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Changelog

v2.13

  • Update to use Dataflow Compiler v3.29.0 (developer-zone)
  • Update to use HailoRT 4.19.0 (developer-zone)
  • Using jit_compile which reduces dramatically the emulation inference time of the Hailo Model Zoo models.
  • New tasks:
    • BEV: Multi-View 3D Object Detection
      • Added support for NuScenes dataset
      • Added PETRv2 with the following configuration:
        1. Backbone: RepVGG-B0 (800x320 input resolution)
        2. Transformer: 3 decoder layers, detection queries=304, replaced LN with UN
  • New Models:
    • CAS-ViT - S, M, T - Convolutional-Attention based classification model
    • YOLOv10 - base, x-large - Latest YOLO detectors
    • CLIP Text Encoders - ResNet50x4, ViT-Large
  • New retraining Docker containers for:
    • PETR - Multi-View 3D Object Detection
  • Introduced new flags for hailomz CLI:
    • --ap-per-class for measuring average-precision per-class. Relevant for object detection and instance segmentation tasks.
  • Bug fixes

v2.12

  • Update to use Dataflow Compiler v3.28.0 (developer-zone)
  • Update to use HailoRT 4.18.0 (developer-zone)
  • Target hardware now supports Hailo-10H device
  • New Models:
    • Original ViT models - tiny, small, base - Transformer based classification models
    • DeiT models - tiny, small, base - Transformer based classification models
    • DETR (resnet50) - Transformer based object detection model
    • fastvit_sa12 - Fast transformer based classification model
    • levit256 - Transformer based classification model
    • YOLOv10 - nano, small - Latest YOLO detectors
    • RepGhostNet1.0x, RepGhostNet2.0x - Hardware-Efficient classification models
  • New postprocessing support on NN Core:
    • yolov6 tag 0.2.1
  • Added support for person attribute visualization
  • Bug fixes

v2.11

  • Update to use Dataflow Compiler v3.27.0 (developer-zone)

  • Update to use HailoRT 4.18.0 (developer-zone)

  • New Models:

    • FastSAM-s - Zero-shot Instance Segmentation
    • Yolov9c - Latest Object Detection model of the YOLO family

  • Using HailoRT-pp for postprocessing of the following variants:

    • nanodet

    Postprocessing JSON configurations are now part of the cfg directory.

  • Introduced new flags for hailomz CLI:

    • --start-node-names and --end-node-names for customizing parsing behavior.
    • --classes for adjusting the number of classes in post-processing configuration.

    The --performance flag, previously utilized for compiling models with their enhanced model script if available, now offers an additional functionality. In instances where a model lacks an optimized model script, this flag triggers the compiler's Performance Mode to achieve the best performance

    These flags simplify the process of compiling models generated from our retrain dockers.

  • Bug fixes

v2.10

  • Update to use Dataflow Compiler v3.26.0 (developer-zone)
  • Update to use HailoRT 4.16.0 (developer-zone)
  • Using HailoRT-pp for postprocessing of the following variants:
    • yolov8
  • Profiler change:
    • Removal of --mode flag from hailomz profile command, which generates a report according to provided HAR state.
  • CLI change:
    • hailo8 target is deprecated in favor of hardware
  • Support KITTI Stereo Dataset
  • New Models:
    • vit_pose_small - encoder based transformer with layernorm for pose estimation
    • segformer_b0_bn - encoder based transformer with batchnorm for semantic segmentation
  • Bug fixes

v2.9

  • Update to use Dataflow Compiler v3.25.0 (developer-zone)
  • Update to use HailoRT 4.15.0 (developer-zone)
  • A new CLI-compatible API that allows users to incorporate format conversion and reshaping capabilities into the input:
hailomz compile yolov5s --resize 1080 1920 --input-conversion nv12_to_rgb
  • New transformer models added:
    • vit_pose_small_bn - encoder based transformer with batchnorm for pose estimation
    • clip_resnet_50x4 - Contrastive Language-Image Pre-Training for zero-shot classification
  • New retraining dockers for vit variants using unified normalization.
  • New Models:
    • yolov8s_pose / yolov8m_pose - pose estimation
    • scdepthv3 - depth-estimation
    • dncnn3 / dncnn_color_blind - image denoising
    • zero_dce_pp - low-light enhancement
    • stereonet - stereo depth estimation
  • Using HailoRT-pp for postprocessing of the following models:
    • efficientdet_lite0 / efficientdet_lite1 / efficientdet_lite2

v2.8

  • Update to use Dataflow Compiler v3.24.0 (developer-zone)
  • Update to use HailoRT 4.14.0 (developer-zone)
  • The Hailo Model Zoo now supports the following vision transformers models:
    • vit_tiny / vit_small / vit_base - encoder based transformer with batchnorm for classification
    • detr_resnet_v1_18_bn - encoder/decoder transformer for object detection
    • clip_resnet_50 - Contrastive Language-Image Pre-Training for zero-shot classification
    • yolov5s_c3tr - object detection model with a MHSA block
  • Using HailoRT-pp for postprocessing of the following variants:
    • yolov5
    • yolox
    • ssd
    • efficientdet
    • yolov7
  • New Models:
    • repvgg_a1 / repvgg_a2 - classification
    • yolov8_seg: yolov8n_seg / yolov8s_seg / yolov8m_seg - instance segmentation
    • yolov6n_0.2.1 - object detection
    • zero_dce - low-light enhancement
  • New retraining dockers for:
    • yolov8
    • yolov8_seg
  • Enable compilation for hailo15h device
  • Enable evaluation of models with RGBX / NV12 input format
  • Bug fixes

v2.7

  • Update to use Dataflow Compiler v3.23.0 (developer-zone)
  • Updated to use HailoRT 4.13.0 (developer-zone)
  • Inference flow was moved to new high-level APIs
  • New object detection variants:
    • yolov8: yolov8n / yolov8s / yolov8m / yolov8l / yolov8x
    • damoyolo: damoyolo_tinynasL20_T / damoyolo_tinynasL25_S / damoyolo_tinynasL35_M
  • New transformers based models:
    • vit_base - classification model
    • yolov5s_c3tr - object detection model with a self-attention block
  • Examples for using HailoRT-pp - support for seamless integration of models and their corresponding postprocessing
    • yolov5m_hpp
  • Configuration YAMLs and model-scripts for networks with YUY2 input format
  • DAMO-YOLO retraining docker
  • Bug fixes

v2.6.1

  • Bug fixes

v2.6

  • Update to use Dataflow Compiler v3.22.0 (developer-zone)
  • Updated to use HailoRT 4.12.0 (developer-zone)
  • ViT (Vision Transformer) - new classification network with transformers-encoder based architecture
  • New instance segmentation variants:
    • yolov5n_seg
    • yolov5s_seg
    • yolov5m_seg
    • yolov5l_seg
  • New object detection variants for high resolution images:
    • yolov7e6
    • yolov5n6_6.1
    • yolov5s6_6.1
    • yolov5m6_6.1
  • New flag --performance to reproduce highest performance for a subset of networks
  • Hailo model-zoo log is now written into sdk_virtualenv/etc/hailo/modelzoo/hailo_examples.log
  • Bug fixes

v2.5

  • Update to use Dataflow Compiler v3.20.1 (developer-zone)
  • Model scripts use new bgr to rgb conversion
  • New Yolact variants - with all COCO classes:
    • yolact_regnetx_800mf
    • yolact_regnetx_1.6gf
  • Bug fixes

v2.4

  • Updated to use Dataflow Compiler v3.20 (developer-zone)

  • Required FPS was moved from models YAML into the models scripts

  • Model scripts use new change activation syntax

  • New models:

    • Face Detection - scrfd_500m / scrfd_2.5g / scrfd_10g

  • New tasks:

    1. Super-Resolution
    • Added support for BSD100 dataset
    • The following models were added: espcn_x2 / espcn_x3 / espcn_x4
    1. Face Recognition
    • Support for LFW dataset
    • The following models were added:
      1. arcface_r50
      2. arcface_mobilefacenet
    • Retraining docker for arcface architecture

  • Added support for new hw-arch - hailo8l

v2.3

  • Updated to use Dataflow Compiler v3.19 (developer-zone)
  • New models:
    • yolov6n
    • yolov7 / yolov7-tiny
    • nanodet_repvgg_a1_640
    • efficientdet_lite0 / efficientdet_lite1 / efficientdet_lite2
  • New tasks:
    • mspn_regnetx_800mf - single person pose estimation
    • face_attr_resnet_v1_18 - face attribute recognition
  • Single person pose estimation training docker (mspn_regnetx_800mf)
  • Bug fixes

v2.2

  • Updated to use Dataflow Compiler v3.18 (developer-zone)
  • CLI change:
    • Hailo model zoo CLI is now working with an entry point - hailomz
    • quantize sub command was changed to optimize
    • Hailo model zoo data directory by default will be ~/.hailomz
  • New models:
    • yolov5xs_wo_spp_nms - a model which contains bbox decoding and confidence thresholding on Hailo-8
    • osnet_x1_0 - person ReID network
    • yolov5m_6.1 - yolov5m network from the latest tag of the repo (6.1) including silu activation
  • New tasks:
    • person_attr_resnet_v1_18 - person attribute recognition
  • ReID training docker for the Hailo model repvgg_a0_person_reid_512/2048

NOTE: Ubuntu 18.04 will be deprecated in Hailo Model Zoo future version

NOTE: Python 3.6 will be deprecated in Hailo Model Zoo future version

v2.1

  • Updated to use Dataflow Compiler v3.17 (developer-zone)

  • Parser commands were moved into model scripts

  • Support Market-1501 Dataset

  • Support a new model zoo task - ReID

  • New models:

    • yolov5s_personface - person and face detector
    • repvgg_a0_person_reid_512 / repvgg_a0_person_reid_2048 - ReID networks which outputs a person embedding
      These models were trained in-house as part of our upcoming new application
    • stdc1 - Segmentation architecture for Cityscapes

v2.0

  • Updated to use Dataflow Compiler v3.16 (developer-zone) with TF version 2.5 which require CUDA11.2
  • Updated to use HailoRT 4.6 (developer-zone)
  • Retraining Dockers - each retraining docker has a corresponding README file near it. New retraining dockers:
    • SSD
    • YOLOX
    • FCN
  • New models:
    • yolov5l
  • Introducing Hailo Models, in-house pretrained networks with compatible Dockerfile for retraining
    • yolov5m_vehicles (vehicle detection)
    • tiny_yolov4_license_plates (license plate detection)
    • lprnet (license plate recognition)
  • Added new documentation to the YAML structure

v1.5

  • Remove HailoRT installation dependency.

  • Retraining Dockers

    • YOLOv3
    • NanoDet
    • CenterPose
    • Yolact

  • New models:

    • unet_mobilenet_v2

  • Support Oxford-IIIT Pet Dataset

  • New multi-network example: detection_pose_estimation which combines the following networks:

    • yolov5m_wo_spp_60p
    • centerpose_repvgg_a0

  • Improvements:

    • nanodet_repvgg mAP increased by 2%
  • New Tasks:
    • hand_landmark_lite from MediaPipe
    • palm_detection_lite from MediaPipe
    Both tasks are without evaluation module.

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:
  • 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 optimization (de-fusing)
  • Model Optimization 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