LAVIS
nerf-from-image
LAVIS | nerf-from-image | |
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18 | 1 | |
8,781 | 363 | |
2.9% | 0.0% | |
6.3 | 0.7 | |
18 days ago | about 1 year ago | |
Jupyter Notebook | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
LAVIS
- FLaNK AI for 11 March 2024
- FLaNK 04 March 2024
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[D] Why is most Open Source AI happening outside the USA?
For multimodal, there's China (*many), then Salesforce.
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Need help for a colab notebook running Lavis blip2_instruct_vicuna13b?
Been trying for all day to get a working inference for this example: https://github.com/salesforce/LAVIS/tree/main/projects/instructblip
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most sane web3 job listing
There's also been big breakthroughs in computer vision. Not that long ago it was hard to recognize if a photo contained a bird; that's solved now by models like CLIP, Yolo, or Segment Anything. Now research has moved on to generating 3D scenes from images or interactively answering questions about images.
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I work at a non-tech company and have been asked to make software that is impossible. How do I explain it to my boss?
The new hotness is multimodal vision-language models like InstructBLIP that can interactively answer questions about images. Check out the examples in the github repo, I would not have thought this was possible a few years ago.
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Two-minute Daily AI Update (Date: 5/15/2023)
Salesforce’s BLIP family has a new member– InstructBLIP, a vision-language instruction-tuning framework using BLIP-2 models. It has achieved state-of-the-art zero-shot generalization performance on a wide range of vision-language tasks, substantially outperforming BLIP-2 and Flamingo. (Source)
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InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Github
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Can I use my own art as a training set?
Most of my workflows are self-made. For captioning I used Blip-2 in a custom script I made that automates the process by going into directories and their sub-directories and creates a .txt file beside each image. This way I can keep my images organized in their proper directories, without having to put dump them all in a single place.
- FLiP Stack Weekly for 13-Feb-2023
nerf-from-image
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most sane web3 job listing
There's also been big breakthroughs in computer vision. Not that long ago it was hard to recognize if a photo contained a bird; that's solved now by models like CLIP, Yolo, or Segment Anything. Now research has moved on to generating 3D scenes from images or interactively answering questions about images.
What are some alternatives?
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
CLIP-Caption-Reward - PyTorch code for "Fine-grained Image Captioning with CLIP Reward" (Findings of NAACL 2022)
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
robo-vln - Pytorch code for ICRA'21 paper: "Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation"
DeepViewAgg - [CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"
linkis - Apache Linkis builds a computation middleware layer to facilitate connection, governance and orchestration between the upper applications and the underlying data engines.
multimodal - A collection of multimodal datasets, and visual features for VQA and captionning in pytorch. Just run "pip install multimodal"
clipseg - This repository contains the code of the CVPR 2022 paper "Image Segmentation Using Text and Image Prompts".
Oscar - Oscar and VinVL
PhotoPrism - AI-Powered Photos App for the Decentralized Web 🌈💎✨
Flow-SGP30-MLX90640 - Pimoroni Breakout Garden - SGP30 - MLX90650 - RP400 - NiFi - Kafka - Pulsar - Flink - Spark - Iceberg