Purpose
awesome-openai-vision-api-experiments is a resource repository for developers and researchers who want to experiment with and build on top of the OpenAI Vision API. It serves as a hub for innovative experiments, showcasing a variety of applications from simple image classification to advanced zero-shot learning models, for beginners and experts to explore the capabilities of the Vision API, share findings, and collaborate to advance the boundaries of visual AI.
Main Capabilities and Coverage
This repository demonstrates how to run inference on the following inputs: images, video files, and camera video streams. The experiments cover simple image classification, advanced zero-shot learning models, and more. The materials list multiple experiment topics, including: How CLIP and GPT-4V Compare for Classification, Experiments with GPT-4V for Object Detection, Distilling GPT-4 for Classification with an API, DINO-GPT4-V: Use GPT-4V in a Two-Stage Detection Model, First Impressions with GPT-4V(ision). In addition, several must-read papers are listed, covering Set-of-Mark Prompting, a preliminary exploration of GPT-4V, the GPT-4 system card, and more.
Usage and Limitations
Using the OpenAI API for experiments requires an API key, which can be obtained through the specified link. The materials state: each API key can use 100 API requests per day. This API cannot be used for object detection or image segmentation; this problem can be solved by combining GPT-4V with foundation models such as GroundingDINO or Segment Anything (SAM), and the repository provides related examples and blog posts. The repository welcomes contributions: you can submit new experiments or propose improvements, and participate through issues or pull requests; to add a new experiment, you can refer to the contribution guidelines.
Applicable Scenarios
Suitable for researchers, developers, and beginners and experts interested in visual AI who want to experiment, prototype, or learn based on the OpenAI Vision API. It can be used to explore inference capabilities on images, videos, and camera streams,as well as to combine the Vision API with foundation models to extend detection and segmentation capabilities.
Additional Notes
The materials do not provide specific installation commands, code example contents, repository folder structure details, or charging or pricing information; these contents are not provided for now.
