Hailo Model Zoo
                     Hailo Model Zoo
What is a Model Zoo?
The Model Zoo is a repository which includes 
pre-trained, pre-compiled models and a full 
building and evaluation environment. Its aim 
is to provide developers with quick 
functionality to hit the ground running.
With the goal of making it as easy as 
possible for developers to get up and 
running with the Hailo-8™ AI processor, 
we have built our own open Hailo Model 
Zoo repository, which includes:
Pre-trained models – a large 
selection, demonstrating both 
the versatility and high AI 
performance of the Hailo-8.
Pre-configured build flows – validated and 
optimized flows to take each network 
from an ONNX/TensorFlow model to a 
deployable binary, incorporating models 
evaluation and performance analysis.
To allow a “fine print-free” evaluation we focused on 
populating hailo model zoo with popular models taken 
from open-source repositories without modification 
and trained on publicly available datasets. A link to 
the model source was also added in case users would 
like to adjust the model, for instance by training it on 
their own custom dataset.
How It Works
The Model Zoo employs the Hailo Dataflow Compiler for a full flow 
from a pre-trained model (ckpt/ONNX) to a final Hailo Executable 
Format (HEF) that can be executed on the Hailo-8. To that end, the 
Hailo Model Zoo provides users with the following functions:
Parse: translate Tensorflow/ONNX model 
into Hailo’s internal representation, which 
includes the network topology and the 
original weights of the model. The output 
of this stage is an Hailo Archive file 
(HAR).
Profile: a report that includes a the 
expected model’s performance on the 
Hailo-8 including FPS, latency, power 
consumption and a full breakdown for each 
layer in HTML format.
Quantize: optimize the model for runtime by 
converting it from full precision to limited integer bit 
precision (4/8/16) while minimizing accuracy 
degradation. This stage includes several algorithms 
that optimize performance by assuring the accuracy 
of the quantized model. The output of this stage is a 
Hailo Archive file that includes the quantized weights.
Evaluate: evaluate the model accuracy 
on common datasets (e.g., ImageNet, 
COCO). Evaluation can be done on the 
full precision model and on the quantized 
model using our numeric emulator or the 
Hailo-8.
Compile: compile the quantized 
model to generate a Hailo 
Executable Format (HEF) file which 
can be deployed on the Hailo-8 chip.