I will have 4 videos, each of which needs to be split into approximately 55,555 frames. Each of these frames will contain 9 grids with numbered patterns. These patterns contain symbols. There are 10 or more different symbols. The symbols appear in the grids in 3x5 layouts. The grids go in sequence from 1 to 500,000.

I need someone who can create a database of these grids in order from 1 to 500,000. The goal is to somehow input the symbols appearing on the grids into Excel or another program. The idea is that if one grid is randomly selected from this set, it should be easy to search for that grid and identify its number or numbers in the database — since some grids may repeat.

Is there anyone who would take on the task of creating such a database, or could recommend someone who would accept this kind of job? I can provide more details in private.

Hi peers!

anyone of you geniuses tried to compile lightglue model to .hef format, to run on a hailo8 accelerator?

Like pens, chairs, scissors, person, laptops and stuff... Without having to spend hours on collecting data and annotating them manually?

PS: I'm a complete beginner

Hi all,
I'm working on a project where I need to detect numbers (e.g. measurements, labels) on various architectural plans (site plans, floor plans, etc.).

Is there a solid pre-trained CNN or OCR model that handles this well — especially with skewed/rotated text and noise?

Would love to hear if anyone has experience with this kind of input or knows of a good starting point.

Thanks!

currently a sophomore in college. This year, i realized that i really want to pursue a career in cv after graduation. I am looking for any advice/ project ideas that can help me break in. Also, i have some other questions in the end.

for context, i am currently taking cv + ml and some other classes right now. Also, i am in a cv club. i had worked on aerial mapping and fine tuning a yolo model (current project). i have 2 internships + 1 this summer (prob working w/ distributed sys). none of them are related to software. also, abs terrible at leetcode.

lastly, i am not sure if this applies. i really wanna do cv for aerospace, specifically drones or any kind of autonomous system. ik the club i am in is alr offering a lot of opportunities like that, but i still need to put a lot of work in outside club.

also, rn. i am putting time into reading cv papers as well.

questions

1) what is a typical day like? ik cv engineers fine tune models. what else do they do?

2) project suggestions? if it include hardware like an imu that would be great.

3) what is the interview process like? do they test u on leetcode or test u on architectures?

Hi community,

I'm quite new to the space and would appreciate your valued input as I'm sure there is a more simple and achievable approach to obtain the results I'm after.

As the title suggests, I have a use case whereby we need to detect if image 1 is in image 2. I have around 20-30 logos, I want to see if they're present within image 2. I want to be able to do around 100k records of image 2.

Currently, we have tried a mix of methods, primarily using off the shelf products from Google Cloud (company's preferred platform):

- OCR to extract text and query the text with an LLM - doesn't work when image 1 logo has no text, and OCR doesn't always get all text
- AutoML - expensive to deploy, only works with set object to find (in my case image 1 logos will change frequently), more maintenance required
- Gemini 1.5 - expensive and can hallucinate, probably not an option because of cost
- Gemini 2.0 flash - hallucinates, says image 1 logo is present in image 2 when it's not
- Gemini 2.0 fine tuned - (current approach) improvement, however still not perfect. Only tuned using a few examples from image 1 logos, I assume this would impact the ability to detect other logos not included in the fine tuned training dataset.

I would say we're at 80% accuracy, which some logos more problematic than others.

We're not super in depth technical other than wrangling together some simple python scripts and calling these services within GCP.

We also have the genai models return confidence levels, and accompanying justification and analysis, which again even if image 1 isn't visually in image 2, it can at times say it's there and provide justification which is just nonsense.

Any thoughts, comments, constructive criticism is welcomed.

Why isn't deformable convolutions not used in real time inference models like YOLO? I just learned about them and they seem great in the way that we can convolve only the relevant information instead of being limited to fixed grids.

Hey everyone!

I'm currently an undergrad in Computer Science and starting to think seriously about my thesis. I’ve been working with synthetic data generation and have some solid experience building OCR pipelines. I'm really interested in topics around computer vision, especially those that involve real-world impact, robustness, or novel datasets.

I’d love some suggestions or inspiration from the community! Ideally, I’m looking for:

• A researchable problem that can be explored in ~6-9 months
• Something that builds on OCR/synthetic data, or combines them in a cool way
• Possibility to release a dataset or tool as part of the thesis

If you’ve seen cool papers, open problems, or even just have a crazy idea – I’m all ears. Thanks in advance!

My latest blog delves into the incredible advancements in Vision AI through the power of deep learning. The piece explores how cutting-edge algorithms are enabling machines to interpret, analyze, and interact with visual data like never before—be it through facial recognition, autonomous vehicles, or healthcare diagnostics.

As computer vision becomes more integrated into our daily lives, questions about its ethical use, potential biases, and long-term societal impacts are growing. For example, how do we balance innovation with concerns over data privacy and fairness?

Check out the blog here: Vision AI - Advancing Computer Vision with Deep Learning. I’d love to hear your thoughts—are we ready for the profound implications of Vision AI, or is society lagging behind in addressing its challenges?

I experimented a few months ago to do a template-matching task using U-Nets for a personal project. I am sharing the codebase and the experiment results in the GitHub. I trained a U-Net with two input heads, and on the skip connections, I multiplied the outputs of those and passed it to the decoder. I trained on the COCO Dataset with bounding boxes. I cropped the part of the image based on the bounding box annotation and put that cropped part at the center of the blank image. Then, the model's inputs will be the centered image and the original image. The target will be a mask where that cropped image was cropped from.

Below is the result on unseen data.

Another example of the hard case can be found on YouTube.

While the results were surprising to me, it was still not better than SIFT. However, what I also found is that in a very narrow dataset (like cat vs dog), the model could compete well with SIFT.

Hi all, I am currently working on a project of event recognition from CCTV camera mounted in a manufacturing plant. I used Yolo v8 model. I got around 87% of accuracy and its good for deployment. I need help on how can I build faster video streams for inference, I am planning to use NVIDIA Jetson as Edge device. And also help on optimizing the model and pipeline of the project. I have worked on ML projects, but video analytics is new to me and I need some guidance in this area.

Hi, I'm working on a student project focused on perception for autonomous vehicles. The initial plan is to perform real-time, on-board object detection using YOLOv5. We'll feed it video input at 640x480 resolution and 60 FPS from a USB camera. The detection results will be fused with data from a radar module, which outputs clustered serial data at 60 KB/s. Additional features we plan to implement include lane detection and traffic light state recognition.

The Jetson Orin Nano would be ideal for this task, but it's currently out of stock and our budget is tight. As an alternative, we're considering the Raspberry Pi 5 paired with the AI HAT+. Achieving 30 FPS inference would be great if it's feasible.

Below are the available configurations, listing the RAM of the Pi followed by the TOPS of the AI HAT, along with their prices. Which configuration do you think would be the most suitable for our application?

1. 8 GB + 13 TOPS — $165
2. 8 GB + 26 TOPS — $210
3. 16 GB + 13 TOPS — $210
4. 16 GB + 26 TOPS — $255

Comment if you want one!

I'm new to Raspberry Pi, and I have little knowledge of OpenCV and computer vision. But I'm in my final year of the Mechatronics department, and for my graduation project, we need to use a Raspberry Pi to calculate the volume of cylindrical shapes using a 2D camera. Since the depth of the shapes equals their diameter, we can use that to estimate the volume. I’ve searched a lot about how to implement this, but I’m still a little confused. From what I’ve found, I understand that the camera needs to be calibrated, but I don't know how to do that.

I really need someone to help me with this—either by guiding me on what to do, how to approach the problem, or even how to search properly to find the right solution.

Note: The cylindrical shapes are calibration weights, and the Raspberry Pi is connected to an Arduino that controls the motors of a robot arm.

I've been stuck using yolov7, but suspicious about newer versions actually being better.

Real world meaning small objects as well and not just stock photos. Also not huge models.

Thanks!

Here's a beginner question. I am trying to build a setup (see schematic) to image objects (actually fingerprints) that are 90 deg away from the camera's line of sight (that's a design constraint). I know I can image object1 by placing a 45deg mirror as shown, but let's say I also want to simultaneously image object2. What are my options here? Here's what I've thought of so far:

1. Using a fisheye lens, but warping aside, I am worried that it might compromise the focus on the image (the fingerprint) as compared to, for example, the macro lens I am currently using (was imaging single fingerprint that's parallel to the camera, not perpendicular like in the schematic).

2. Really not sure if this could work, but just like in the schematic, the mirror can be used to image object1, so why not mount the mirror on a spinning platform and this way I can image both objects simultaneously within a negligible delay!

P.S: Not quite sure if this is the subreddit to post this, so please let me know if I kind get help elsewhere. Thanks!

Hi I am training a model to segment an image based on a provided point (point is separately encoded and added to image embedding). I have attached two examples of my problem, where the image is on the left with a red point, the ground truth mask is on the right, and the predicted mask is in the middle. White corresponds to the object selected by the red pointer, and my problem is the predicted mask is always fully white. I am using focal loss and dice loss. Any help would be appreciated!

I'm currently building a lidar annotation tool as a side project.
Hoping to get some feedback of what current tools lack at the moment and the features you would love to have?

The idea is to build a product solely focused on lidar specifically and really finesse the small details and features instead of going broad into all labeling services which many current products do.

I've been working on adapting Detectron2's mask_rcnn_R_50_FPN_3x model for fashion item segmentation. After training on a subset of 10,000 images from the DeepFashion2 dataset, here are my results:

• Overall AP: 25.254
• Final mask loss: 0.146
• Classification loss: 0.3427
• Total loss: 0.762

What I found particularly interesting was getting the model to recognize rare clothing categories that it previously couldn't detect at all. The AP scores for these categories went from 0 to positive values - still low, but definitely a progress.

Main challenges I've been tackling:

• Dealing with the class imbalance between common and rare clothing items
• Getting clean segmentation when garments overlap or layer
• Improving performance across all clothing types

This work is part of developing an MVP for fashion segmentation applications, and I'm curious to hear from others in the field:

• What approaches have worked for you when training models on similar challenging use-cases?
• Any techniques that helped with the rare category problem?
• How do you measure real-world usefulness beyond the technical metrics?

Would appreciate any insights or questions from those who've worked on similar problems! I can elaborate on the training methodology or category-specific performance metrics if there's interest.

or Consistent 3D Pose Estimation Pipelines That do Proper Foot and Back Detection?

Hey everyone!
I’m working on my thesis where I need accurate foot and back pose estimation. Most existing pipelines I’ve seen do 2D detection with COCO (or MPII) based models, then lift those 2D joints to 3D using Human3.6M. However, COCO doesn’t include proper foot or spine/back keypoints (beyond the ankle). Therefore the 2D keypoints are just "converted" with formulas into H36M’s format. Obviously, this just gives generic estimates for the feet since there are no toe/heel keypoints in COCO and almost nothing for the back.

Has anyone tried training a 2D keypoint detector directly on the H36M data (by projecting the 3D ground truth back into the image) so that the 2D detection would exactly match the H36M skeleton (including feet/back)? Or do you know of any 3D pose estimators that come with a native 2D detection step for those missing joints, instead of piggybacking on COCO?

I’m basically looking for:

1. A direct 2D+3D approach that includes foot and spine keypoints, without resorting to a standard COCO or MPII 2D model.
2. Whether there are known (public) solutions or code that already tackle this problem.
3. Any alternative “workarounds” you’ve tried—like combining multiple 2D detectors (e.g. one for feet, one for main body) or using different annotation sets and merging them.

If you’ve been in a similar situation or have any pointers, I’d love to hear how you solved it. Thanks in advance!

...

I recently came across some work on optimisers without having to set an LR schedule. I was wondering if people have similar tools or go to tricks at their disposal for fitting / fine tuning models with as little hyperparameter tuning as possible.

Hello everyone i am new to this computer vision. I am creating a system where the camera will detect things and show the text on the laptop. I am using yolo v10x which is quite accurate if anyone has an suggestion for more accuracy i am open to suggestions. But what i want rn is how tobtrain the model on more datasets i have downloaded some tree and other datasets i have the yolov10x.pt file can anyone help please.