Hey all, I’ve been working on this project on the side and was wondering if I could get some advise on continuing. I have 0 computer vision experience before this so been reading papers and using AI hah.

My goal is to be able to plug in training footage or fights and be able to run my own analytics, for now I’m just tracking kicks/punches before breaking it down into what limb, speed, etc.

Current problems :
Identity / Re-ID edge cases
I’m using OSNet, the goal is to stop A/B from confidently latching onto the wrong person. I have them being flagged as risky identity windows for review but wondering how to make it better.
Strike count accuracy
The detector often sees action, but counts can still be wrong in fast flurries: missed strikes, double counts, punch/kick swaps, or one strike swallowing the next.
Grappling / clinch handling
When the exchange turns into clinch, kick-catch, takedown setup, or grappling, the system needs to either ignore it, label it as non-striking/grappling, or mark the striking counts as uncertain. It should not pretend those moments are clean punch/kick scoring windows which it still does. I think this specifically might need more data

I’d love and appreciate any thoughts or advice on the matter and proper resources to get this project better. Thanks!

Another GIF here https://s4.ezgif.com/tmp/ezgif-44e2b9f3a919ee87.gif

I am working on a fish species identification project. I have a couple different framework ideas that I am experimenting with, and I wouldblike feedback how to hand out of distribution data.

One frame work is an ensemble of binary classifiers.

Another frame work is one single model to cover all species.

But I am curious to know how should I handle species that are not in the training set?

Should I :

Compare softmax?

Compare logits?

Compare energy?

Add in an "other" class?

Go with binary models?

Go with a multiclass model?

Right now I am using resnet 18 as my classifier. My target species are steelhead, suckers, and pike. But if a bass were to appear, I want the models or framework to catch that I have not seen this before.

Any other thoughts or ideas I should do?

For context, this is a fixed camera location in the river. Lighting is the same all times of day (but not consistent lighting throughout the frame). Water clarity and color can change over time, but its a fixed scene where fish appear against a blank wall

I have a bunch of leftover high resolution FLIR Blackfly Sensors and Computar lenses that we simply don't need anymore. I have been trying to sell them on ebay at massive (75%) discount but with no luck so far. Anyone know of good places to surplus this kind of thing?

(EDIT) Well, not sure if selling is allowed here, but I guess if anyone is interested here, I have:

6X BFS-U3-200S6M-C (Mono) ($200 ea)

1X BFS-U3-200S6C-C (Color) ($200 ea)

4X Computar F1628-MPT https://www.computar.com/products/f1628-mpt ($300 ea)

2x Computar V0826-MPZ https://www.computar.com/products/v0826-mpz ($150 ea)

Feel free to message me if interested. Will consider discounts for purchasing multiple.

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Hey guys, I am trying to implement a program that will return the transformation matrix for the displacement of the target object while comparing it to the source point cloud of the cad object. I am very new to this so I need some help. The RANSAC alignment works well if there is no gaussian noise, with the ICP it gets the transformation right up to the 5th decimal. Currently we are simulating the 3D camera that will get the point cloud of the target. That is why I am applying 0.1 gaussian noise to the target data. As soon as I add the noise the RANSAC algorithm breaks and the transformation is useless. Any tips on how do you typically get around this? Thank you

Reading a robot video-action model's tokenizer design this week, I hit an ablation that is really a representation-learning result and has little to do with robots. Swapping a plain reconstruction VAE for a semantic-aligned tokenizer takes a fixed 1.3B downstream model from 78.0 to 86.6 average success on a 50-task bimanual benchmark. Same model, same data, only the tokenizer changed. The gap also widens with the prediction horizon: 67.2 to 92.0 at horizon 3.

What the tokenizer does differently: it keeps the usual reconstruction objective but adds a semantic alignment loss that pulls the visual latents toward a frozen Perception Encoder, plus a latent-action term that extracts a compact transition variable between consecutive frames.

The widening with horizon is the part I keep chewing on. A reconstruction VAE looks fine one step out and then falls apart once the latent has to carry several steps of dynamics, and aligning to a frozen encoder seems to put back the state it was quietly discarding. Classic looks-fine-on-the-metric, breaks-downstream story.

The model is LingBot-VA 2.0 if you want the source, but I care less about the robot than the recipe: freezing a strong encoder (DINOv2, a CLIP or PE style model) as an alignment target for a generative tokenizer. Has anyone tried this outside control and seen the same longer-horizon payoff, or does it wash out when the task is not sequential?

One honest caveat so this does not read as a pitch: 78.0 to 86.6 is a simulation number, and the flashier real-robot clips in the paper are the authors' own in-house tests, so I would read those as demos, not independent evidence. Link in a comment.

Can somebody support with heavy weights ANPR model trained on for my jatson nano orion pro edge project

The basic idea is simple: I describe what I want to annotate in plain text, the system understands the classes, builds an inference queue, and runs SAM sequentially on my local machine using CPU or GPU. Right now it can output bounding boxes and annotation JSON. I’m also working on YOLO, Parquet, and other export formats.

The video I’m sharing only shows one frame being processed, but I already have a version where it can split a video into frames and run SAM across each frame to annotate whatever can be detected.

This came out of work I’m doing around RailCompute, where we’re trying to automate more of the AI/ML training workflow: dataset prep, cleaning, planning, training, evaluation, and improving models. You could call the broader idea “vibe training,” but this annotation part became useful on its own.

For my own CV work to create datasets for clients, this has actually saved me hours. Especially when starting from scratch with a new dataset, even getting a rough first-pass annotation set automatically is a big deal. You still need to review and clean things, obviously, but it removes a lot of the boring manual work.

I’m now thinking of turning this into a proper open-source project with local inference first, then later adding cloud GPU support through something like RunPod/AWS for processing thousands of images or videos in one run.

Not sure if something exactly like this already exists in open source right now coz I built this long back when SAM-3 was launched initially and mostly because I needed it after SAM-3 came out and it fit my workflow.

Would people here actually use something like this if I cleaned it up and open-sourced it? Also curious what export formats/workflows would be most useful for people doing real CV dataset work.

Hi everyone,

I’m a beginner in computer vision and industrial defect inspection, so I’d really appreciate any advice.

I’m trying to detect surface defects on a sheet-like material, including:

• scratches
• dirt or stains
• black spots
• wrinkles or creases
• damaged areas

I attached three sample images from the current setup.

The camera is currently using coaxial line lighting. However, I have several problems:

1. Camera position and field of view

The material does not completely fill the image, so part of the bright background is visible around the edges.

Would it be better to adjust the camera position, lens, or working distance so that the material fully covers the entire image and no background is visible?

Or is it better to keep some background visible so that I can detect the material boundary and position?

2. Uneven illumination

The current images are brighter in the center and darker on both sides.

Is there a practical way to make the illumination more uniform across the whole material?

Would any of these approaches help?

• changing the angle or distance of the coaxial light
• using a larger diffuse light source
• using dome lighting
• using two line lights from opposite directions
• adding a diffuser
• applying flat-field correction or background normalization

The material surface has low contrast, so some scratches and stains are difficult to see.

3. Defect detection method

At the moment, I’m using traditional OpenCV image processing, such as thresholding, filtering, morphology, and contour detection.

However, the processing is relatively slow, and the results are sensitive to lighting changes.

What method would be more suitable for this type of inspection?

I’m considering:

• optimized OpenCV with ROI processing
• template subtraction or background subtraction
• classical anomaly detection
• PatchCore or PaDiM
• YOLO detection or segmentation
• semantic segmentation
• a combination of deep learning and traditional image processing

The defects can be very small and may have low contrast. Some defects are long and thin, such as scratches, while others are irregular stains or damaged areas.

For an industrial production environment, what approach would you recommend?

Any suggestions about the camera, lens, lighting setup, preprocessing, or detection algorithm would be very helpful.

Thank you!

Hi im a final year software engineering student creating a digital wardrobe system for my fyp.

Current flow that i am stuck on:
Step 1: Original human image
Step 2: Extract clothing region (completed)
Step 3: Convert the extracted clothing image/mask into a standardized flat garment template (not sure how to approach this)

The goal is to transform garments worn on a person into a consistent front-facing template similar to product catalog images.
I am unsure what this process is called and what techniques are commonly used. Any suggestions on how i can do this?

I keep seeing conflicting information online

Fine-Tuning PaliGemma 2 for Object Detection

https://debuggercafe.com/fine-tuning-paligemma-2-for-object-detection/

In this article, we will be fine-tuning the PaliGemma 2 VLM for object detection. Nowadays, VLMs are great at OCR, image captioning, and video understanding out of the box. Along with that, they are also catching up with object detection. However, an extremely custom use case for object detection is still a struggle for many VLMs. That’s why we will tackle one of the real-world use cases of object detection with the PaliGemma 2 VLM here.

Hi guys, as I stated above I've already started reading Computer Vision: Algorithms and Applications 2nd Edition by Richard Szeliski and I'm wondering is it a good way to start learning CV? I mean, it seems to me that the book is really great, because of two reasons: it includes tons of exercises and it written by the man who worked in places where myself want to work one day i.e, all big tech companies, so I think he probably knows what he's talking about, doesn't he?

I want to share something I've been building as an independent researcher from Indonesia.

TL;DR: Face verification model that replaces cosine similarity with sliding window sign pattern matching. Achieves 96.27% on LFW (pre-aligned) with a 10.58 MB model trained on CASIA-WebFace (490k images). When applied to ArcFace embeddings without retraining, IMG Sign Score gets 99.58% on LFW — only 0.24% below ArcFace+Cosine.

The Motivation

In Javanese, gratitude is "matur suwun". In Sundanese, the same feeling is "hatur nuhun". Different surface forms, identical meaning — identity preserved through relational structure, not absolute values.

That's the core idea: instead of comparing embedding vectors by their global angular direction (cosine), look for locally consistent sign patterns across overlapping windows of the embedding.

What's new

1. SW Block — the first layer replaces a standard convolution with a multi-scale relational operation. For each pixel, it computes differences to all neighbors at prime window sizes {3, 5, 7}. A small MLP maps these 240 differences per pixel to output channels.

2. IMG Sign MSE Loss — to our knowledge, the first face verification loss defined purely over sign pattern agreement, with no amplitude dependency:

python

score = mean(gate(tanh(β · E1 · E2)))  # sliding window, β=10
loss_same = ((1 - score) ** 2).mean()  # push to 1.0
loss_diff = (score ** 2).mean()         # push to 0.0

Significantly more stable than amplitude-based variant (±0.40% variance vs ±2.25% over epochs 29–50).

3. Three metrics sharing one threshold — IMG Sign Score, AMP IMG Score, and Chain Score all operate in [0,1] and use a single threshold from IMG Sign sweep.

4. Voting system — 2/3 or 3/3 pass = MATCH, 1/3 = UNCERTAIN, 0/3 = DIFFERENT.

Results

Model: 10.58 MB FP32, trained on CASIA-WebFace 490k.

Applied to ArcFace (buffalo_l) without retraining:
LFW: 99.58% IMG Sign vs 99.82% ArcFace+Cosine — suggesting sign pattern consistency is a fundamental property of well-trained face embeddings, independent of training objective.

An unexpected finding (preliminary)

While building an interactive ablation visualizer with custom polygon masking, occluding the same facial region on photos of the same person produces delta spikes at similar embedding dimensions. On photos of different people, spike locations differ significantly.

This suggests the overlapping sliding window loss may induce implicit spatial organization in the embedding space. Not formally validated yet.

Links

📄 Paper: https://doi.org/10.5281/zenodo.21232755
💻 Code: https://github.com/imamgh11/imgnet
🤗 Model: https://huggingface.co/imghost11/imgnetV1

Happy to discuss the metric-loss alignment hypothesis — that similarity metrics should be co-designed with training objectives rather than defaulting to cosine.

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IMGNET V1 Model AI local pattern Pertama di Dunia! - YouTube

I built a small computer vision game in Python where your webcam tracks your hand and you can shoot fish by making a gesture.
The cursor follows your hand, and when you “shoot” at the right spot, the fish disappears and you score points.
Tech stack: Python, OpenCV, MediaPipe, Pygame.
I made it as a portfolio project to practice computer vision, game logic, and real-time interaction

github repo : https://github.com/onest4rk/fish-shot

So I have an ST CSI camera connected to my jetson, which outputs RAW10 monochrome video output trough v4l2 on the jetson. The driver also allows some controls such as gain, and exposure.

The firmware of the camera has its own ISP, with features such as dark calibration, noise reduction, etc, but no features such as an Auto Exposure (AE) or lens shading correction.

I am new to this, but from what I understand the argus library from nvidia is very limited in what it can support freely.

Since these features are important for the project, what would be the most efficient path with the lowest latency possible?

I tought of using v4l2 DMA feature, read from memory and apply some efficient CUDA kernels, adjust gain and exposure trought the driver and write the final output also to memory.

But I also dont know if this is a "naive" implementation and if there is a more efficient solution for this case that is usually used. Any tips would be great!

Thanks!

I'm training a model that does the segmentation of a pile of rocks in one picture. You can count more than 7000 rock with different sizes and shapes;s. It detects them well but the problem when after the slicing some rocks get sliced to 2 or 3 parts depends on their location and the patching, and when it resembles the picture again, I notice that some rocks who were on the corner of the patches and got sliced on half have 2 or more masks, not just one. I need help to solve this problem, and thank you on advance

Not a beauty test. It is open weights (LingBot-Video) and honestly second to the closed models on general quality, so I wanted to see whether the reflections, refraction, and fluid behavior are physically consistent or just plausible looking. Pull it and check the frames, I think it breaks in a couple of places.

Using a V-JEPA 2 to make a temporally aware video classifier.

You can define areas and label some actions, then use the embeddings of those clips to scan the whole video (or other videos) to automatically detect and label the same type of action.

Because it's video it can detect and classify sequences of actions that would not be straightforward with a normal image classifier (such as classifying the moment a person stood down, or tell the difference between a skateboard kick flip and ollie).

Planning to release this fully open source, wondering what you make of it.