nvdiffrec VS yolov5

It depends on which CV research you’re in. In NeRF view synthesis, it’s pretty common to use test sets as validation sets. This has been done in several papers, including oral papers.

I think there came a paper recently nerf2mesh, which I still have to evaluate (but haven't found time yet). There's also https://github.com/NVlabs/nvdiffrec/. And there's cool easy-to-use research software like nerfstudio (at least if you compare it to a lot of the raw code releases from research papers).

For your use case, why is your model devoid of texture? You can try 3D scanning your desired object so that it comes with texture. Either that or use Nvidia's MoMA here to get your object from images.

Have you seen this project: https://github.com/NVlabs/nvdiffrec (I haven't tried it). Also videos tend to have compression. If you can get images you'll get higher quality results with most photogrammetry software. Projects like meshroom are probably better for this if you have high quality pictures. There's a few articles that cover high quality scans that can help also.

You can generate an accurate mesh from a NeRF: https://github.com/NVlabs/nvdiffrec, and measure from that.

NERF specifically generates a radiance field, but there are research codes for turning that into a mesh (https://github.com/NVlabs/nvdiffrec) (not easy to use yet)

Hi, I'm not sure if this is the right place, but I was looking into seeing what the latest photo to model reconstruction looks like from here with NVIDIA (ArXiV paper is included there). There's a couple of neat examples, and after one dumb mistake setup was pretty easy. However, the meshes are not converging except very loosely when using the examples from the paper.

Hi everyone! I'm not sure this is the right place to ask, but I've been drooling over these cool ml and deep learning techniques showcased in videos. I was wondering if anyone could help me out in getting something like nvdiffrec to work with my own sample. https://github.com/NVlabs/nvdiffrec

Ref https://www.youtube.com/watch?v=0GwnxFNfZhM https://github.com/ultralytics/yolov5 https://dev.to/gfstealer666/kaaraich-yolo-alkrithuemainkaartrwcchcchabwatthu-object-detection-3lef https://www.kaggle.com/datasets/devdgohil/the-oxfordiiit-pet-dataset/data

!git clone https://github.com/ultralytics/yolov5.git ## Navigate to the model %cd yolov5/ ## Install requirements !pip install -r requirements.txt ## Download the YOLOv5 model !wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt

Specifically what I did was take the coco128.yaml, added 6 new classes from Dataset A (which have already been converted to YOLO Darknet TXT), from index 0-5 and subsequently adjusted the indices of the other COCO classes. The I proceeded to train and validate on Dataset A for 20 epochs.

I am using the default YOLOv5m6 model here with sahi/yolov5 library for my object detection project. I want to change just some of labels - for example when YOLO detects a human, I want it to label the human as "threat", not "person". Is there any way I can do it just changing some code, or I should train the model from scratch by just changing labels?

You should add them without annotations. Go through this.

You are correct and if you check the firmware, it's yet another famous 3rd party project without attribution, namely https://github.com/ultralytics/yolov5

On one side, being a Linux user for 24 years now, it annoys me that they rip off code and claiming it as theirs again, thus violating licenses, but on the other thanks to k3d's exploit I'm able to tinker more with the machine and if needed do (selective) updates by hand then with a closed source system. It's not just "klipper", with klipper, fluidd and moonraker, it's also ffmpeg and mjpegstreamer. It's gonna be interesting since they also use a project that isn't just GPL, but APGL (in short "If your software gives service online, you have to publish the source code of it and any library that it borrows functions from.") - they use yolov5 (for AI).