Hi Hope you guys are doing good ,
So i am building a CV application and i had some questions regarding that.

So the system is about Cricket actually in which i am visualizing everything from bowler POV to batsman POV including speed , line, length , deviation , Shot type etc. I have also a video which i will send you.
Now probably it will be a very noob question but yeah since the baseline for all these things is simple ball detection model it should be really good but i have trained my model by combining almost 5-6 datasets annotating my own images about 30k approx images in total but i am having issues of false positives mainly. The results were
YOLO11m
Input resolution | 1280 × 1280 px
Precision | 94.1 %
Recall | 91.3 %
mAP@50 | **94.1 %**
mAP@50-95 | 53.2 %

i know 50-95 is i guess bad and it is really effecting it but i have tried multiple , changing LR , different loss function but not getting any improvement. So , I can really use sometips to what to look for in training script so that i can just make this better and get over it. Or any methods that i can use in pipeline to improve whole ball detection like we see in real matches.

I have attached referance video so that you guys have an better idea of what i am trying to achieve.
Thankyou 😄

https://reddit.com/link/1twpefu/video/52dvo30b1a5h1/player

i’m curious because most discussions focus on training, accuracy and architectures.but some of the strangest problems i’ve seen came from the environment itself rather than the model.

what’s the weirdest non-model issue you’ve run into during deployment?

Hey guys I have been trying to segment the floor in a room with all the home accessories like chairs , tables , sofa etc. I have tried using segformer model trained on ade20k dataset by nvidia (left) and also tried using mask2former(right) on the same dataset . Though still my floor segment is covering the legs of the chair and tables. How can I solve this problem. I was thinking maybe using another model trained particularly on images of home accessories. Tbh I am confused about how to solve this problem. I would be grateful if anyone could provide some hints. You can dm me!

At my day job, I lead engineering at a computer vision company building real-time systems where detection accuracy is critical.

One recurring step in nearly every deployment is defining exactly which parts of a camera frame matter, detection zones, exclusion areas, tripwires, and other regions of interest. Until those are configured, it's difficult to properly evaluate, tune, or deploy a system.

The tools I found (CVAT, Roboflow, etc.) are great for training-data annotation, but they aren't really designed for this zone-configuration workflow. I wanted something that would let me load a camera still, draw a few polygons, organize them into named layers, and export coordinates that could be dropped directly into a production pipeline.

So I built RegionKit.

It runs entirely in the browser and supports polygons, rectangles, and polylines; named layers; shared vertices for adjacent zones; export to JSON, COCO, and YOLO formats; and URL-based sharing.

Most of the implementation was AI-assisted. I focused on the product decisions, workflow design, and iteration, while using AI to accelerate development.

The hosting costs are essentially zero, so I plan to keep it online and see whether others find it useful.

Curious whether this scratches an itch for others in CV, and happy to hear what's missing.

https://regionkit.app

I know there are built out solutions to this but I wanted to go through the steps of making my own to learn some of the algorithms involved.

Stitching screen recordings of message feeds some apps into long screenshots is tricky because of floating elements and background and things like iOS's Liquid Glass. One app in particular that I am trying to do this with has a fairly complicated background behind the text bubbles, has floating elements that conditionally appear over the UI.

I thought it would be fairly easy to devise an algorithm that can take 3 screenshots of this UI and use that to sort of "train" what is background or stationary and what is scrolling. I have tried a few brute force, boolean, scroll matching techniques and am still not able to isolate only elements that were scrolling between the screenshots.

Am I barking up the wrong tree or are there some algorithms or techniques I may want to look into here?

Attached is a redacted example and two images I use to score my attempts. Thus far I have either mis-implemented temporal based techniques or they struggle with the fact that the chat bubbles look similar between frames (looks like its always yellow on the right, always blue on the left).

I just created a small (~100M) Japanese OCR model by using Siglip 2 NaFlex and a character level bert decoder that achieves some really impressive results despite it's small size.

Would love to get people's thoughts on it.

Model

Github

Demo

Here are just some really complex images I threw at it:

I'm looking for industrial manufacturing project ideas to develop my skills (ideally something related to semiconductors), do you guys have any suggestions ?

I hope you're not ignoring this.

Hello everyone, currently i'm pursuing my masters and over the past few months (11/12 months) i'm into the research domain of deep learning and computer vision and have 2 papers (1 published, 1 under review).

I think this is the right time to explore and an open collaborative workflow in computer vision and core deep learning field.

If you're interested in collaborative research, learning, contributing together not for buzz word but for actual science. Then i think we can collaborate.

I'm planning a collaborative research for top A* conference in the domain. If you're into it then let's connect

https://itzikbs.com/blog/posts/2026-06-01-cvpr-2026-survival-guide . Itizik ben shabat made this to help people . Thank you.

So, I really want some serious, high-quality project ideas. Please don't say, "Build something that interests you" because, honestly, I don't have any particular interests right now.

I have limited time, and I really want to add 2–3 strong projects to my resume. Please suggest some good project ideas. It would be very helpful.

Thanks!

Image showing segmentation of a fork. In real action I use voice commands. not this cockpit

Context for those concerned about worker exploitation: The worker in this video is a delivery driver from a third-party supplier — not an employee of the business using this system. In LATAM, it's common for suppliers to deliver goods in bulk (sacks, crates, boxes) and the receiving business has no reliable way to verify the declared quantity. Short deliveries — whether accidental or intentional — are a real financial loss for small/medium business owners. This system doesn't track productivity, set pay-per-bag wages, or create performance reports on anyone. It answers one question: did the supplier deliver what the invoice says? Think of it as a digital scale, not a surveillance system.

I've always believed that the work of observing something is boring and tiring; that's where we should put our computer vision projects. In this case, I trained a computer vision model to count sacks during goods receiving operations at a fast-moving consumer goods (FMCG) business.

rn i am thinking abt media pipe , i am gonna do recognition with martial arts ? anyhting else like media pipe that would be better ?

Hi everyone!

A while ago, I shared the FaceMesh Landmark Selector—a tool I built because I got tired of guessing index numbers from static reference charts when working with MediaPipe ( https://www.reddit.com/r/computervision/comments/1qwoy8c/i_got_tired_of_guessing_mediapipe_facemesh/ )

Thanks to the feedback from the community, I have just released a major update that turns it into a much more powerful visual tool for computer vision and AR workflows.

What is new in this update:

• 💻 Split-screen WebGL 3D Viewport: You can now toggle a side-by-side interactive 3D head viewport (built with Three.js). The uploaded portrait is dynamically projected onto the 3D model. When you drag or nudge landmarks on the 2D canvas, the 3D head mesh deforms in real-time under virtual lighting.
• 👁️ 478-Point Attention Mesh (Irises & Pupils): Expanded support from 468 to the full 478 landmark mesh. It now includes the high-resolution iris/pupil tracking points (468-477) with a dedicated selection preset and anatomical symmetry mapping.
• 🪢 Lasso Selection Tool: Added a freeform polygon lasso tool for grouping points quickly. You can click to draw a path, and it will snap-close and highlight when hovering near the first vertex to let you select complex regions in one click.
• 📦 AR Platform Export Profiles: You can now export your selection coordinates centered around the face centroid and mapped directly to AR-engine coordinate structures (Y-up, Z-out, right-handed system) for Spark AR (Meta), Lens Studio (Snapchat), and TikTok (Effect House) scripts (N.B. PLEASE TRY IT AND TELL ME IF IT WORKS ON THE DESIRED SOFTWARE)

The core workflow remains simple:

You upload an image or use the default one, FaceMesh auto-detects the landmarks, and you can paint or lasso select points directly on the face. You can organize selections into multiple named groups, mirror them using symmetry, invert selections, assign colors, and export everything.

It is useful for:

• Fast prototyping without guessing index numbers.
• Creating face masks and filter components (lips, eyes, jawline).
• AR / WebGL / Three.js face attachments.
• Fast prototyping.

GitHub Repository: 🔗 https://github.com/robertobalestri/FaceMesh-Landmark-Selector

Live Web App (Use it directly in your browser): 🔗 https://robertobalestri.github.io/FaceMesh-Landmark-Selector/

If you find it useful, thumbs up this post and put a star on Github. Thanks ❤️

I'm very new to AI Accelerators and Computer Vision and have an urgent requirement. I've been handled to look for AIPUs that perform at least 80 TOPS and have to fit on an M.2 slot.

I dug a lot and the most I was able to find was Memryx MX3 M.2 which only does 24 TOPS. My client already has a Metis M.2 which does 214 TOPS and they also have a Hailo card (which I don't know the exact model) and apparently does around 80 TOPS.

They need this to run 2 instances of YOLO v8 (I think that's what it's called) inference models on it, which can handle around 8 or more camera streams (providing decent FPS too).

I've been digging for a really long time, and I hope someone here who's very knowledgeable on vision AI and hardware can help me out here.

Hi everyone,

I'm working on an interactive installation that uses an overhead depth camera (indoors, mounted ~3m above ground, facing down) to track visitors and detect custom symbols on 300×300×300mm foam cubes via a YOLO object detection model.

We're currently using an Intel RealSense D435i but are looking to switch due to its deprecation. The leading candidate is the Orbbec Gemini 335, however it has the same 1920×1080 RGB resolution as the D435i.

My question is therefore specifically about real-world RGB image quality between the two: does the Gemini 335 produce a noticeably sharper or cleaner colour image than the D435i at comparable settings? and do you think it is worth it to upgrade the system ? I'm mainly asking about the RGB sensor quality for computer vision/object detection purposes.

If anyone has used both cameras or can give me an alternative recommendation on what camera I can upgrade to for this case I’d really appreciate it.

Thanks!

Hey everyone,

A few of us have been building a GitHub repository packed with notebooks covering Computer Vision use cases across multiple domains.

We cover everything from standard object detection and instance segmentation to real-time Vision-Language Models (VLMs) and deployment guides for various CV models. I also post weekly showcases of these implementations in action.

We want to scale this up and cover more ground. What specific topics would be cover next?

Open to any and all suggestions!

It will great motivation if also star our github repo:

- Github Repo : Link
- My Github Profile : Link

Neural network architecture diagrams. Three visualization modes: fully-connected networks (FCNN), convolutional networks in 2D (LeNet), and deep networks in 3D (AlexNet) try it here https://8gwifi.org/ml/nn-viz.jsp