dinov2
mods
| dinov2 | mods | |
|---|---|---|
| 8 | 11 | |
| 8,008 | 2,455 | |
| 5.3% | 6.4% | |
| 6.3 | 9.0 | |
| 26 days ago | 9 days ago | |
| Jupyter Notebook | Go | |
| Apache License 2.0 | MIT License |
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.
dinov2
- FLaNK Stack 5-June-2023
-
Meta Open-Sources Computer Vision Foundation Model DINOv2
I remain sceptical.
Look at the license in question:
https://github.com/facebookresearch/dinov2/blob/main/LICENSE
Not a single time it talks about the act of copying and/or using the data. It constantly talks about sharing it.
- DINOv2: Computer Vision Foundation Model by Meta AI Is on GitHub as CC-by-NC 4.0
-
DinoV2: Meta’s Open Source State-of-the-art computer vision models
License
https://github.com/facebookresearch/dinov2/blob/main/README....
Yes, DINOv2 isn't open source, but it is source-available. Hopefully a free and open source alternative can replicate its performance.
-
DINOv2 - Another tool, from Meta, for computer vision
Code and pretrained models: https://github.com/facebookresearch/dinov2
- DINOv2: State-of-the-art computer vision models with self-supervised learning
- DINOv2: Learning Robust Visual Features Without Supervision
mods
-
Essential Command Line Tools for Developers
View on GitHub
- AI on the Command Line
-
LocalAI v1.18.0 release!
Mods
- FLaNK Stack 5-June-2023
- Mods adds support for LocalAI
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LLM, ttok and strip-tags–CLI tools for working with ChatGPT and other LLMs
Another enthusiastic vote for https://github.com/charmbracelet/mods - this is precisely the UX I was looking and waiting for - the day that I cloned it and started using it within my terminal was the day I no longer needed to even window out to firefox - and it feels very natural to compose with pipes, wrap into shell scripts, etc.
Early days, but you can see some of the ways this is already helping me out quite a bit (and increasing my enjoyment of things I already like to do): github.com/zackproser/automations
- Mods: AI for the command line built with Bubble Tea and Go
- GPT for command line pipelines
- ChatGPT on the command line
What are some alternatives?
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
wizapp - The Wizard's Apprentice, an AI-powered Typescript project functionality suite with CLI.
lance - Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.
Rio - A hardware-accelerated GPU terminal emulator focusing to run in desktops and browsers.
butterfish - A shell with AI superpowers
jmap - JSON Meta Application Protocol Specification (JMAP)
chat_term - fast terminal access to ChatGPT
fq - jq for binary formats - tool, language and decoders for working with binary and text formats
bashGPT - Use ChatGPT, GPT-3 and other models from the command line.
shell_gpt - A command-line productivity tool powered by AI large language models like GPT-4, will help you accomplish your tasks faster and more efficiently.
thoughtloom - ThoughtLoom is a powerful tool designed to foster creativity and enhance productivity through the use of LLMs directly from the command line. It facilitates rapid development and integration of LLM-based tools into various workflows, empowering individuals and teams to experiment, collaborate, and ultimately streamline their daily tasks.