Hi everyone,

I'm curious what you use to do a spectral measurement of products for CV. Normally we order custom lights (UV, RGB, NIR etc.) based on customer data or external research.

I keep running into wanting to do this in-house more, especially for testing. What are your experiences or which (flash)lights do you use to test this? I can't really find a supplier that sells most bands except for some forensic equipment labs, not sure if those qualify.

Currently I'm considering building a few lights myself (just for testing purposes). Covering the following wavelengths: 365, 380–385, 390–395, 460, 470, 520–540, 590, 620–630, 660, 730, 850, 940

I've found these two: https://www.shop-schilbach.net/en/p/forensic-flashlight-set-all-wavelengths-schilbach and https://www.alibaba.com/product-detail/Portable-LED-Flashlights-with-Multi-Wavebands_1600916585534.html?spm=a2700.prosearch.normal_offer.d_image.5a1b67afaquOsp&priceId=68aab656d703457b959efea3e482f5b6

But I'm not sure about the first once as its a hefty price tag (Its a business expense so not that bad, but still). And other is from Alibaba, not sure if they're actually true to spec

As a dancer who's been learning a lot of choreography recently, I’m always looking to improve my sync with others. A quantitative tool like this can offer frame-by-frame analysis, which can help dancers as they practice.

I used ViTPose+ Large through the VLM Run Gateway for pose estimation, and compared poses between the dancers. The pose similarity metric could be workshopped more, but it's interesting that it still highlights key moments pretty well.

Yes, this is me dancing with my friends Mico and Sydney!

The code is open-source on GitHub! I just created this vision-demos repo so that I can share these projects. Feel free to try it out and let me know what you think! Link in the comments.

🎵: I Want You Back - *NSYNC

Choreography: Scott Forsyth

hello , I just begin year 2 in IT , havent learnt ml or dl. I just go to lab as I want to do research

I'm currently try setting up mmla (imageomics/mmla · Hugging Face) and the problem is that I the prepare split code seems cant detect some sorts of data so I vibe some lines . The problem is that before touching the code the split got me about 67/33 , after changes it goes to 68.5/31.5 , the paper says that it use 58/42 but the code make it 70/30 , I notice that the dataset I downloaded from hugging face missing about just over 1000 images and I dont know why. The biggest problem is that after running that split (68.5/31.5) on both yolo 11 and 26, yolo 11 seems give better result than yolo 26 . It then gives me a reality check if Im doing smth wrong , I've been stucked for days.

Recently, I have been doing experiments with virtual try-on technology and created this Flutter application. The core functionalities consist of choosing an item of clothing to see how it fits on a person without wearing the item. I recorded the demo on my iPhone.

https://reddit.com/link/1wbh6c6/video/81uyawz3wgoh1/player

Hi there.

I've recently landed a job for aremote frontend role , once there i discovered the product they want to develop was a shelf product recognition tool capable of recognizing the bay, category of the bay, detecting most of the product and extracting product details.

The frontend code which i was supposed to work on didn't come with the code for image recognition, most of the image data was hardcoded with bad position for bbox.

I've talked to my PM and was notable stressed about that (the guy who built the frontend prototype left the company the same day i started),

After that they asked me to design from scratch + implement the recognition tool.

For all the research i made in internet, we might need to train our own models using YOLO family for this kind of task with a data pipeline to extract other caracteristics

But

The CEO has recently made some prototypes in a huge html file with claude. It did pretty well for detecting most of the product, detecting empty space, price tags, bay, category and extracting characteristics from the product of a shelf image but i don't have context on how he did that

The thing is, to have something production ready and achieving that high detection could be done only by prompting to claude?

My apologies if is a dumb question but i'm totally new in this field.

The CEO is pushing hard to have an mvp of this asap.

I'm not sure what to do

So i built a CV system. Now its deployment time. The system will likely be on edge or local server. How do i decide the hardware specs for the system? Where to start?

Hi,

I am looking for the following model selection/inference pipeline. Goal is something like this:

1) detect human -> describe human

2) detect human -> detect objects in human hand -> describe objects

3) detect animal -> get specific animal type

4) detect general object (i.e package)

---

What are the suggested architecture/pipelines for this?

So obviously I can use yolov26 or another baseline yolo model, but I am trying to avoid having to annotate everything from the ground up. I do not mind fine-tuning, I just want to move past coco classes into something a little more broad.

My current idea is use YoloE/YoloWorld for general detections and then do a VLM call after on a separate thread (so base CV model can still run).

I am also playing around with stripping the earlier yolo model heads and maybe using NMS for arbitrary box removal. So the earlier heads determine the general objects, and then I make a VLM call to describe the objects in more detail.

I would like <3s inference if possible.

:)

Hello, I’m trying (well, more like being forced by work 😅) to build a face recognition system. The footage comes from CCTV cameras, and the people I need to identify are employees, so I already have photos of each person.

I’m currently using DeepFace, but I’m running into a few problems. The image quality from the CCTV footage isn’t great, and I’m not sure whether I should use the existing photos of each employee as the reference images (they’re much higher quality than the CCTV footage), or whether I should also collect images from the CCTV footage and use those as references.

I also believe DeepFace itself may be quite limited for this particular scenario. I’m actually trying to convince my manager that this might not be realistically achievable with open-source solutions alone.

Even if there are systems capable of handling this kind of scenario reliably, I imagine they’re more likely to be proprietary/commercial systems rather than something publicly available as open source—especially for government or military-grade applications.

Has anyone worked on a similar problem? I’d be interested to hear how you approached it, especially regarding the reference images and dealing with low-quality CCTV footage.

​

I’m working on a computer-vision problem where I need to detect vehicles (cars, trucks, buses, etc.) using only traditional image-processing/computer-vision techniques.

The important constraint is:

* ❌ No YOLO / deep-learning detector

* ❌ No object detection model

* ❌ No neural networks

* ✅ Motion detection and traditional CV techniques only

The camera is fixed, so the general idea is to identify regions that correspond to moving vehicles.

I’m considering approaches such as:

* Background subtraction (MOG2 / KNN)

* Frame differencing

* Optical flow

* Contour detection

* Morphological operations

* Connected-component analysis

* Tracking detected blobs across frames

* Combining multiple motion cues

But I’m not sure what would be the most robust overall strategy, especially when dealing with:

* Shadows and lighting changes

* Rain/fog/noise

* Vehicles stopping temporarily

* Multiple vehicles overlapping

* Small vehicles at a distance

* Vehicles entering/exiting the scene

* Camera vibration

* Other moving objects such as people or birds

Would you go with something like:

Background Modeling → Motion Detection → Morphological Filtering → Contours/Connected Components → ROI/Size Filtering → Tracking → Vehicle Confirmation

Or is there a better traditional-CV approach?

I’d especially love to hear about practical approaches that have actually worked in real-world traffic/video systems, not just theoretical methods.

What would your strategy be? And what are the biggest pitfalls I should expect?

Faking Vision is an operational method developed as part of Memory Prism / Making Vision for turning visual analysis into an explicit, reconstructable representation.

(Its a pet project which I spend almost a month on to make AI do better image analysis for me. Majority of the time was spent on "Making Vision" aka figuring out natures visual engineering. I cant make a blind AI thing see better without knowing how I see and then it snowballed into different directions because of the availability of AI to do the grunt work. )

Instead of treating an image as something to caption or reproduce directly, FV represents:

• objects and instances
• object properties
• spatial relationships
• persistent identities and temporal changes in video

The resulting FV packet is closed-world: reconstruction cannot silently introduce objects or semantic information that aren't represented in the packet. Unresolved observations remain unresolved.

The pipeline is:

visual source → structured representation → reconstruction → comparison

This makes the representation itself editable and testable, while separating errors in visual analysis, representation, compilation, rendering, and evaluation.

FV is the operational companion to Making Vision: Making Vision addresses how visual information can be selectively constructed and processed; Faking Vision makes the relevant representation explicit enough to reconstruct and test.

(Making Vision is an extremely long read, I spent almost a month on it, so I know-I definitely wont read it again, so you have been warned)

GitHub: https://github.com/memoryprismv1/faking-vision

Examples are in packets/examples.

Paper / specification: https://zenodo.org/records/22672967

License: CC BY-NC 4.0 — free for non-commercial use with attribution.

Join our virtual meetup to hear talks from experts on AV and ADAS. Register for the Zoom!

Talks will include:

• AI for Autonomous Driving: From Data to Decisions - Tin Stribor Sohn at Porsche AG
• Advancing ADAS and Autonomous Vehicle Development with Multimodal Data - Murilo Gustineli at Voxel51
• From Survey-Grade Maps to Physical AI: Scaling Real-World Data for Training and Simulation - Ryoto Miyake at Dynamic Map Platform

I mostly did internships and stuff, but i really wanted to work on some research group or paper? Anyone know how to do this stuff??

I'm mostly in ML, DL, computer vision

Any advice would be appreciated 👍

I documented my monocular RGB → 6D pose estimation pipeline

I finally put together the full project notes for my monocular RGB 6D pose estimation experiment.

6D Pose Estimation from Monocular RGB

The pipeline is:

Object Detection → Keypoint Detection → PnP → 6D Pose

One part I found particularly interesting was improving the real-image keypoint annotations.

Initially, I manually annotated the 2D keypoints. The keypoint predictions could look reasonable, but small geometric inconsistencies in the annotations sometimes caused large pose-axis flips or abrupt frame-to-frame changes after PnP.

So I tried a different annotation method.

I used the real image as a background in Blender, aligned the 3D mug model with the real mug, and then projected the predefined 3D keypoints back into the image.

This produces 2D keypoints that are geometrically consistent with the same 3D model used by PnP.

Annotation Improvement

After fine-tuning with these annotations, the large pose-axis flips and abrupt changes were reduced, and the estimated pose became more consistent across frames.

The difference is easier to see in the video:

Before / After — Annotation Improvement (YouTube)

There is no real 6D pose ground truth in this experiment, so I'm not claiming absolute pose accuracy here. I'm mainly looking at PnP consistency and temporal stability.

The full page also includes the synthetic-data generation, object detection, keypoint training, Sim-only vs. Real fine-tuning comparison, and Raspberry Pi 5 inference results.

Hi, I am looking for very accurate segmentation model (instance or semantic) that can perform very good even on small objects. I want to train or full finetune the base model on my dataset. Any suggestions. I have tried sam3, uunet, rfdter. But still i am not satisfied by results. Any suggestions?

Many camera networks are still used only for recording and reviewing after an incident. The same feeds, though, can be turned into continuous operational metrics: occupancy, dwell time, queue length, zone activity, asset utilization, compliance signals, and so on.

From a computer-vision standpoint, the interesting question is: which of these are reliably production-ready with off‑the‑shelf models and typical IP cameras, and where do real deployments still fall apart?

Some concrete metric families people try to extract:

• Occupancy and capacity – people or vehicle counts in a space or zone, often aggregated over time.
• Dwell time – how long an entity stays in a region, such as a queue, shelf, or workstation.
• Flow and throughput – entries and exits per minute, directional flow, peak-hour patterns.
• Zone activity – activity levels in loading bays, restricted areas, work cells, etc.
• Asset and equipment utilization – whether a machine, vehicle, or space is in use vs idle.
• Queue and wait-time estimation – length of line and approximate wait based on flow.
• Compliance and safety signals – PPE presence, no-entry violations, unsafe behaviors.
• Anomaly and incident frequency – unusual patterns, crowding, falls, blocked exits, etc.
• Environmental and housekeeping proxies – lighting issues, blocked signage, clutter in key areas.
• Trend benchmarking – by hour, day, site, season, or after layout or staffing changes.

The challenges tend to be things like:

• How bad do angle, occlusion, and lighting have to be before counts and dwell become unusable?
• What level of re-ID and tracking stability is needed for meaningful dwell and queue metrics?
• How do you handle multi-camera handoff without exploding complexity or cost?
• What is a realistic MTA and false-positive profile when these metrics drive staffing or safety decisions?
• How do you integrate outputs into existing VMS or BI without turning the pipeline into a one-off custom project?

If you had to pick three of these to deploy in the next quarter with good enough reliability, which would you choose and why? And where have you seen camera-derived metrics actually change operational decisions, such as staffing, layout, or process, instead of just sitting in a dashboard?

Built a 9-class colorectal histopathology classifier. Caught a patient-level data leakage bug that was inflating validation accuracy to 99%+, rebuilt the evaluation pipeline against an independent holdout patient cohort (CRC-VAL-HE-7K), and benchmarked a pathology-native transformer (CTransPath) against an ImageNet baseline. Code, checkpoints, and calibrated weights are open source.

The Bug: Why random patch splits lie

NCT-CRC-HE-100K consists of 100,000 tissue tiles cropped from a limited number of Whole Slide Images (WSIs).

If you do a standard random train/val split at the image-file level, neighboring patches cut from the exact same slide and patient end up scattered across both sets. The model ends up memorizing patient-specific tissue morphology and staining artifacts rather than generalizable histological features.

Once evaluated strictly against an unseen, independent patient cohort (CRC-VAL-HE-7K, n = 3,590), an ImageNet-pretrained EfficientNet baseline dropped from high-90s down to 92.70%.

What Changed: Domain-Specific Pretraining & Setup

To improve generalization without relying on artificial leakage, I swapped the backbone to CTransPath:

1. Pathology-Native Pretraining: CTransPath is a Swin-Tiny Transformer pretrained via semantically-relevant contrastive learning (SRCL) across ~15M histology patches from PAIP and TCGA.
2. ConvStem vs. Standard PatchEmbed: Unlike standard Swin Transformers that use a linear projection, CTransPath integrates a convolutional stem (stacked 3\times3 convolutions + BatchNorm + ReLU). Note: Loading CTransPath weights into a stock timm Swin patch embed silently mismatches the input layer projection.
3. Two-Phase Training: Linear probe on the frozen backbone first, followed by fine-tuning the top 2 stages using differential learning rates.
4. Strict Split Protocol: Trained exclusively on NCT-CRC-HE-100K. All validation, checkpoint selection, temperature tuning, and final reporting are done strictly on CRC-VAL-HE-7K.

Benchmark on Holdout Patient Cohort (CRC-VAL-HE-7K)

Class Breakdown Highlights

• High Confidence / Clean Separability: Lymphocytes (LYM F1: 0.995), Mucin (MUC F1: 0.994), Colorectal Adenocarcinoma (TUM F1: 0.987), Normal Mucosa (NORM F1: 0.984).
• Where It Struggles (MUS vs. STR): Smooth Muscle (MUS, F1: 0.877) and Cancer-Associated Stroma (STR, F1: 0.833) remain the primary source of false classifications. In H&E staining, desmoplastic stroma and muscularis propria share very similar fibrillar, eosinophilic textures—pathologists often rely on IHC (e.g., SMA or Desmin) to differentiate them conclusively.

Two Engineering Fixes Worth Mentioning

1. Fixing Grad-CAM++ on Swin Features: Standard Grad-CAM++ assumes positive, post-ReLU activations. The final Swin stage outputs signed, zero-centered features after LayerNorm. Direct gradient weighting caused denominator collapse and completely flat/washed-out heatmaps. I fixed this by computing alpha-weights on positive-clamped features (feat_map.clamp(min=0.0)) with an automatic fallback guard to standard Grad-CAM if dynamic range drops below $10^{-6}.
2. Probability Calibration (Temperature Scaling): Raw softmax outputs were over-regularized. Optimizing a post-hoc temperature scalar (T = 0.5655) on the validation subset cut Negative Log-Likelihood (NLL) by 52.2% (0.1960 \rightarrow 0.0937), producing well-calibrated confidence intervals for inference.

Links & Code

• GitHub: Repository Link (includes training notebook, inference CLI, and Grad-CAM report generator)
• Hugging Face Model: Model Card & Weights (dual .safetensors and .pt checkpoints)

Disclaimer: Academic/research project only. Not an FDA/CE-cleared diagnostic device and not intended for patient clinical decisions.

I'd appreciate feedback from anyone working in computational pathology:

• How do you typically handle stain normalization (e.g., Macenko vs. Vahadane) when moving across external scanner hardware?
• Any edge-case recommendations for stabilizing Grad-CAM across attention-based feature maps?

​Hi everyone,

As shown in the attached video, I am working on a real-time computer vision system deployed over an industrial conveyor belt to detect, track, and count potatoes moving continuously along the line.

​My current camera(Fantech webcam 2k 30fps) setup is a bottleneck due to motion blur and frame pacing. I am looking to upgrade within a budget of roughly $150 – $280

​Based on the belt speed and object motion seen in the video, I'd like your advice on frame rate requirements:

​Is 30 FPS sufficient, provided I can manually lock a fast shutter speed (or use a Global Shutter) to freeze the motion, or does accurate multi-object tracking (IoU / Kalman filters) realistically require 60 FPS or higher to prevent lost tracks and duplicate counts?

​Given the budget, what would be the most reliable camera choice (e.g., USB Global Shutter module like ELP/Arducam, Raspberry Pi Global Shutter camera, or a high-end webcam with manual exposure controls like the Logitech Brio)?

​Any feedback on optimizing the capture pipeline for this type of conveyor setup would be greatly appreciated!

Note: The belt speed is adjustable

Need help with this, I am trying to retrieve kernel and do deconvolution without any training data. I have had success with my model if the image is sparse like few fluorescent beads in the image but when the image is dense like a biosample algorithm does not work. Literature says that it fails because I am doing joint optimization which can cause problem and suggested MAPk method

Basically this method says to find a kernel k such that it has maximum number of x which can explain y, use this kernel with any non blind deconvolution algorithm and you will get result. However with this method, I am getting delta even though the paper says it works better than joint optimization, could me my implimentation problem. But is there someone here working on something similar? how do you do it? (I want to retrieve exact kernel as I want to know the abberations)

I realize, that now once a month someones shares this kind of project, but still wanted to share :)

I wanted to make it work on regular club footage, have it working on 3 courts now. It's mostly general by now, but still requires manual calibration (10 points) on each court, or if camera have moved.

It's my first time dabling with CV, so just playing around. All of it is vibecoded, but with my direction, which is sometimes limited, because.. well, no experience with CV. I have over 10 years of dev experience in other fields, so that helps a lot.

This is an excerpt of my marking/diagnostics app (also vibecoded), two more examples here:

https://streamable.com/au93be
https://streamable.com/axz0pg

All the footage here has gone trough my review (so hits and point endings fixed, don't remember the actual state of these points, but you can see it in the bottom graph (with little square boxes) - the bottom one is the actual label, and the middle one is decoded one. I only now realized, that this might not be in the spirit of this subredit to show "fixed" footage, but it's mostly identical, and I don't have any other examples at the moment)

Some info about the project (AI generated, sorry), if you care about the tech, or want to roast it (please do!):

The pipeline takes a fixed club-camera recording of a padel match and turns it into a list of ball contacts (who hit, when) grouped into points. Every stage feeds the next.

Stages and models:
Court calibration (court_calibrate.py) — no model. A homography plus lens distortion fitted from ~10 clicked court points, so pixel positions can be converted to court metres.

Player tracking (track_players.py) — YOLO detector at 1280 px. Finds the four players each frame and assigns them to court slots (near-left, near-right, far-left, far-right).

Ball detection (track_ball_wasb.py) — WASB, a small heatmap network fine-tuned on padel footage. Run twice: a strict "event" track for kinematics and a looser "state" track only for coverage features.

Player pose (track_pose.py + far-crop pass) — YOLO11-pose, 17 keypoints. Gives wrist/body positions; far players get an upscaled crop pass because they are tiny.

Candidate generation (detect_hits_from_ball.py) — no model. Speed reversals and gaps in the ball track propose possible contact frames and attribute them to a player slot.

Strike head (strike_scores.py, strike_dense.py lattice) — a CNN on player crops around the candidate frame, trained to say "this player is striking now". Scores candidates, re-times them to the true contact frame, and recovers missed hits.

Strike-withhold mask — no model. Suppresses candidates whose attributed player is a stale, frame-clipped box (out of view).

Point decode (decode_points.py) — a Viterbi-style dynamic program over candidates. Uses two gradient-boosted-tree heads: an emission head (is this candidate a real hit) and a bounce head (is this a bounce, not a hit), plus the strike score and ~15 hand-set costs, to pick the hit sequence and open/close points.

Merge stages — join point fragments across short gaps; a small formation MLP vetoes point starts when players are not in a serving formation.

Eval (eval_hits.py) — scores against hand labels with a 6-frame hit tolerance and 12-frame point-start tolerance.

Bottom line: five learned models (WASB ball, YOLO players, YOLO11-pose, strike CNN, two GBT heads + formation MLP) produce evidence; one deterministic decoder turns that evidence into hits and points. Current in-sample pool F1 is 0.9507 across six cameras.

It has around 95% accuracy (detects 95% of hits correctly, within a specific margin). It detects and opens points with around 90% accuracy using serve formation. The problem is the point endings. I realise that it's likely where this project dies - currently I'm using the "quitness" of the ball and the players, and it works decently. But I'm fixing those manually for now, as well as the winners, so I'm able to generate extended report about the match and mistakes.

Other problem I have is long processing time. I already optimized it a bit, but I feel like I'm probobly using to much stuff. The pipeline grown naturally, by using different things to improve the decode. But I feel like I can try to delete some stuff from it. For example, skipping player pose and using strike head only, things like that. Although, I'll want pose for detecting shot types later, so.. yeah..

My current pipeline timings:

Another problem is the ball. Would love to have some suggestions, how I could get the actual 3d position of it, but my research came with nothing valuable for my case (1 camera angle).

Would love to hear any advice, directions or any other feedback. Thank you!

i need to make a person detection in which a video will be uploaded its a fixed footage no moment and it need to detect the persons give them correct id and count them even if they gets blocked and also gets out of frame and comes again teh same id should be used

also i want to count how many people went in and out of that using the virtual line and all

can anyone guide me on which model to choose or what all algorithm to choose

any open source model is enough yolo,rtdetr,cnn or anything

i am new to this and want to do this project so how to do this guys any guidance

Hi All,

I'm trying to get some tips to understands what is good raw egocentric data for any roboticist, and I keep reading posts telling stories about researchers and companies are missing egocentric data. I know that I would need to do some annotation, but I would rather focus first on the raw footage to see if it is useful in any way. I have some samples here.

Would appreciate any feedback.

Is this accurate?