edgefirst-validator 5.3.0

EdgeFirst Validator

EdgeFirst Validator

This repository contains the Packer configuration, entrypoint script, and the validation implementation used by EdgeFirst Studio to perform validation of Vision (detection, segmentation, multitask) models with support for Keras, ONNX, TensorRT, and TFLite either on the cloud or on target validation.

Overview

• Changelog
• Installation
• Usage
• Documentation

Changelog

• Nov 26, 2025 [v5.0.0]: Merge Ultralytics and Deployment (EdgeFirst) metrics. Computation of deployment metrics @ optimal thresholds. Defined validation stages.
• Nov 14, 2025 [v4.2.0]: Added HAL support (TBR). Added Kinara model support. Distinguish semantic and instance segmentation metrics.
• Aug 24, 2025 [v4.1.0]: Added ModelPack external decoding. Added Keras support. Added TP vs FP scores chart. Removed +1 padding to the NumPy NMS.
• June 16, 2025 [v4.0.0]: EdgeFirst Validator (formerly Deep View Validator) - Ultralytics metrics and EdgeFirst Studio integrations.

Installation

For on target validation, the system may already contain the dependencies needed to run the model such as tflite_runtime for TensorFlow Lite. In such cases, it is recommended to first create a python virtual environment with the --system-site-packages option enabled.

EdgeFirst-Validator is available in PyPI and can be installed via pip.

pip install edgefirst-validator

Validation of specific models requires these installations which includes the packages needed to run the models, in case the system at present does not contain these packages.

• Keras pip install edgefirst-validator[keras]
• ONNX: pip install edgefirst-validator[onnx]
• TensorRT: pip install edgefirst-validator[tensorrt]
• TFLite: pip install edgefirst-validator[tflite]

Usage

1. As a quickstart, deploy default validation of a YOLOv5 model with COCO128 dataset.

   edgefirst-validator
   

2. Deploy validation as a user-managed session in EdgeFirst Studio. First login with EdgeFirst Client.

   edgefirst-client login
   

   edgefirst-validator --session-id <validator session ID>
   

   Otherwise, you can specify the credentials directly.

   edgefirst-validator --token <token> --session-id <validator session ID>
   

   edgefirst-validator --username <username> --password <password> --session-id <Validator Session ID>
   

3. Deploy standalone validation.

   edgefirst-validator <model path> <dataset path>
   

Documentation

Run the following commands to generate the documentation.

cd doc
pip install -r requirements.txt

make clean
make html