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EVA Vision ML Release Notes

v1.0.0 (Oct 1, 2025)

  • Compatibility: This version requires EVA App version 2.2.0 or higher

Features

  • TorchServe Migration: Migrated from ALO ML framework to TorchServe for production-grade model serving with improved reliability and scalability.
    • Real-Time HTTP-Based Inference: Replaced file-based API communication with real-time HTTP-based inference endpoints for faster and more efficient processing.
    • Unified API Endpoint: Introduced a FastAPI-based proxy server that consolidates TorchServe's multiple ports (inference, management, and metrics) into a single unified interface while maintaining consistent API endpoint paths.
    • Optimized OWLv2 Model Handlers: Separated OWLv2 model into dedicated handlers with zero-shot detection and image-guided detection split into independent handlers for optimized batch inference.
    • Few-Shot Learning Support: Added few-shot learning capabilities through the image-guided detection handler.

Internal Improvements

  • Project Architecture Restructured: Reorganized project architecture with clear separation between TorchServe handlers and proxy server components for improved maintainability and scalability.
  • Utility Modules Streamlined: Optimized utility modules for better reusability across handlers and cleaner codebase organization.
  • Model Workflow Improved: Enhanced model download and packaging workflow with dedicated scripts for more efficient model management.
  • LLMDet and OMDet-Turbo Temporarily Removed: Temporarily removed LLMDet and OMDet-Turbo models for migration to TorchServe architecture. These models will be reintroduced in version 1.1.0 with TorchServe support.

v0.1.0 (Sep 30, 2025)

  • Compatibility Notice: This version requires EVA App version 1.1.0 to 2.1.2

Features

  • Add new detection models for enhanced object detection capabilities: Integrate LLMDet and OMDet-Turbo models for open vocabulary zero-shot object detection.
  • Remove YOLOE model from the supported model list: Due to licensing issues, YoloE has been excluded from the supported models.

Internal Improvements

  • Requirements Configuration Optimized: Updated requirements.txt and uv.lock files to streamline the deployment process and improve system performance.

Bug Fixes

  • Python 3.10 Compatibility Fixed: Resolved compatibility issues with Python 3.10 to ensure proper functionality across supported Python versions.
  • Ultralytics Dependencies Removed: Removed ultralytics dependencies to resolve version conflicts and improve package stability.
  • Bounding Box Handling Error Fixed: Fixed an error in bbox handling that occurred when processing multiple bounding boxes during few-shot learning operations.
  • Inference Dataset Folder Created: Added inference_dataset folder to establish a consistent inference workflow.