Add new detection models for enhanced object detection capabilities: Integrate OmDet-Turbo and LLMDet models for improved open vocabulary zero-shot object detection.
Add flexible model endpoint routing: Updated proxy server to support dynamic model-specific endpoints (changed from predictions/Owl-v2 to predictions/{model_name}) to accommodate various model prediction methods.
Add comprehensive log management commands: Introduce new logs, status, and clean commands for easier service monitoring and maintenance.
logs command: View service logs with configurable line count (e.g., ./run.sh logs proxy -n 100)
status command: Check running services, port status, and disk usage
clean command: Remove old rotated logs and temporary files (e.g., ./run.sh clean --days 30)
Implement automatic log rotation with retention policy: Configure Log4j2-based log rotation with 15-day retention, daily rotation, 100MB size limit, and automatic .gz compression for all TorchServe logs (access, model, service, metrics).
Enhance service cleanup reliability: Improve stop_services() function to handle partial service states gracefully with specific pattern matching (python.*/proxy/main.py) and health checks before stopping services.
Fix deployment script cleanup behavior: Correct cleanup trap to preserve background services on normal exit and only trigger cleanup on errors, preventing unintended service termination.
Improve log configuration for Docker compatibility: Update eva_ts/config.properties and create eva_ts/log4j2.xml with relative paths to ensure proper operation in containerized environments.
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.
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.
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.
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.