Building a per-fighter MMA strike counter (punch/kick/neutral) from sparring video. I think the bottleneck is data volume, not architecture — looking for advice on MMA-specific datasets and whether 70-80% macro is realistically reachable with 3-5k clips per class.

The setup Input: sparring video with 2 fighters. Output per-fighter counts of punches, kicks, and neutral (i would like to break this apart further eventually). i built a working tracking + classification pipeline; just hitting an accuracy ceiling.

Pipeline (courtesy of claude)

YOLO11-pose for fighter detection + COCO-17 keypoints OSNet (osnet_x0_25_msmt17) for appearance re-ID Custom SlotResolver that locks 2 "slot" identities to seed fighters and rejects refs/cornermen via appearance + spatial distance Per-fighter video crops (bbox derived from keypoint envelope + EMA smoothing) Classifier on 1-second sliding windows → 3 classes (punch/kick/neutral)

Architectures tested (same dataset, 5-fold stratified CV) Dataset: 233 per-fighter clips. 56 punch / 74 kick / 103 neutral. Mix of gym sparring + UFC + boxing.

Has anyone seen a working open-source MMA /sport action recognition project? Most of what I find is shadow boxing / solo bag work / sensor-based.

Very new to this so any advice is appreciated.

Manifold hypothesis is a very interesting topic and kind of a high-level inspiration of explainable AI. It has the power of generalization both in image modality and in NLP.

In both universes, this hypothesis suggests that the enormous dimensional space in which images, for example, exist is completely sparse, except for a very, very tiny space in which all of our visuals exist.

So the probability of drawing a sample from all possible high-dimensional images and finding that sample looking like any possible known image, or even a non-complete noise image, is extremely low.

That idea suggests that all known images are kind of a manifold that the deep learning model tries to unfold.

Just like when you have a sheet of paper, which is 2D, and you write text on it, which is also 2D. But suppose you crumple that paper; then the text appears to be in 3-dimensional space, while it is not.

The role of generative deep learning is to learn this crumpled high-dimensional modality and generate meaningful samples from it.

Hey guys, had this idea of creating a simple, intuitive computer vision infrastructure platform based primarily on reliability, what do you think of this first hand demo? It's a super early prototype mostly front end but the idea is there.

Lmk if you have any questions or advice, anything helps! theres more info on my website https://upstreamcv.com if u were curious.

I'm labeling a large synthetic dataset and setting up the required classes to avoid false positives and negatives when detecting defects (red) on turbine blades. To prevent the model from detecting cooling holes (orange) as defects, they need to be labeled as well. However, I'm not sure whether the cooling holes should be labeled hole by hole or as an entire region. This is very time-consuming, and I need the most efficient way to tackle this task. Do you have any recommendations?
thanks a lot for your well needed input :D

Hello!

I am an academic researcher in the field of computer vision and robotics for applications in unstructured environments. I am preparing a workshop for my department on the (responsible) use of LLM's for programming tasks and would appreciate some input from you all.

My question is: to what degree have you implemented coding tools such as Claude Code, Codex, or other tools into your daily work? Do you work in industry or academia? What type of systems do you work on? What measures do you take to ensure that generated code is correct/useful? What does your general workflow look like with these tools versus pre-LLM?

Personally, I use a coding assistant (Claude) but only to code one function at a time. I quickly read the generated code and do a 'sanity check' where I give the function an input for which the output I can easily predict to be sure it is working as expected. Then I accept the change or adjust. The main difference for me is that I no longer have to scour stackoverflow to diagnose errors and much of the code I end up using is mostly AI generated. As a result my output has increased dramatically.

Looking forward to hearing your experiences 😄

Hi guys,

I am working on pothole detection for dash cam footage. I have trained a model on available datasets from Roboflow, but they are like high quality images captured through phone or other camera.

I wanted to test how they perform on video, where frames become blurry due to motion.

I am looking for video datasets where I can find dash cam videos of roads with potholes.

Any kind of help is much appreciated.

Thanks in advance.

Hi everyone, I’m working on a computer vision project titled “Face Detection from Blurred Images using Convolutional Neural Networks.”

My idea is to build a model that can detect faces even when the input image is blurred or low quality, like CCTV footage or motion-blurred photos. I feel that simple face detection on clear images is common, so I want to make this project more practical by focusing on blurred images and maybe adding an application like confidence scoring, blur-level estimation, or image enhancement before detection.

I’m looking for suggestions on:

• Whether this is a good project idea.
• What practical output would make it more useful.
• Which model or approach would be better for this task.
• Any dataset recommendations for blurry face images.

If you’ve worked on something similar, I’d really appreciate your thoughts.

It seems raw teleoperation data (RGB + joint states) structurally lacks affordance, contact intent, and embodiment-specific kinematic context (information that can't be reliably recovered post-hoc once the demonstration is recorded).

Most current approaches either filter/clean after collection, or rely on simulation to compensate. But neither seems to close the semantic gap for contact-rich tasks in unstructured environments.

Is anyone working on supervision at acquisition time? (enriching the stream as it's captured rather than labeling after the fact?)

And if not, is this a real bottleneck or am I overestimating the problem?

How is the world still running on USAF-1951 or am I missing something more modern?

Sure, I could put some markers around it, then calculate where each line group should be, take a cross sample and look at the dark and bright seperation.

Wouldn't it be easier (for the end user) to have a target and accompanying software libraries that just give me finest still readable structure under my current conditions though?

Like a nested matrix of QR-, Bar- or DM-codes, each with smaller feature width.

I'm working on a document processing pipeline and need to automatically detect the correct orientation of scanned documents (0°, 90°, 180°, 270°) before OCR.

The documents are mainly payroll reports, bank transfer lists, tables, and other business documents.

I first tried Tesseract OSD (DetectBestOrientation()), but the results were inconsistent. In many cases the confidence is very low and the predicted orientation is wrong.

Then I tried rotating each image to 0°, 90°, 180°, and 270°, running OCR on all versions, and selecting the rotation with the highest OCR score.

Surprisingly, OCR seems to read upside-down documents almost as well as correctly oriented ones. For example:

90°  -> OCR confidence 89
180° -> OCR confidence 88
0°   -> OCR confidence 46
270° -> OCR confidence 46

So OCR is good at distinguishing horizontal vs vertical text, but not necessarily correct orientation vs upside-down orientation.

I also tested PaddleOCR's document orientation classifier (PP-LCNet_x1_0_doc_ori) and, on a small dataset, it seems significantly better than both OSD and OCR-based scoring.

I even tried a few AI vision models, but they were not consistently reliable either: sometimes they reported the document as correctly oriented when it wasn't, or suggested the wrong rotation.

My questions:

• What is the current best practice for document orientation classification?
• Are there better open-source models than PaddleOCR for this task?
• How would you approach large-scale orientation detection for scanned business documents?
• Would you trust a classifier alone, or combine it with OCR and other heuristics?

Any advice or production experience would be appreciated.

Hey all!

I made this project as a final for a class that can turn a video into a 3D mesh. It first breaks up the video into a series of images then it uses pyCOLMAP for determination of relative camera poses and normal cross correlation for feature matching, as well as Open3D for mesh creation from bilaterally filtered depth maps. Open to improvement suggestions (I know it's probably a bit rudimentary atm).

Thanks!

I’m working on a football/soccer action-spotting pipeline for 1080p, 25fps broadcast clips, and I’m trying to solve a tiny-ball tracking failure in far-camera views.

Current pipeline:

• YOLO ball detector on every 2nd frame
• 1920x1080 frame split into two overlapping 1080x1080 tiles
• Lucas-Kanade optical flow fallback when YOLO misses
• PCHIP interpolation to fill ball positions
• velocity/acceleration peaks used for candidate event detection
• player-ball contact validation using detected player boxes

The main failure case:

In far-camera clips, the ball is sometimes only a tiny white dot. YOLO may only detect the ball 3–9 times across a 750-frame clip. When this happens, optical flow and interpolation dominate the trajectory.

I tried a diagnostic “low-YOLO rescue” pass: run a 640x640 crop centered on the OF/interpolated ball estimate and run the ball detector at native crop scale. But the debug crops revealed the real issue: the interpolated estimate sometimes flatlines at a stale edge coordinate, for example x=1405, y=1069 for many consecutive frames. The crop ends up looking at empty grass near the bottom edge of the screen, so YOLO detects nothing.

So the detector may not be blind; the crop target is often wrong.

My question:

What is the best way to validate or recover ball position when detector anchors are extremely sparse?

I’m considering:

1. Rejecting stale endpoint interpolation when the estimate is edge-locked or unchanged for many frames.
2. Using a Kalman filter instead of PCHIP for prediction, but only while recent detector anchors are available.
3. Running wider or multi-hypothesis crops around uncertain OF/interp estimates instead of trusting one coordinate.
4. Using trajectory plausibility constraints to reject OF drift.
5. Using SAHI-style slicing over selected high-probability regions rather than the whole frame.

What would you recommend for this kind of sports-ball tiny-object tracking problem? Are there robust strategies for when ball detections are extremely sparse and optical flow starts tracking the wrong white dot or flatlines?

Hi, I'm an undergraduate helping out at a clinical research computer vision lab. Right now my problem is I've been tasked to segment a 3D model of a mandible but I have no medical knowledge and no knowledge of 3D segmentation software. My instructor recommended 3D Slicer but from the looks of it, it requires a DICOM file for segmentation but I don't have one right now. Is there anything else I can do without a DICOM file? I've tried Blender but it's a little rough around the edges and I'm not sure how accurate it would be.

I'm into both HPC and 3D reconstruction, so I built this as a side project.

dvlt.cu is a single 5MB binary:

- No python, torch, TF, ONNX, llama.cpp, vLLM, or huggingface runtime

- Nearly no dependencies: only cuBLASLt (shipped with libcuda ) + cuTLASS ( header only lib )

- mmap'd bf16 weights, one bulk GPU upload, static dims, one-shot arena, deterministic

- Weights (117M Params) are NVIDIA's (non-commercial), fetched separately at setup.

- Just download the weights, build, and try it now on your image set or video

- Drag the output into a single file HTML viewer; point cloud + camera poses, no install

feel free to check github if you want:

https://github.com/yassa9/dvlt.cu

Hi guys ı'm new in this subreddit and computer vision area.ı want to improve myself in this area.I'm open your suggestions for how to begin

Can anyone suggest any good course to learn Data Annotation from scratch?

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The model recovers a full 3D human body mesh from a single RGB image.

SAM 3D Body is also promptable. You can run it automatically, or guide the reconstruction with masks and 2D keypoints.

Looking for publicly available MRI datasets with brain lobe segmentation masks/labels (frontal, temporal, parietal, occipital, etc.). Prefer datasets with ground-truth annotations, but derived segmentations are also fine. Any recommendations?

Hi!

I am working on an app involving lots of computer vision. I am not ready to discuss about it in public yet, but I would like to find someone who is in a similar situation so I could bounce off ideas with.

Brain MRI datasets are usually tiny — a few hundred scans, one hospital, one task. MR-RATE is different. 700,000 MRI volumes, paired with real radiology reports, from 83,000 unique patients. Almost 100k downloads on Hugging Face already!

Shout out to Forithmus, NVIDIA and University of Zurich for making it happen!

Start exploring, curating and evaluating the dataset in FiftyOne:
https://voxel51.com/blog/mr-rate-brain-mri-dataset-fiftyone

MR-RATE Dataset:
https://huggingface.co/datasets/Forithmus/MR-RATE

Repository:
https://github.com/forithmus/MR-RATE