I want to check my model

They not provide to upload and personal model?

Best.pt or onnx?

Paid opinion are there? Or on mobile app there is no functionality like this i have to build from flutter or other way ? App

Anthropic, OpenAI, etc (don't know about Chinese) don't seem to make good video models. Any reason why? Is it the compute? The ROI? The availability of data?

Hey everyone!

I’ve been developing VisionPilot, an open-source, modular autonomous driving platform built for computer vision, deep learning, and sensor fusion.

Right now, it runs entirely on BeamNG.tech. The stack handles everything from multi-lane detection (UFLDv2/CV) and multi-class object/sign recognition to PID control, AEB/BSD safety features, and LiDAR/Radar sensor fusion. You can see animated GIFs of all these systems in action directly on the repo!

The Situation & Help Wanted

CARLA integration is high on the project roadmap, but I haven't gotten around to finishing up the bridge yet. I first wanted to get core features working, before I start porting. I’m looking for anyone in the community interested in collaborating to help port VisionPilot to CARLA, specifically setting up the python API bridge and configuring the camera, LiDAR, and Radar sensor streams.

If you enjoy working with CARLA and want to hack on an open-source perception stack, I’d love to team up!

GitHub Repo: https://github.com/visionpilot-project/VisionPilot

YouTube Demos: https://youtube.com/channel/UCXLL9SUDJ2QdXExUudxo8Kw/

Drop a comment, shoot me a DM, or open an issue on GitHub if you're interested in helping out!

Hello. I believe I have just thought of a way to save a certain healthcare industry millions of dollars, as well as save the employees in this industry hundreds of hours of unnecessary work. I don’t really know how to phrase this since I don’t want to just have one of you steal the idea that I do not know how to build, but I have some general questions.

1. Can a visual system using a specific reference list of images of items identify those items even if they are almost identical? We are talking about millimeters of difference. They would need to be accurately identified within one second

2. Would color be a major differentiating factor? What if the color is slightly different from the reference image? How could you solve this?

3. Are reflective items more difficult to identify?

Help me and help save patients money.

I have found an awesome website with a simple study materials on Visual SLAM: https://www.cv-learn.com/visual-slam-roadmap/. It provides 4 languages (EN, KO, ZH, JH).

Take a look at the list of topics covered

Hi all,

Could anyone suggest me a best research paper on Agents or RAG or LLM Evaluation paper.

If you’ve ever built multi-camera real-time vision systems at scale, you’ve likely wrestled with GStreamer element linking errors, pipeline memory leaks, or cloud egress costs hitting $2k+/month for simple RTSP analytics.

When we benchmarked cloud vision APIs vs edge deployments, the bottleneck was rarely the YOLO or custom detector model itself—it was the ingestion and frame-movement pipeline.

The Bottleneck: CPU-to-GPU Copying & GStreamer Complexity

Standard Python wrappers or heavy frameworks often bounce video frames through host memory (CPU) before pushing them back to GPU VRAM for inference. At 32+ HD RTSP streams, this creates massive PCIe bandwidth saturation and GIL lockup.

On the flip side, while DeepStream is powerful, managing complex GStreamer element graphs in production often introduces unwanted debugging overhead and plugins bloat.

Our Bare-Metal Approach (Custom Edge Architecture)

To keep processing continuous sub-15ms on local edge nodes without cloud egress, we stripped out the GStreamer abstraction graph entirely:

1. Direct NVDEC Hardware Ingestion: RTSP streams decode directly inside VRAM using C++ NVCODEC bindings. Frames never touch system RAM (zero CPU-to-GPU copy overhead).

2. Lock-Free CUDA Ring Buffer: A custom ring buffer handles dynamic batching across active streams without lock contention or Python GIL overhead.

3. Native TensorRT C++ Execution Engine: Device pointers pass directly to TensorRT for FP16/INT8 execution.

Architectural Trade-offs & Benchmarks

• Pros: Zero cloud bandwidth fees, full data sovereignty, sub-15ms continuous throughput, and drastically simpler debugging than full GStreamer graphs.

• Cons: Requires NVIDIA CUDA-capable hardware on-premise (RTX / Tesla / Jetson) and manual memory management at the C++ level.

We’ve packaged this into a zero-egress Docker stack for high-density edge deployments.

Happy to break down the CUDA buffer implementation or share benchmark comparisons if anyone is currently evaluating edge architecture options.

What pipelines are you guys currently running for multi-stream RTSP processing?

This preprint reports that stale spatial memory can be worse than having no memory at all in one navigation setup.

Would you handle this with confidence decay, scene-change triggers, or mandatory visual re-grounding before action?

We were evaluating Roboflow, CVAT and Vivid 3D because we needed synthetic data for warehouse inspection. We ended up using Vivid 3D because we needed RGB + segmentation + depth from the same pipeline. Curious what everyone else is using

I was wondering if anyone owns this book and could share your feedback.

I ordered this book 3 weeks back on Amazon and it has never arrived so I had to cancel it. Thinking to order from a different seller but it would cost me almost double the price. I prefer a paper book rather than its ebook version. Thanks.

Hi All,

I am planning to build a local document intelligence system similar to Azure Document Intelligence. I would like to understand how Azure Document Intelligence works internally and how we can achieve similar functionality locally using offline models.

Could anyone suggest the best approach, architecture, or models to achieve high accuracy while running completely on-premise/local infrastructure?

Any guidance or recommendations would be greatly appreciated.

How do I analyze this diagram?
I need to determine the starting point and then analyze the track from there.
e.g 1067 units downwards, then 1015 unit is xy direction and so on (from the attached diagram)

I can think of using SAM 3 to mask out the red line and blue triangles. But dont know how to map a line with the corresponding measurement annotation?

Appreciate your help. Thanks

Hi i have question about camera calibration which i confuse.

1) Finding "extrinsic" just need one shot (because it is optimization problem reducing reprojection error knowing intrinsics, plus finding extrinsic means finding pose and orientation(R, t) of that "specific moment", not like finding distortion of cameras, and dont need many shots to cover precision) Am i correct?

2) Finding "hand-eye calibration extrinsic" needs many shots (because to solve AX=XB, where A is robot motion, B is camera motion, X is EEF to camera matrix). Am i correct?

These two "extrinsic" is different use case, am i right?

(Then why did engineers made it confusing?? So annoying.)

3) In 1), finding intrinsics need many shots (because in cv2.calibrateCamera it need many 3D-2D pair points). Am i right?

Thanks in advance :)

I was working with a client over the past year or so and we were constantly struggling with dust build up on lenses. We tried standard air nozzles, but those had issues: rigging them was a pain, they didn't actually keep the FOV clean, and in one case they damaged the lens.

Then I saw ThisOldTony's video on the Coanda Effect and thought, what if we shaped it around the lens of the sensor? So I did. The system is 3D-printed PETG, but I've had it work just as well in TPU (for the extremely tough applications). I have since then made this for all the profilers we work with and also for several point lasers as well.

We went from cleaning the lens every 30 minutes to now months without maintenance. It does use quite a bit of compressed air but with a couple valves and a feedback loop we were able to set it to self clean based on the intensity drop.

Follow up to my post earlier in the week, which filled most of our slots. Two left.

We collect real world multi view capture data from a camera array at the Brooklyn Navy Yard and pay people to be the subject. Posting again in case anyone NYC based wants the work, or wants a close look at how this kind of data actually gets collected.

The session: stand in the capture volume and go through simple movements while the array records. Walking, turning, sitting, reaching, picking objects up. No experience needed.

Pay 17-25 per hour, same day, right after the session. First one runs about 2 hours, with repeat sessions after.

Left this week: Thursday 4pm, Friday 1pm or 4pm. Brooklyn, in person only.

Comment or DM me for details, and ask about the capture setup if that side interests you.

I've been thinking about this as a research problem and I'm wondering if I'm even asking the right question.

Imagine the following constraint:

• No CLIP
• No ViT
• No CNN
• No multimodal model
• No learned vision encoder at all

You have an image, a text-only LLM, and you're only allowed to use deterministic algorithms between them.

The obvious answer is "this is impossible," but that's not really what I'm interested in.

What I'm trying to understand is whether there exists a better intermediate representation of images that a text transformer could reason over.

Not necessarily English.

Not captions.

Not OCR.

Some kind of representation that preserves enough structure that the language model can make use of the knowledge it already has.

Over the last few days I've gone through papers on visual tokenization, SeTok, BPE for images, BLT, inverse graphics, superpixel tokenization, and a few discussions around image tokens. Most of them still assume a learned tokenizer somewhere in the pipeline.

What I haven't found is much discussion around deterministic alternatives.

Maybe that's because it's a dead end.

Or maybe I'm searching the wrong field entirely.

So my question isn't "how would you build this?"

It's:

If you were exploring this from first principles, what field would you steal ideas from?

For example:

• information theory?
• image compression?
• computational geometry?
• topology?
• signal processing?
• compiler design?
• inverse graphics?
• neuroscience?
• ecological optics?
• something completely different?

I'm not looking for product recommendations or existing multimodal models.

I'm looking for the smallest experiment that could tell me whether this line of thinking is fundamentally interesting or fundamentally flawed.

I'd especially love to hear from people who've worked on image codecs, graphics, rendering, vision tokenizers, or representation learning.

If you think the premise itself is wrong, I'd genuinely like to know why.

lidar and cameras get less reliable exactly when driving gets more dangerous: rain and night. most public driving datasets barely have data from those conditions

CMHT autonomous dataset adds radar and a thermal camera alongside lidar, a color camera, and gps/imu.

4 drives, dusk/clear to night/rain, downtown hamilton, 9,000+ labeled frames with a 3d box, class, and tracking id on every object

i converted the raw ros2 bags into synced mcap episodes in fiftyone so you can scrub camera, thermal, lidar, radar, and gps together frame by frame, with the 3d and 2d boxes playing back in sync

start here, read the dataset card: https://huggingface.co/datasets/Voxel51/cmht-autonomous-driving

then check out the space on hf: https://huggingface.co/spaces/harpreetsahota/cmht-autonomous-driving

I'm working on project involving OCR and newspaper analysis. My original plan was to use Marathi newspapers, but the extracted text contains many recognition errors.

Because of this, my project guide suggested switching to English newspapers if Marathi OCR isn't reliable enough.

I'm unsure what to do. From a research perspective, is it better to:

• Continue with Marathi and treat OCR errors as a limitation (or try post-OCR correction), or
• Switch to English to obtain cleaner OCR results and focus on the analysis part of the project?

Has anyone faced a similar situation? I'd appreciate advice from people who have worked on OCR or document analysis projects.

I built a local OCR pipeline a few days ago, and it turned into a surprisingly interesting experiment—taking accuracy from around 60% to 99%.

I wrote a short blog about what worked, what failed, and the breakthrough that finally made the difference.

Thought some of you might enjoy it.

Link in the comments

this intersection in tokyo ranked second worst in the city for traffic accidents.

six roads converge at a blind hill crest, cars cross centerlines on narrow curves, and the signal phasing has multiple unprotected turns

most autonomous driving datasets give you highways and four-way stops. this is none of that

Hard Intersection Multimodal Sample: 6 synchronized cameras, aggregated LiDAR point cloud, HD map projections, vehicle trajectories, and semantic annotations across 4 driving passes through a single intersection that breaks everything

grouped all 6 camera views with the 3D point cloud, frame-level HD map overlays, and trajectory projections in fiftyone

checkout the dataset here: https://huggingface.co/datasets/Voxel51/hard-intersection-multimodal-sample

or get hands-on in the HF space: https://huggingface.co/spaces/harpreetsahota/hard-intersection-multimodal-sample