codet

Maintainer: adirik

Total Score

1

Last updated 5/23/2024
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Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

The codet model is an object detection AI model developed by Replicate and maintained by the creator adirik. It is designed to detect objects in images with high accuracy. The codet model shares similarities with other object detection models like Marigold, which focuses on monocular depth estimation, and StyleMC, MaSaCtrl-Anything-v4-0, and MaSaCtrl-Stable-Diffusion-v1-4, which are focused on text-guided image generation and editing.

Model inputs and outputs

The codet model takes an input image and a confidence threshold, and outputs an array of image URIs. The input image is used for object detection, and the confidence threshold is used to filter the detected objects based on their confidence scores.

Inputs

  • Image: The input image to be processed for object detection.
  • Confidence: The confidence threshold to filter the detected objects.
  • Show Visualisation: An optional flag to display the detection results on the input image.

Outputs

  • Array of Image URIs: The output of the model is an array of image URIs, where each URI represents a detected object in the input image.

Capabilities

The codet model is capable of detecting objects in images with high accuracy. It uses a novel approach called "Co-Occurrence Guided Region-Word Alignment" to improve the model's performance on open-vocabulary object detection tasks.

What can I use it for?

The codet model can be useful in a variety of applications, such as:

  • Image analysis and understanding: The model can be used to analyze and understand the contents of images, which can be valuable in fields like e-commerce, security, and robotics.
  • Visual search and retrieval: The model can be used to build visual search engines or image retrieval systems, where users can search for specific objects within a large collection of images.
  • Augmented reality and computer vision: The model can be integrated into AR/VR applications or computer vision systems to provide real-time object detection and identification.

Things to try

Some ideas for things to try with the codet model include:

  • Experiment with different confidence thresholds to see how it affects the accuracy and number of detected objects.
  • Use the model to analyze a variety of images and see how it performs on different types of objects.
  • Integrate the model into a larger system, such as an image-processing pipeline or a computer vision application.


This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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