About 7 months ago I kept seeing videos of people waving their hands at a camera while the music reacts to them, almost all of them made in TouchDesigner. I opened TouchDesigner, stared at the node graph for a while, and closed it.

So, being a C++ dev, I did the obviously more reasonable thing and spent months building a VST/AU plugin that does it inside Ableton Live instead. It uses OpenCV for the webcam input and MediaPipe for hand tracking. You can either play MIDI notes with your hand or map it to any effect parameter in DAW.

Honestly, I started making this plugin for myself, but then decided I could publish it: https://handlink.itch.io/hand-link

Hello everyone!
We are a group of female Computer Science students working on our graduation project.
We are having difficulty finding suitable datasets to train a YOLO model.
We found a dataset called **ImageCLEF 2021 DrawnUI Wireframe Datase https://www.aicrowd.com/challenges/imageclef-2021-drawnui-wireframe?utm_source=chatgpt.com**
but unfortunately, it is no longer available for download
**Does anyone have access to this dataset or know how we can obtain it?**
We need it for research purposes only.
Thank you so much to anyone who can help us! ❤️

Hi,

I hope you are all doing well! I was curious what the current best multi-object detection model is for an iOS and Android application that needs to detect and segment multiple objects in an image taken. Would it still be YOLO11 or YOLO26? If anyone could give me any leads I would greatly appreciate it!

Also, out of curiosity, if I had the model on a server instead of locally on the phone, would using RF-DETR be more appropriate?

I am fairly new to the computer vision space so my apologies for the ignorance.

Thank you

Hi everyone,
I work in automotive software and want to build a solid understanding of Image Signal Processing (ISP) for ADAS cameras. I’m new to ISP, and I find vendor documentation difficult to follow without understanding the basics first.
I’m looking for a structured learning path covering:
Camera fundamentals: How image sensors work, RAW images, Bayer patterns, exposure, gain, and noise.
ISP pipeline: Demosaicing, white balance, noise reduction, color correction, HDR, and tone mapping.
ISP tuning: How to evaluate image quality and understand the trade-offs between different settings.
ADAS applications: How ISP settings affect object detection and perception, especially in low light or high-contrast scenes.
Which courses, books, YouTube playlists, or practical tutorials would you recommend? Free and paid options are both welcome. Hands-on exercises using RAW images, Python, or OpenCV would be especially helpful.
If you work in automotive imaging, where would you start, and in what order would you learn these topics? Please share links or resource names.
Thanks!

I'm looking into face-tracking SDKs for a project I'm working on and before buying anything I want to hear about people's personal experiences.

Has anyone here worked with any of these (Banuba, Azure Face API, AWS Rekognition, Face++, MediaPipe) in a real project? Curious about:

Does the latency/performance stuff hold up in practice?

Any hidden pain points not mentioned in these kinds of writeups (pricing surprises, integration headaches, docs quality, etc.)?

Would you recommend something else entirely that wasn't even covered (MediaPipe, ARKit/ARCore, open-source options)?

Just want to know how these actually perform in real use.

The clip shows defect detection on sewer CCTV footage across different pipe types and materials. The defect classes follow EN 13508-2.

Curious to compare notes with anyone working on this:

1. How do you handle false positives that depend on the pipe material, e.g., seams or streaks on plastic pipe being picked up as cracks?

2. Detection, segmentation, or frame-level classification: what has worked best for you, especially for defects that cover large areas?

3. How do you evaluate: per frame, per defect, or per meter of pipe?

4. Any tricks for rare defect classes: synthetic data, augmentation, or oversampling?

Hey!

For a couple of years, I’ve been working on and off on synthetic data generation in Blender. I’m now developing a Blender extension/Python library to streamline and organize the process.

I’m getting close to the point where I’ll need to make some final decisions about its shape, so I’m looking for input from people who do SDG or train models on synthetic data.

The easiest way to get a feel for the library is to check out the quick start examples: https://blendmentation.docs.csmx.eu/quick-start/
The repo is available at: https://github.com/mmaciejak/Blendmentation

Are there any people here that managed establish a career as a freelancer developing or training computer vision models for customer-specific tasks? Could you tell me about your experience? Is this even a thing at all in the industry?

I imagine many smaller/older companies have processes/tasks that could be significantly accelerated or automated entirely by using computer vision to analyze complex image information instead of relying on human assessment.

working on replacing my 10 year old Halloween box that used mostly timers and beam sensors with something more modern and that I don’t have to re-learn every year. decided to use yolo for positioning.

first constraint is rtsp, camera is very far any server and 70’ from house so must encode. there goes 60-100ms.

im projecting position on a straight line, bottom center, perpendicular intersection is position.

should i use kalman or predictive filtering at all? the primary reason im keeping it now is so i can quickly lock in with medium confidence and then just rely on low confidence there after. that means *some* sort of tracking but at 20FPS I’m not so sure I need any.

point of app is to track people’s linear progress. I’m pretty happy with it now as long as I keep tracking coast down to 200ms or less. wondering what a pro would do. I don’t expect laser beam sensor speed. Any further ideas?

i made the docker app public as “yolo-halloween” obv had to put agpl3 because ultralytics monopoly at least when it comes to accuracy and performance on my intel minis.

Finished this project up today. Used roboflow to train all the images on the board. Ran a really simple FastAPI backend. Used the stockfish api and all that.

Hey everyone,

I'm new to the sub and im a junior getting deeper into computer vision performance optimization.

I’m currently researching the bottlenecks of running heavy object detection models at very high frame rates. I know most standard applications are fine with 30-60 FPS, but I want to understand the extreme end of the spectrum.

My question for the professionals here is: what specific fields or applications are actually waiting for object detection models to improve so they can reliably run at 100+ FPS in the future?

I mean, like outside fields like High-speed industrial automation, such as tracking items on fast conveyor belts or Sports analytics, what else is there?

Hey everyone! I'm looking to explore some interesting OpenCV projects and could use your suggestions. Are there any good GitHub repos you've come across that are worth checking out? I'm looking for projects that are practical, interesting to build, and solve real-world problems using computer vision. I'd love to hear your recommendations.

might be of interest to this community. Certain objects (depth, segment, objectdectect) support dynamically swapping out the models, and is therefore super convenient for comparing
both performance and effectiveness.

I want to build a handwritten mathematical expression predictor, which will predict the mathematical operators and digits and then give me the result as output, currently I am including just 4 basic mathematical operations, I want to add open CV camera feature to that project, how will I add it? Can someone help me by providing suggestions or tutorials ?

We’ve been working on mapping points in surveillance footage to geographic coordinates. In this demo, clicking a point in the video shows its corresponding location in a 3D scene.

The approach is purely vision-based and requires no additional hardware. We’re exploring how it could support location reconstruction, trajectory tracking, and analysis of camera coverage gaps.

I’d be interested to hear from anyone working on similar image-to-world mapping problems.

I'm a fresher aiming for computer vision and ML engineer roles. I finished a project and I'd like honest feedback on whether it's interview-worthy or not.

What it does: detects 6 kitchenware classes (plate, spoon, fork, knife, cup, glass) in live camera video on an Android phone, fully on-device.

What I did:

- Data (9,400+ images, ~6.5-7 hours total): I labeled it semi-automatically, not by hand-drawing every box.

1. Ran Grounding DINO on 900 images (150 per class) to generate bounding boxes, then checked and corrected them by eye.

2. Trained a seed YOLO model on those 900 images and used it to label the remaining images.

3. Reviewed those labels by eye for missed or wrong boxes, and did the corrections in X-AnyLabeling.

4. Published the dataset on Kaggle: https://www.kaggle.com/datasets/raunakgola/kitchenware-object-detection-dataset-yolo-format

- Model selection with Optuna: 25 trials, max 15 epochs each, maximizing mAP50-95. Search space:

- model: yolo26n/s, yolo12n/s, yolo11n/s, yolov9t/s, yolov8n/s (10 candidates)

- lr0: 1e-4 to 1e-2 (log scale)

- batch size: 8, 16 or 32 (capped for 24 GB VRAM)

- mosaic: 0.0 to 1.0

- Training: fine-tuned the two models I picked, YOLO26n and YOLO26s, on the full dataset.

- Compute: a RunPod RTX 4090 pod. Optuna, both training runs and the model conversion took about 5 hours in total and cost about $4.5. It saved me a lot of waiting compared with Google Colab.

- Export + quantization: exported to LiteRT and compared formats. Static INT8 was rejected. I went with 8-bit weights and float32 activations (w8a32): mAP50-95 0.9406 at 2.72 MB.

- App: Kotlin app with CameraX and LiteRT, GPU first with CPU fallback. It filters by class, has confidence/IoU sliders, and shows live FPS, latency and RAM. On my one test phone it ran about 10-14 FPS on GPU and about 4-5 FPS on CPU (single readings, not a proper benchmark).

Being upfront: I generated the Android app with an AI coding agent (Claude Code). I directed it and tested it, but I didn't hand-write the Kotlin. The ML side (data, Optuna, training, quantization decision) is my work.

Known weaknesses:

- The 0.94 mAP is on my validation split, not a separate real-world test set.

- The labels (train and val) came from the same semi-automatic pipeline, so the score partly reflects label consistency.

- It misses some objects and sometimes confuses fork and spoon.

- I tested on one phone only.

Repo: https://github.com/raunakgola/Wareye

My questions:

1. Is this strong enough to help in interviews, or does the AI-built app hurt it?

2. What would you add or fix first: a held-out real-world test set, error analysis, something else?

3. Would you rather see more depth on the model side or a hand-written app?

Thanks for any honest feedback.

Hey everyone! Previously, I built a 5-servo robotic hand controlled via computer vision. Instead of modifying the old setup, I decided to build a completely new robotic hand from the ground up.

This new version steps up to 6 servo motors to get much more complete, fluid hand movement.

How it works:

• Real-time Tracking: Using Python, OpenCV, and MediaPipe on the laptop to track hand landmarks live.
• The Hardware: Wired up 6 servos acting as tendons to control the fingers and the extra axis of movement.
• The Test: Putting the new frame and wiring through its paces to check gesture mirroring and response time.

Still working on refining the code and handling power distribution for all 6 motors. Would love to hear your thoughts or any tips you have for multi-servo setups!

The approach followed:

- YOLO11n-pose gives 17 keypoints. Keypoints 0-4 (nose, eyes, ears) give the head centre.

- Box size comes from ear-to-ear distance, eye distance x 3.2, or shoulder width x 0.6, whichever is most reliable that frame.

- If the person is facing away (no face keypoints), fall back to the top ~22% of their body box.

- Pixelate an oval on a worker thread, encode the JPEG, then restore the sharp pixels for the local preview.

- Boxes older than 0.6 s are dropped, so it never blurs the wrong spot. Costs ~3 µs on the main thread and ~50-100 µs per frame on the encoder thread, with zero GPU cost.

Where it can fail: heavy occlusion and very small/far faces.

Hello! I am new here and basically wanted to know if someone can recommend any cost-effective development board for embedded solution? Basically an embedded board that can be programmed for edge usages, and allow adding sensors like camera.

We continued pre-training kha-white's mangaOCR (same architecture, new vocabulary) on the training data of manga-ocr-nar-small, and released two models:

- manga-ocr-base-CPT (224×224, same input as the original): https://huggingface.co/muscgab/manga-ocr-base-CPT

- manga-ocr-base-CPT-flex (aspect ratio kept): https://huggingface.co/muscgab/manga-ocr-base-CPT-flex

JMangaBench_Read v1.3, 7,789 boxes, exact match: original mangaOCR 81.29% (greedy) → CPT 91.00% → CPT-flex 91.42%.

With greedy decoding and CUDA graphs it takes about 11 ms per box at batch 1 on an RTX 2080 Ti (original package: 84.9 ms). The repo also has inference scripts that make Hayai v2.5 Nova and PaddleOCR-VL-For-Manga about 2.6× faster at batch 1, with identical outputs.

Code and technical report: https://github.com/muscgab/manga-ocr-CPT

Caveats: the benchmark is ours (tight text-block crops from AnimeText YOLO12x detector, mostly web-published manga).

A candidate has a technical interview in a few days for an MLE internship on TikTok's data search/visual search team. How would you divide the remaining prep time?

The immediate question is how much to spend on coding versus ML fundamentals and search/computer vision concepts. Intern-level advice would be especially useful: which ideas should a candidate be able to explain clearly, and what kinds of practice helped connect the theory to an actual interview discussion?

TikTok Interview Questions can help organize company-specific practice, though the team and role still matter when choosing prompts. If other internship applications are eating into the remaining days, application autofill and Agent autoapply can reduce the repetitive admin.

If you've interviewed with this team or a similar one, what was worth reviewing? What would you leave out when time is this short?

Disclosure: I operate PracHub and LandOffer.