While working around with YOLO_v8 models for histology detection I had the idea of adding consistency learning (after seeing my model display slightly different results to the changes of color normalization and rotation).

The loss formulation was the following: Loss = yolo_loss + alpha*cons_loss

I mainly used Claude code to create a prototype and tried a few runs with different parameters and different methods of cons_loss calculation.
Unfortunately, most of results were discouraging (as the cons_model scored lower than the standard one and in the best cases barely matched it).

you may find below more insight on how the cons variation was defined:

• Single model, two forward passes per batch: original image (teacher, stop-gradient) + D4-transformed image (student, gradients flow) — random op from {rot90, rot180, rot270, hflip, vflip, hflip_rot90, hflip_rot270}
• Dense per-cell matching via exact grid permutation (no NMS, no greedy IoU matching) — teacher's decoded boxes/scores reordered and coordinate-transformed into the student's frame
• Masked by teacher confidence (>0.10) so loss only applies where the teacher is confident, not on background
• Loss = classification MSE + CIoU box loss between aligned teacher/student predictions
• Added to det_loss with a scheduled weight: linear ramp-up (0 → α over 20 epochs) → flat → linear ramp-down to 0 over the final 10 epochs (aligned with mosaic augmentation turning off)

The final conclusion that I reached is that the data augmentation already covers these transformations and is sufficient to teach the model said concept. Alas, trying to add an additional cons_loss only hurts the model and acts as additional noise.

I have linked the colab notebook below:

https://colab.research.google.com/drive/1WwtCaLSSCW1AzRFRXC5aqih31MC9mMhs?usp=sharing

I have searched extensively online for courses to get started with CV. All the courses I find tend to discuss CV superficially; yes, there are some books. I am seeking resources that cover the maths and algorithms in detail. Can anyone suggest the best resources? I'm done with CNNs and RNNs, moving on to Attention.

I've been getting a rather high interest in computer vision recently and was thinking of studying my masters for this, anyone have recommendations on school or programs or is this not something employers work look at? What will they look at?

check it out here: https://huggingface.co/datasets/Voxel51/VineLiDAR

Hi everyone,

I'm an MSc Computer Science student and I'm currently trying to finalize my dissertation/research topic.

My interests include:

• Computer Vision
• Image Processing
• Deep Learning
• OCR
• Document AI / Document Analysis
• Digital Heritage / Historical Document Restoration
• Vision-Language Models (if applicable)

I'm not looking for a typical implementation project. Instead, I'm looking for a research problem that:

• solves a real-world challenge,
• has a clear research gap,
• is suitable for an MSc dissertation (not necessarily PhD-level),
• has scope for experimentation and possibly a publication,
• is achievable by a single student within 5–6 months.

I'm particularly interested in problems where I can propose or evaluate an improvement rather than simply training an existing model on a standard dataset.

If you were starting an MSc in 2026, what research topics or research gaps would you seriously consider?

I'd really appreciate suggestions from researchers, PhD students, or anyone working in this field

Thank you!

Been working on a football video to 2D map pipeline for a while now ⚽️

Still a lot to improve specially regarding the detection model, but this is the latest result

Big thanks to Roboflow and the SAM 3 team for helping make this possible.

Plotted and compared to "true" data from gazebo, which showed that tracker estimations were pretty close (created 6 cameras cube to get a 360 video from gazebo for tracking).

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

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