Hailo Model Zoo

The Hailo Model Zoo provides pre-trained models for high-performance deep learning applications. Using the Hailo Model Zoo you can measure the full precision accuracy of each model, the quantized accuracy using the Hailo Emulator and measure the accuracy on the Hailo-8 device. Finally, you will be able to generate the Hailo Executable Format (HEF) binary file to speed-up development and generate high quality applications accelerated with Hailo-8. The models are optimized for high accuracy on public datasets and can be used to benchmark the Hailo quantization scheme.

Change Log

V1.4

• Update to use Dataflow Compiler v3.14.0 (developer-zone)
• Update to use HailoRT 4.3.0 (developer-zone)
• Introducing Hailo Models - in house pretrained networks with compatible Dockerfile for easy retraining:
  • yolov5m_vehicles - vehicle detector based on yolov5m architecture
  • tiny_yolov4_license_plates - license plate detector based on tiny_yolov4 architecture
• New Task: face landmarks detection
  • tddfa_mobilenet_v1
  • Support 300W-LP and AFLW2k3d datasets
• New features:
  • Support compilation of several networks together - a.k.a multinets
  • CLI for printing network information
• Retraining Guide:
  • New training guide for yolov4 with compatible Dockerfile
  • Modifications for yolov5 retraining

V1.3

• Update to use Dataflow Compiler v3.12.0 (developer-zone)
• New task: indoor depth estimation
  • fast_depth
  • Support NYU Depth V2 Dataset
• New models:
  • resmlp12 - new architecture support (paper)
  • yolox_l_leaky
• Improvements:
  • ssd_mobilenet_v1 - in-chip NMS optimitzation (de-fusing)
• Model Optimitzation API Changes
  • Model Optimization parameters can be updated using the networks' model script files (*.alls)
  • Deprecated: quantization params in YAMLs
• Training Guide: new training guide for yolov5 with compatible Dockerfile

V1.2

• New features:
  • YUV to RGB on core can be added through YAML configuration.
  • Resize on core can be added through YAML configuration.
• Support D2S Dataset
• New task: instance segmentation
  • yolact_mobilenet_v1 (coco)
  • yolact_regnetx_800mf_20classes (coco)
  • yolact_regnetx_600mf_31classes (d2s)
• New models:
  • nanodet_repvgg
  • centernet_resnet_v1_50_postprocess
  • yolov3 - darkent based
  • yolox_s_wide_leaky
  • deeplab_v3_mobilenet_v2_dilation
  • centerpose_repvgg_a0
  • yolov5s, yolov5m - original models from link
  • yolov5m_yuv - contains resize and color conversion on HW
• Improvements:
  • tiny_yolov4
  • yolov4
• IBC and Equalization API change
• Bug fixes

V1.1

• Support VisDrone Dataset
• New task: pose estimation
  • centerpose_regnetx_200mf_fpn
  • centerpose_regnetx_800mf
  • centerpose_regnetx_1.6gf_fpn
• New task: face detection
  • lightfaceslim
  • retinaface_mobilenet_v1
• New models:
  • hardnet39ds
  • hardnet68
  • yolox_tiny_leaky
  • yolox_s_leaky
  • deeplab_v3_mobilenet_v2
• Use your own network manual for YOLOv3, YOLOv4_leaky and YOLOv5.

V1.0

• Initial release
• Support for object detection, semantic segmentation and classification networks

Public Models

Full list of pre-trained models can be found here.

Retrain on Custom Dataset

To retrain a network from the Hailo Model Zoo with your custom dataset please refer to the following guide.

Hailo Models

Full list of Hailo Models trained in-house for specific applications can be found here

Benchmarks

List of Hailo's benchmarks can be found in hailo.ai. In order to reproduce the measurements please refer to the following page.

Usage

Quick Start Guide

• Install the Hailo Dataflow Compiler, HailoRT and enter the virtualenv. In case you are not Hailo customer please contact hailo.ai
• Clone the Hailo Model Zoo

  git clone https://github.com/hailo-ai/hailo_model_zoo.git
  

• Run the setup script

  cd hailo_model_zoo; pip install -e .
  

• Run the Hailo Model Zoo. For example, to parse the ResNet V1 50 model:

  python hailo_model_zoo/main.py parse resnet_v1_50
  

Getting Started

For further functionality please see the GETTING_STARTED page (full install instructions and usage examples). The Hailo Model Zoo is using the Hailo Dataflow Compiler for parsing, model optimization, emulation and compilation of the deep learning models. Full functionality includes:

• Parse: model translation of the input model into Hailo's internal representation.
• Profiler: generate profiler report of the model. The report contains information about your model and expected performance on the Hailo hardware.
• Quantize: optimize the deep learning model for inference and generate a numeric translation of the input model into a compressed integer representation. For further information please see OPTIMIZATION.
• Compile: run the Hailo compiler to generate the Hailo Executable Format file (HEF) which can be executed on the Hailo hardware.
• Evaluate: infer the model using the Hailo Emulator or the Hailo hardware and produce the model accuracy.

For further information about the Hailo Dataflow Compiler please contact [hailo.ai](https:// hailo.ai/contact-us/).

License

The Hailo Model Zoo is released under the MIT license. Please see the LICENSE file for more information.

Contact

Please visit hailo.ai for support / requests / issues.