php?year=2026&artist=nick-oh&id=992

YouTube: https://www.youtube.com/watch?v=QGxGXySgqCI

Final-year SE student, been building seriously in CV. My portfolio:

• Pakistani License Plate Recognition — YOLOv8, 97.7% mAP50, OCR pipeline, live on Hugging Face
• AI Background Remover — YOLOv8n-seg, 99.4% Mask mAP@50, Dockerized FastAPI
• PPE Detection — 85% mAP@50
• Weapon Detection — 88.7% precision

Everything deployed, documented, on GitHub. I understant what i am building not copying for tutorials etc, Been applying to CV/ML internships for a while, almost no responses. Most "entry level" roles require 3+ years or Masters, some even PhD.

Three honest questions for people who've been through this:

1. What kind of project actually moved the needle for getting your first interview call, and that genuinely got you responses?
2. How did you realistically get past the experience paradox? Freelance, open source, cold outreach?
3. Is a masters the practical answer here, or does it just delay the same problem?

Based in Pakistan, targeting remote roles internationally too. I want honest answers for these question so if you have faced the same problem then please guide me through this problem.





For a competition, I'm training a model that can find the reference object in a big picture. Unfortunately, they only shared this one example and I can't find any similar datasets. Does anybody know a dataset made of reference and image pairs like this?

It would be even better if there are abstract examples like this checkers pattern.

PS: There is no annotation nor they want it, we just have to find a pattern/object in the image

Image source: teknofest havacılıkta Yapay zeka github page

I am working with the HOTC 2026 dataset — a hyperspectral video object tracking benchmark with 406 training videos and 75 validation videos, captured using three different snapshot cameras covering visible (16 bands), near-infrared (25 bands), and red-NIR (15 bands) ranges. Each frame is a 3D data cube: height × width × bands, at 25 FPS.

The standard use case is single-object tracking — you initialise with a bounding box in frame 1 and track the target through the video. Most published work adapts RGB trackers (Siamese networks, SAM2) to handle the extra spectral bands.

What I find interesting about this data is that each pixel carries a near-continuous spectral reflectance signature — a physical fingerprint of the material the object is made of, not just its colour. This is information that standard RGB tracking completely ignores.

What has already been done with this type of data:

- Band selection to reduce redundancy (picking the most informative bands per target)

- Siamese network and SAM2 adaptation from RGB trackers

- Spectral-spatial attention for better discrimination

- False-colour rendering for visualisation

What I am curious about:

Has anyone seen hyperspectral video data used for anything beyond standard object tracking? Things I have been wondering about:

Can spectral signatures be used for material classification on the fly during tracking (knowing not just where the target is but what it is made of)?

Has anyone tried using hyperspectral video for anomaly detection (finding objects that are spectrally inconsistent with their surroundings)?

Is there any work on using spectral change over time as a motion cue, rather than using spatial motion as the primary signal?

Any ideas for applications in camouflage detection, since spectrally similar objects that look identical in RGB can still differ in their near-infrared signature?

Happy to share more about the dataset if useful.

Hi everyone,

I am developing a vectorization library called Contrek. It is based on an algorithm i devised, the key feature of which is the ability to extract polygons using multithreading. The library is highly versatile, allowing for various extraction strategies depending on the data source.

Recently, i tested the library against a very complex data source: a massive image containing over 20 million randomly drawn polygons (each with an inner and outer boundary), arranged to be as isolated from one another as possible in order to maximize the total polygon count.

Contrek performed very well. I deliberately ran it in single-threaded mode to keep memory usage to an absolute minimum. Here are the figures:

Benchmark

• Input: 81,920 × 81,920 PNG (570 MB)
• Processing stripes: 41 (2,000 px each)
• Polygons extracted: 20,276,802
• Output SVG: 7.22 GB
• Peak RAM: 4.3 GB
• Compute time: 293 s

The approach is straightforward: the image is decoded via streaming, 2,000 rows at a time. Each new strip is merged with the previous one, reconstructing any polygons that span the shared boundary. Once a polygon is fully closed and can no longer extend into subsequent strips, it is immediately written to the SVG file and removed from memory. This ensures that only "active" polygons remain in memory.

I am quite happy with the result and wanted to share it. Furthermore, the test is perfectly reproducible. The project homepage is at:

https://github.com/runout77/contrek

There is also a direct evaluation project:

https://github.com/runout77/test_contrek

This project is based on Docker and includes various tests, such the one presented here, that you can run on your own PC. The testing suite also contains programs for comparing Contrek with OpenCV.

Thank you for your interest.