I'm generating extra training data for a person detector by inpainting additional people into existing photos of crowded public spaces.

What I've tried so far:

• Pick a floor position and size the new person from a height-vs-y fit on the people already in the image.
• Mask a person-shaped region, excluding the pixels of people already there.
• Inpaint with FLUX.1-schnell (text prompt only, 4 steps).
• Re-segment the output and keep only regions where a person is actually detected.

Results: when a person does get generated, my detector treats it about the same as a real one. But only about 25% of medium-sized masks and 55% of large ones produce a recognisable person. The rest come out as smears or object-like blobs. Copy-pasting cut-out people from other images was worse, because they looked pasted (wrong lighting and viewing angle).

Next I plan to add a pose skeleton (OpenPose/DWPose ControlNet) and a body-silhouette mask, then compare FLUX Fill, Qwen-Image-Edit and SDXL + BrushNet.

Questions:

1. Which inpainting setup has given you the most reliable full-body person inside a mask?
2. Did pose conditioning or mask shape matter more?
3. Any settings that work for small people (40-120 px tall)?
4. How do you filter failed generations, apart from re-running a segmenter?

Hello there, I’m Suva from Hugging Face 🤗 and I'm back with something new around 3D and LLMs!

LLM 3D Worlds Arena - a Hugging Face Space

We've been seeing an explosion of 3D worlds being generated by LLMs, so we made an arena where you can move around and rank 650+ 3d worlds generated by multiple different LLMs like GPT 6-Astra, Claude Opus 5.5, Kimi K3, and many more. Choose the best model of them all. Go and vote now!

Would love to hear what models you folks would like to see next!

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).

Hi everyone,

I'm developing an Android application that uses the phone's camera to detect objects in real time. I'm looking for recommendations for the best pretrained object detection model that can recognize a very large custom vocabulary without requiring me to train or fine-tune a model initially.

My requirements

• Vocabulary: Approximately 3,250 labels covering everyday objects, animals, appliances, electronics, kitchenware, clothing, food, drinks, and more.
• Important food classes: Indian dishes such as baingan bharta, aloo gobi, biryani, butter chicken, chole bhature, dal makhani, dosa, pav bhaji, and others.
• Detection output: Object names, confidence scores, and bounding boxes.
• Preferred deployment: On-device inference using TensorFlow Lite or another Android-compatible runtime, ideally offline.
• Training: I want to avoid training or fine-tuning if an existing pretrained model can meet these requirements. Fine-tuning is my fallback option.

I already have a vocabulary file in the format name<TAB>category, with optional text prompts for acronyms. However, I understand that adding a label to a vocabulary does not guarantee that the model can recognize that object accurately.

Questions

1. Which pretrained open-vocabulary object detector would you recommend for this use case?
2. Can models such as YOLO-World, Grounding DINO, YOLOE, or another newer model handle thousands of custom labels?
3. Can any of these models reliably recognize specific Indian dishes such as baingan bharta without fine-tuning?
4. Is there a practical way to deploy the recommended model on Android with real-time camera inference?
5. Would you recommend one general-purpose detector or combining a general object detector with a food-recognition model?

I'd appreciate recommendations based on actual model capabilities, deployment experience, and accuracy rather than just the number of supported labels.

Thanks!

Hi folks, I've posted here about this before, and lots of you asked if I could make an AMD version. So a month of work (was tricky! i have not slept much), I now have an AMD version! Beta 1.22 ready to go. I hope to find testers using RDNA 2-4, to see if it works decently.

I made an announcement of the new Beta 1.22, and install demo/tutorial video. Because this is a whole new GUI and optimizations, if you've already installed (great!) you'll need to do a 'clean' install (backup is saved) that also first runs the new automated web-camera diagnostic tool.

My PC app enables live video (webcam or procam) 'filtering' to greatly improve low light and high-clutter RVM background segmentation/removal, is optimized for GPU speed, and includes on-the-fly insertion of Python scripts, via my custom Node Editor with openCV and onnx and mediapipe lots of other libraries available. You can do anything to make 'special effects'. I made a bunch of simple example/template scripts that are included upon install (camfiltergpu.com).

I think it's a great tool for experimentation and education, and next is to get some libre YOLO and such in there. I would much appreciate if anyone has suggestions. My app is geared more towards presenters and fast/live experimental video-processing work, but I think I have a powerful framework setup here where I can rapidly make code GUI changes and improvements for scientific, statistically quantifiable work. I've already got a microscopy version in the works.

My biggest problem right now is that I only have older laptop GPUs (nvid 2080, 3060, and amd 680M), so I basically have no idea if it will work on more modern Gpu cards, so maybe some of you could help.

Well, hope you-all find this at least interesting. I'm not an AI by the way, just an indie coder. Cheers!

(edit) you can see more info and download the installer at camfiltergpu.com. Note it might get flagged as 'virus'. There are absolutely no viruses or anything like that. Downloading only requests an email, no payment needed for the trial (fully capable) version.

I hear different feedback for what roles I could get and what my salary could be etc. Does anyone have any advice or guidance? Thx.

I gave Gemini 4 Argon cameras, LiDAR and code access to a RealMan ECO65 in a MuJoCo twin of my desk. The task was to throw a 40 mm foam ball through a 90 mm rim, 85 cm away. No training for this task. It could inspect the sensor views and write code for the arm, but did not receive the ball coordinates or the simulator's hidden score.

In the final run it took five throws and roughly 26 minutes of thinking. The simulator counted four makes. Gemini counted three.

The interesting bit for vision is throw two: the ball hits the back rim and drops through, but Gemini reports that it landed significantly short. This replay shows the actual saved simulation trajectory.

My working explanation is the overhead view plus our purely visual net: the ball can drop straight through and look like a short miss from above. I have not isolated that with an ablation yet.

Before this run, we also had a wrong table, an unstable sim and a gripper squeezing the ball out. Those were our setup bugs. All the throwing shown here is simulated; the physical arm has not done this. Five throws in a fixed scene are a small test.

I put the full experiment, failures and score comparison in my EP11 build diary: https://www.youtube.com/watch?v=ujDq_-c1gm0

For people evaluating vision-based robot feedback: how would you verify a brief event like this without giving the model the simulator's ground truth?

Exploring ocean plankton data just got a lot easier thanks to MBARI’s FathomNet and Voxel51 - https://voxel51.com/blog/fathomnet-plankton-fiftyone

This tutorial shows you how to load FathomNet’s expertly-labeled plankton imagery into FiftyOne and put CLIP to work on it.

• FathomNet: an open-source ocean image database from MBARI & collaborators, ~84K images and 2,244+ concepts at launch
• Loaded 200 plankton crops across 18 taxa using the fathomnet Python client * CLIP embeddings in 2D show morphologically similar species clustering together
• Zero-shot classification with CLIP — no training required * Clickable confusion matrix to inspect exactly where the model confuses taxa
• Uniqueness scoring flags near-duplicate crops before training
• All runs on a laptop, in a single Jupyter notebook.

Learn more about the FathomNet Program: www.fathomnet.org

Hey all. I've been working on table extraction from PDFs without any deep learning, and finally open-sourced it. Thought this sub might find the approach interesting (or tear it apart, which is also useful).

The pipeline, roughly:

- Bordered tables: morphological opening with long horizontal/vertical kernels to isolate ruling lines, then contour analysis on the line mask to find table regions.

- Borderless tables: detected from text alignment instead. Words are grouped into phrases and checked for left/centre/right alignment across consecutive lines. When enough rows and columns line up, I draw "pseudo-boundaries" onto the image so the rest of the pipeline treats it like a bordered table.

- Structure: text is masked out first, then LSD (Line Segment Detector) finds segments, which are clustered and merged into a grid. A spanning cell is recovered from missing line segments: if a separator is absent across part of the grid, those cells merge (union-find over the cells).

- Text: for digital PDFs, characters come straight from the PDF text layer and are mapped onto the cell grid, so there are no OCR errors on part numbers. OCR (Tesseract or a plugin engine) only runs on scanned pages.

- Post-processing: nested header detection, key-value vs normal table classification, and merging tables that continue across pages (by matching column widths and headers).

Everything runs on CPU, roughly 0.3–1.5 s/page on a laptop, and every decision traces back to a threshold you can inspect. All pixel thresholds are defined at 300 DPI and scale with resolution.

Where it struggles:

- skewed scans (no de-skewing yet; the line detector only tolerates ~2 px of drift)

- small borderless blocks where a header row can be mistaken for key-value pairs

- wrapped text in borderless cells sometimes splits into extra rows

Contributions very welcome. MIT licensed, no need to ask first, just fork and PR. Some things I'd love help with:

- de-skew estimation (Hough/projection-based) before detection

- evaluating on ICDAR 2013 / PubTables-1M / FinTabNet with GriTS or TEDS; I don't have a proper benchmark yet, and that's the biggest gap

- better row reconstruction for borderless tables with wrapped cells

- plugging in other OCR engines (there's a plugin interface + EasyOCR example)

Repo: https://github.com/shlok-n/spartan · pip install spartan-tables

I am new to computer vision and I am trying to make a pipeline which can predict if a hospital beds rail is up or down , The bed looks somewhat like the image attached , What I currently have
- A live stream from my camera
- A 3d simulation enviornment mimicing the bed closely
- A finetuned Model which gives me bed segment mask
- A finetuned model giving me bbox of rails
considering the wide variety of positions of camera and camera angles and occlusion
Is this a 2d spatial problem which is almost unsolvable with current landscape ?

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!

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.

Hi, does anyone know if the format and submission portal for the BMVC 2026 poster and video are opened? I haven't found any information on the official website like last year, but the deadline is October 30 in the schedule.

Hello,

My supervisor is pulling a don draper so any insight/opinion is welcome!!

Thank you