datasaurus
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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!
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
What are some alternatives?
ai-health-assistant - An open source AI health assistant
LLaVA - [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
Segment-Everything-Everywhere-All-At-Once - [NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
squirrel-datasets-core - Squirrel dataset hub
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of 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
guidance - A guidance language for controlling large language models.
squirrel-core - A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:
autodistill - Images to inference with no labeling (use foundation models to train supervised models).
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]