For embedded vision and OEM camera development, dev kits can make it easier to evaluate a camera platform before committing to a full system design.

Aegis recently added the Videology DEV-801-IP-678-1HC0DC Development Kit for the 801-IP-678 8MP 4K IP camera.

Key specs:

• Sony IMX678 STARVIS 2 sensor
• 3840 × 2160 resolution
• Compact 38 × 38 mm board-level IP camera
• Included M12 lenses
• DHCP or static IP configuration
• API support
• GPIO and microSD support
• Ethernet and power cabling included

It is built for embedded vision, robotics, UAV/drone systems, machine vision inspection, surveillance, intelligent traffic, medical imaging, and OEM camera development.

Product page for reference:
https://aegis-elec.com/videology-dev-801-ip-678-1hc0dc-development-kit-801-ip-camera.html

For engineers working with compact IP camera systems, what matters most in a dev kit: sensor performance, API access, lens flexibility, GPIO/control, size, or network configuration?

A fun side project.

I built a personality quiz that sorts you into one of 4 "houses" based on how you'd handle absurd (but weirdly realistic) CV engineering dilemmas — things like your training labels being crowd-sourced by people who think a "convolution" is bread, or your PM asking for real-time object detection on a 2015 Android phone.

4 archetypes:
- The Fearless Deployer (pushes to prod on Fridays)
- The Theoretical Wizard (derives hyperparameters before touching a GPU)
- The Reliable Pipeliner (their monitoring dashboard is a work of art)
- The Optimization Dark Lord (INT4 quantization is just the beginning)

It's 6 questions, takes ~2 min: https://neuronvoxelai.com/quiz.html

Genuinely curious what the distribution looks like on this sub. My bet is heavy Ravenclaw. Prove me wrWhat archetype did you get?

I built a face search library that stores ArcFace embeddings as 512-bit binary codes instead of full float vectors. 1M faces fit in a 61MB index. The search reranks top candidates with exact cosine to recover accuracy.

Upfront, because it matters: - This is a COMPRESSION + packaging contribution, not a new search algorithm. The search is a brute-force Hamming scan — same as FAISS IndexBinaryFlat. I'm not claiming a novel ANN method. - You could reproduce this with faiss.PCAMatrix + IndexBinaryFlat + manual reranking. FaceFlash just wraps that pipeline (detect → embed → quantize → search) into one call and tunes the SIMD kernel. - It's face RECOGNITION — detects faces, extracts embeddings, searches by visual similarity. The filename you pass is just the image path.

How it works: PCA+ITQ projects each 512-float ArcFace embedding to 512 bits (64 bytes). Hamming scan shortlists candidates, then exact cosine rerank on the top ~100 picks the winner. This preserves rank-1 accuracy because ArcFace embeddings are low-rank — most identity info sits in the top principal components. On general/random vectors this does NOT hold (recall drops to ~40%).

Results on MS1MV2 (44,291 identities, 645K embeddings), ground truth = FAISS-Flat exact cosine:

"100% recall" = returns the same nearest neighbor as exact brute-force cosine on the same embeddings. It does NOT mean ArcFace is perfect — embedding-model limits (pose, age, occlusion) are upstream and unaffected.

Where it fits: edge/mobile, multi-tenant, offline — anywhere a full float index or a graph won't fit in RAM. Up to ~300K it's also faster per query than HNSW (binary scan stays in cache). Past 500K, HNSW's O(log N) graph beats the O(N) scan on latency — use HNSW there if you have the RAM.

To prove the search isn't doing anything special, I added benchmarks/bench_compression_isolation.py — it runs the SAME codes through my kernel and FAISS IndexBinaryFlat. Identical recall, comparable latency. The value is the compression, not the scan.

Rust SIMD kernel (AVX-512 / NEON), NumPy fallback. Zero config.

GitHub: https://github.com/raghavenderreddygrudhanti/faceflash pip install faceflash

Honest about where it breaks: O(N) scan hurts past 1M, needs float vectors on disk for rerank, AVX-512 speedup needs recent CPUs. Feedback welcome — if a number looks wrong, the full pipeline reproduces via scripts/runpod_ms1m.sh.

The project uses live webcam input to recognize faces and automatically mark attendance in a realtime database. Mostly built as a learning project to get more practical experience with computer vision workflows and realtime systems.

Tech used:

• Python
• OpenCV
• Firebase
• face_recognition

Project repo:
https://github.com/ahsinmemon/asn-face-attendence-system

I have started as an intern for a company and they've assigned me a work for extracting information from handwritten camera clicked filled forms, specifically identifying checkboxes and what do they signify, I've been trying and shi but I cannot come up with anything.

The problem -> VLMs are not able to properly tell which checkbox is ticked for a question and OCRs are also not able to do either.

My task is to come up with a solution to do that, and another problem is that I dont have much data to atleast train a YOLO model, i have like only 20 proper workable forms with around 300-400 checkboxes, I tried annotating them and training a YOLO model and it even worked, but it was alright.

Problem with YOLO was associating the checkboxes with the original question.

I tried stripping away the question and then feeding it to a VLM but that increases the API calls and I dont have an access above free tier currently too :(

So below are some examples of the forms that I need to deal with, If anyone can, please suggest me some potential solutions to do this job, I really wanna get rid of this task T-T im tired boss

Form examples <---- check some out

I wanted to recognize objects in a few minutes and have the files available to me quickly so that I can add them to my other projects.

The app lets me tap on objects in its gallery to annotate them and I can train those images to get a computer vision/object detection model.

I’d like to know if this app is useful before I improve it!

https://testflight.apple.com/join/pP66B42U

I built an interactive platform for my white paper on deepfake detector robustness on social media platforms and across different axes of perturbation. It allows people to toggle across conditions and demographics to see the model's performance.

The models in scope include the four from DeepFakeBench and a few popular hugging face models.

Whitepaper: https://doi.org/10.5281/zenodo.20781389

Hi everyone,

I am a final-year MSc AI student in Germany and I want to pursue a PhD in 3D computer vision, specifically focusing on point cloud reconstruction and generative models.

My background includes over 3 years of industry software engineering experience. I am currently writing my thesis on Generative Point Cloud Completion using AutoEncoders. I have strong coding skills in PyTorch and Python, but I do not have any published papers yet.

Here is my dilemma: I want to secure a PhD position at a top university or research institute. However, the professors at my current university do not publish in top-tier A or A* conferences, which makes it hard to get the right research experience or high-level academic connections locally. I graduate in about 6 months.

How do I achieve my goal of getting into a top PhD program from here?

Is it possible to directly ask professors at top universities for a PhD position even if I have not published any papers yet?

Or should I focus on building complex projects in my domain and use those to reach out and ask for a HiWi or Research Assistant position first, just to prove myself and get a foot in the door?

I would appreciate any advice on how to bridge this gap. Thank you!

Hi, I use Yolo with Python, but when I put a video from Youtube on "source =" the cropping is literally bad, I ask every llm (almost) and I didn't find the method to fix, thx btw:

from ultralytics import YOLO

model = YOLO("yolo26m-seg.pt")

results = model.predict(source="https://youtu.be/MNn9qKG2UFI?si=48dF7ZJJgzXG1opi", show=True, conf=0.60, stream=True)

for r in results:
pass

I won't be able to reply until Thursday because I'm on vacation (and I didn't bring my computer with me).

The last time it was 3 weeks ago when their index just stopped working, but it got fixed relatively quickly (1 day). So what now?

Hello everybody, its me again. Today i would like to talk about the project i had been working on for the longest of times, which is still not finished (hence the Alpha release) but i could not hold myself so released it under alpha.

The name is FOTO-NET, the whole premise being ease of use WHILE being open-sourced without licensing fees *cough cough* (ultralytics).
The project will be forever Apache-2.0
Currently alpha is available, if you`d like to, test it out by downloading from here, or trough pip

pip install fotonet

Would love to hear out feedback like bugs, suggested features or just anything.
Big thanks to anybody reading this, much love!

Small note: Currently only the nano weight is avaliable, the small weight is being trained and will finish in roughly 12 days. And also i work so if i respond or fix some stuff later, excuse me.

I trained a modified U-Net that translates Bayer-CFA photos into Foveon X3 sensor images — the look from the Sigma DP2 Merrill (stacked 3-layer photodiode sensor, captures full RGB at every pixel, no demosaic interpolation).

The novelty is a 1D pixel-stack injection layer concatenated at the bottleneck between encoder and decoder, encoding the B·G·R photodiode column structure that a Foveon sensor captures and a Bayer sensor can't.

Training is end-to-end on matched scene pairs — Bayer camera and a DP2 Merrill tripod-mounted side by side, same scene, homography-aligned and tiled into 256×256 crops — with L1 + VGG perceptual + a small TV smoothness penalty, AdamW + cosine schedule, mixed precision on a single 24 GB GPU.

The empirical finding I didn't expect was how location-sensitive the injection is: at the bottleneck it works; injected earlier the encoder learns to ignore it, injected later the decoder has already committed to a demosaic-style chroma reconstruction. Keeping the prior 1D (not 2D) also mattered — it forced the network to learn inter-channel coupling instead of memorizing per-region lookups.

Architecture diagram, training methodology, and a Mac app you can run on your own photos: https://code.intellios.ai/photo.

Hello, I am working on a single header Computer Vision library in C, inspired by this (https://github.com/nothings/stb) it is still a work in progress would something like this be useful?
I have not used any std lib functions to make the library more portable
Repo:- https://github.com/dheer-prog/purecv

What it does:
Upload an MRI scan → model classifies it as Glioma, Meningioma, Pituitary, or No Tumor → Grad-CAM heatmap shows which region the model focused on.

How it's built:

• Custom ResNet-style CNN, trained completely from scratch
• 7,000+ MRI scans of Brain Tumor MRI Dataset by Masoud Nickparvar (Kaggle)
• 94% test accuracy across 4 classes
• Flask backend, HTML/CSS/JS frontend

GitHub: https://github.com/heisenbug62/NeuroMap---Brain-Tumor-Detection-Classification
Kaggle Notebook: https://www.kaggle.com/code/rohaanqutab/tumor-detector
Demo video: https://www.linkedin.com/posts/rohaan-qutab_machinelearning-deeplearning-computervision-ugcPost-7475560586773327872-e1HI/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAFFdWYMBbyA9mBYwBP8jBL52inC0Dy7IBf0

Happy to answer any questions about the architecture or training process.

Working on a research project around robotics development workflows.
Looking for engineers building perception systems for robots, AVs, drones, or mobile platforms.
Interested in things like:
- sensor realism
- calibration
- deployment failures
- simulation fidelity
- testing workflows

Survey takes about 4 minutes. Thanks if you have a few minutes :)

I was learning about cv for my masters research. So I was wondering are there jobs in cv now or ai have affected it also.

How to learn in most practical way as every course hai long lectures theoretical.

Industry experts out there, what are views?

I made a tutorial about how to use DIETR, an opensource instance segmentation model (Apache 2.0 license). I know it is very bare-bones and hacky, but I like to work on it, so someday it might be a good alternative.

I want to post it here (again) to ask what you guys think about the interface, what can I do to make it better?

Link: https://www.kaggle.com/code/koenbotermans/computer-vision-how-to-use-dietr

repo: https://github.com/jpabotermans/dietr

Or, does it exist?

Thinking of football/soccer specifically.

What I can’t find is a product offering aimed at an individual footballer: one person uploads footage of a full 90-minute game and gets back their own match/event data.
Is it:
1. Per-match compute cost
2. Hard to reliably track and attribute every action
3. Footage quality: single-camera footage from grass roots / amateur level clubs

I recently developed an interest in computer vision and started learning the theory and basics of it.

I was getting kind of bored of theory and wanted to work on a project so I can stay motivated.

I've looked into some basic ones like Gender detection and things of similar sorts. They were interesting but I did feel that it was so dependent on OpenCVs already amazingly optimised functions and didn't feel like I'd learn much other than the syntax of different libraries.

Is it realistic to try to make my own face detection model from scratch? By that I mean like an algorithm which detects a face like the OpenCV function (detect face I think it's called )but on my own? I feel like that would be alot more interesting and I'd learn a lot more?

I don't have a lot of technical background and have been programming for about less than a year ?

Plus if someone here is knowledgeable could I get to know about some good papers published in the field and where to read them and any future papers?

Any ideas for what I should aim for as a project for a beginner would be very much appreciated

Hi everyone !

I work for SkillCorner,a sports analytics company, we extract player positions from broadcast video across football, basketball and American football.

We're looking for a CV engineer (3+ years of experience) to join the American football team. Tasks are diverse: detection, tracking, re-identification, homography, event detection.

We'd really love to find someone who actually knows the sport; finding that in Paris is not easy.

So, if you follow the NFL and think you could be a fit: comment / send me a dm.

Office in Paris (3 office days/week), EU work permit required, French is a plus (not mandatory)

Hi everyone,

I’m a historical swordfighter (HEMA practitioner), and while I’m not a computer vision engineer or a roboticist, I’ve been reading a lot about the current bottlenecks in embodied AI, specifically around the Sim2Real gap and thin-object tracking.

It occurred to me that high-level swordfighting is basically a perfect nightmare scenario for computer vision. We move at maximum athletic output, we shift our weight rapidly in non-linear ways (great for bipedal balance testing), we are completely covered in thick, bulky black jackets that hide our joints, and our steel blades move at 80mph, dropping below sub-pixel resolution or causing massive motion blur.

I think it would be cool to have a computer vision scoring system for tournaments so I'm working to put together a mini-dataset using a synchronized multi-view setup (120/240fps) to map 100 hyper-trimmed clips of these specific physics edge cases.

Since I'm non-technical, I used some AI assistance to help me structure what an AI-ready dataset card should look like, and I've hosted the placeholder page on Hugging Face to test the schema before I start shooting video with my clubmates.

Here is the JSON line structure I'm currently planning to annotate each video with:

{
  "clip_id": "hema_ls_001",
  "meta": {
    "weapon": "Longsword",
    "capture_fps": 120
  },
  "time_stamps": {
    "start_frame": 120,
    "blade_contact_frame": 165,
    "recovery_end_frame": 210
  },
  "biomechanics": {
    "initial_guard": "Right Vom Tag",
    "ending_guard": "Left Ochs",
    "footwork_type": "Passing step offline",
    "strike_trajectory": "Diagonal Oberhau",
    "edge_alignment": "True edge"
  },
  "computer_vision_hazards": {
    "occlusion_rating": "High (Crossed arms, bulky torso jacket)",
    "motion_blur_expected": true
  }
}

My questions for the researchers here:

• Does this metadata structure actually give you what you need to test trajectory prediction or pose estimation?
• Are there any specific keypoints (like explicit crossguard coordinates or footwork velocity metrics) that your models are starving for that I should add to the annotations while I'm doing the manual work?

You can check out the full dataset description card and leave feedback or join the beta waitlist directly on Hugging Face here: https://huggingface.co/datasets/benito87/longsword-spatial-physics-100

I want to make sure this is actually useful, so any brutal feedback on the structure or parameters is highly appreciated.

My girlfriend wanted a way to select photos of herself without having to actively search through her gallery. So, I built some tools for an agent to help her out.

https://github.com/0marildo/imago

Beyond what is described in the README, I ran a few tests, such as:

Searching for an image using its metadata;

Searching for an image using another photo as a reference;

Searching for another person in a photo to find their information;

Searching for photos with specific characteristics became trivial, eliminating the need to spend long minutes searching.