datasaurus
| Segment-Everything-Everywhere- | datasaurus | |
|---|---|---|
| 2 | 1 | |
| - | 11 | |
| - | - | |
| - | 7.2 | |
| - | 7 months ago | |
| TypeScript | ||
| - | Apache License 2.0 |
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Segment-Everything-Everywhere-
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Is supervised learning dead for computer vision?
Yes, you can. The model that I was talking about LLaVA only output text but other models such as SEEM (https://github.com/UX-Decoder/Segment-Everything-Everywhere-...) outputs a segmentation map. You could prompt the model "Where is the pickleball in the image?" and get a segmentation map that you could then use to compute its center. Please let me know if you would be interested to have SEEM available in Datasaurus
datasaurus
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Is supervised learning dead for computer vision?
And let’s talk about development speed. By using text prompts to interact with your images, you can whip up a computer vision prototype in seconds. It’s fast, it’s efficient, and it’s changing the game.
So, what do you all think? Are we moving towards a future where foundational models take the lead in computer vision, or is there still a place for training models from scratch?
P.S. Shameless plug: I’ve been working on this open-source platform called Datasaurus https://github.com/datasaurus-ai/datasaurus) that taps into the power of vision-language models. It’s all about helping engineers get the insights they need from images, fast. Just wanted to share some thoughts and start a conversation. Let’s talk about the future of computer vision!
What are some alternatives?
LLaVA - [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
ai-health-assistant - An open source AI health assistant
Segment-Everything-Everywhere-All-At-Once - [NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
squirrel-datasets-core - Squirrel dataset hub
guidance - A guidance language for controlling large language models.
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
autodistill - Images to inference with no labeling (use foundation models to train supervised models).
squirrel-core - A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:
obsidian-ava - Quickly format your notes with ChatGPT in Obsidian
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]