instant-ngp
sentence-transformers
instant-ngp | sentence-transformers | |
---|---|---|
147 | 45 | |
15,364 | 13,793 | |
1.1% | 2.1% | |
6.7 | 9.2 | |
15 days ago | 5 days ago | |
Cuda | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
instant-ngp
- I want a 3d scanner...
-
Mind-blowing results (LORA/Checkpoint mix)
This is really cool! Could you now use something like this to turn the new images in a 3d model? Or even use open pose (controlnet) to generate a bunch of images from different angles and use InstantNeRF to make a 3d model for free!
-
Scanning in real life environments to be viewed in VR >>> taking pictures. Simple process from video -> render, using instant-ngp
It is at this point that you should have Instant-NGP setup. The script for the COLMAP processing is in the repo, as well as the steps to perform it. My exact parameters were 3 fps and 16 aabb. It is pretty helpful to add the scripts directory into path for exact access system-wide.
-
[D] NeRF, LeRF, Prolific Dreamer, Neuralangelo, and a lot of other cool NeRF research
[Project Page] https://nvlabs.github.io/instant-ngp/
-
Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
instant-ngp ([1]) from NVIDIA can render NeRF in VR in real-time, assuming a very good desktop video card. Note that instant-ngp is not as photo-realistic as Zip-NeRF. But it's still very good!
1. https://github.com/NVlabs/instant-ngp
- How about Ranger Green?
-
Roast my MC kit
Playing around with neRF AI (https://github.com/NVlabs/instant-ngp) to create some 3d gear reveals. I think this a fun way to show off a kit, what do you think?
- Has anyone tried to generate images from enough angles to feed Nvidia Nerf to make 3D models?
-
Instant NPG: how do minimize noise and maximize quality? Tips welcome!
3 not sure if it's the one you want but the -aabb_scale is a crop. This page recommends trying a large value of 128 for some outdoor scenes: https://github.com/NVlabs/instant-ngp/blob/master/docs/nerf_dataset_tips.md
-
I NeRF'd the new Taco Bell on Rt. 40
I don't know about lumalabs, but basically all NeRF projects these days are based on NVIDIAs Instant neural graphics primitives ( GitHub: instant-ngp). It utilizes COLMAP for SfM (preprocessing step for the neural network) and runs on average Geforce cards pretty good. The fox example (50 photos) on their page literally takes 5 seconds to complete.
sentence-transformers
-
External vectorization
txtai is an open-source first system. Given it's own open-source roots, like-minded projects such as sentence-transformers are prioritized during development. But that doesn't mean txtai can't work with Embeddings API services.
-
[D] Looking for a better multilingual embedding model
Ok great. My use case is not very specific, but rather general. I am looking for a model that can perform asymmetric semantic search for the languages I mentioned earlier (Urdu, Persian, Arabic etc.). I have also looked into the sentence-transformer training documentation. Do you think it would be a good idea to use the XNLI dataset for fine-tuning? Or maybe you can suggest much better dataset. Furthermore, I am not sure if fine-tuning is suitable for my task. Because my use case is general so I can use already trained model.
- Best pathway for Domain Adaptation with Sentence Transformers?
-
Syntactic and Semantic surprisal using a LLM
The task you are looking for is semantic textual similarity. There are a few models and datasets out there that can do this. I'd probably start with the SemEval2017 Task 1 task description and competition entries here and then work outward from there (using something like SemanticScholar or Papers With Code to find newer state of the art works that cite these models if needed). For what it's worth you might find that Sentence Bert (SBERT) gives good vectors for cosine similarity comparison out of the box for this task.
-
Mean pooling in BERT
Check out the sentence-transformers implementation. If I don't miss anything they don't exclude CLS when the pooling strategy is set to 'mean'
-
I Built an AI Search Engine that can find exact timestamps for anything on Youtube using OpenAI Whisper
Break up transcript into shorter segments and convert segments to a 768 vector array. Use a process known as embedding using our second ML model, UKP Labs BERT’s sentence transformer model.
-
Seeking advice on improving NLP search results
Not sure what kind of texts you have, but these models have a max sequence limit of 512 (approx 350 words or so). If you're texts are longer than that, consider splitting them up into chunks or creating a summary and taking an embedding of that. Some clustering algorithm may be the way to go here. Here's a bunch of examples. I use agglomerative for my use case.
-
Dev Diary #12 - Finetune model
https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/data_augmentation (Augmented Encoding)
-
[R] Customize size of Bio-BERT pre-trained embeddings
For vector representation you can take the mean and then pca to get the size that you want, but if you have time then use sentence transformers to train a vector representation instead.
- SentenceTransformer producing different sentence embedding results in Docker
What are some alternatives?
awesome-NeRF - A curated list of awesome neural radiance fields papers
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
onnx - Open standard for machine learning interoperability
nerf-pytorch - A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
TensoRF - [ECCV 2022] Tensorial Radiance Fields, a novel approach to model and reconstruct radiance fields
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
colmap - COLMAP - Structure-from-Motion and Multi-View Stereo
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
instant-meshes - Interactive field-aligned mesh generator
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools