The Hailo Model Explorer is a dynamic tool designed to help users explore the models on the Model Zoo and select the best NN models for their AI applications.
The Model Zoo gives users the capability to quickly and easily reproduce the Hailo published performance on the common models and architectures included in our Model Zoo and retrain these models. The collection encompasses both common and state-of-the-art models available in TensorFlow and ONNX formats.
The pre-trained models can be used for rapidly prototyping on Hailo devices and each model is accompanied by a binary HEF file, fully supported within the Hailo toolchain and Application suite (accessible to registered users only).
Selecting an appropriate model to use in your application can be challenging due to various factors like inference speed, model availability, desired accuracy, licensing, and more. Inference speed is unique since it cannot be easily estimated without the underlying hardware used.
Unfortunately, no single intrinsic model attribute (e.g., FLOPS, parameters, size of activation maps. etc.) is a reliable predictor for inference speed and, to complicate things further, different hardware architectures have different optimal workloads. While it is possible to measure the inference time for each model, it can be tedious and time consuming.
The Model Explorer below was designed to helps users in making better-informed decisions, ensuring maximum efficiency on the Hailo platform. The Model Explorer offers an interactive interface with filters based on Hailo device, tasks, model, FPS, and accuracy, allowing users to explore numerous NN models from Hailo’s vast library.
Read more about how the Hailo Model Zoo works and what it can do on the Hailo Blog.
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