Overeasy enables the creation of custom zero-shot computer vision pipelines by orchestrating pre-trained models. It targets developers and researchers who need to build vision solutions for tasks like detection, classification, and segmentation without extensive dataset annotation. The primary benefit is rapid prototyping of complex vision workflows using readily available models.

How It Works

Overeasy utilizes a modular "Agent" system, where each agent performs a specific vision task (e.g., object detection, classification). These agents are chained together into "Workflows," forming execution graphs that define the data flow and processing sequence. This approach allows for flexible composition of diverse zero-shot models, abstracting away the complexities of model integration and data handling.

Quick Start & Requirements

• Primary install: pip install overeasy
• Prerequisites: Python 3.7+, Pillow. GPU with CUDA support is recommended for performance.
• Documentation: https://docs.overeasy.sh/
• Example Colab: https://colab.research.google.com/github/overeasy-sh/overeasy/blob/main/examples/overeasy_colab.ipynb

Highlighted Details

• Orchestrates zero-shot models like OWL-ViT and CLIP.
• Supports bounding box detection and classification; segmentation is planned.
• Visualizes execution graphs using Gradio.
• Offers agents for NMS, splitting/joining images, and class mapping.

Maintenance & Community

• Support channel: help@overeasy.sh
• Issue tracker available on GitHub.

Licensing & Compatibility

• License: MIT License.
• Compatible with commercial and closed-source applications.

Limitations & Caveats

Segmentation capabilities are listed as "Coming Soon." The library is relatively new, and extensive community adoption or long-term maintenance signals are not yet apparent from the README.