The constraints of edge devices often necessitate extra steps to convert and
optimize models before they run efficiently, and visualization is one of the
most effective ways to understand a model and identify targets for optimization.
Conversion
Quantization
Optimization
Model Explorer's side-by-side comparison feature makes it easier to
spot conversion-related issues. Navigate the graph layer by layer,
diving deeper into the graph by expanding and collapsing sections.
Inspect the internal structure and connections within graphs at the
granularity you need.
Use Model Explorer to identify problematic operations affected by
quantization. Sort ops by error metrics to find quality drops,
get insights per layer, and compare different quantization
results to find the ideal model size-quality trade-off.
Use Model Explorer to better understand the output from your
benchmarking and debugging tools. Gain insights into which ops can
run on GPU, sort ops by latency, and compare per-op performance
across accelerators.
Support for large models
Model Explorer is designed to render large models seamlessly. Thousands of
nodes? No problem. The GPU-based rendering engine is capable of scaling up to
smoothly render even very large models. And Model Explorer's unique approach to
collapsing layers like a system of files and folders means that it's faster and
easier to navigate.
Features designed to help you work faster
Search
Split View
Data Overlays
Powerful regex-based search helps you locate, filter, and highlight
specific nodes.
Load models side by side in the same tab for easy comparison.
Load custom, node-specific data into Model Explorer to quickly
identify hot spots and other issues with your model.
Export to .png
Bookmarking
Easy to access metadata
With the click of a button, export an image of the graph to share
with your team.
Save your location in the graph by adding bookmarks, making it easy
to jump between areas.
View tensor shapes, trace inputs and outputs, highlight identical
layers, see child node counts, and more.
Two ways to use Model Explorer
Run it locally
Run it in a Colab notebook
Follow the easy installation
instructions on GitHub to set up Model Explorer on your local
machine. It runs in a browser window and all your data stays local.
Supports Linux, Mac and Windows.
Model Explorer runs well in Colab, meaning you can integrate it into
your existing model development workflow. Try the demo
notebook or follow the installation
instructions to add it to your own.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-01-13 UTC."],[],[],null,["# Model Explorer\n\n**A visualization tool that lets you analyze ML models and graphs, accelerating\ndeployment to on-device targets.**\n\n[Get Started](https://github.com/google-ai-edge/model-explorer/wiki/1.-Installation)\n[Try it in Colab](https://github.com/google-ai-edge/model-explorer/blob/main/example_colabs/quick_start.ipynb)\n[Learn More](https://github.com/google-ai-edge/model-explorer/)\n\n### Making edge development faster\n\nThe constraints of edge devices often necessitate extra steps to convert and\noptimize models before they run efficiently, and visualization is one of the\nmost effective ways to understand a model and identify targets for optimization.\n\n| **Conversion** | **Quantization** | **Optimization** |\n|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Model Explorer's side-by-side comparison feature makes it easier to spot conversion-related issues. Navigate the graph layer by layer, diving deeper into the graph by expanding and collapsing sections. Inspect the internal structure and connections within graphs at the granularity you need. | Use Model Explorer to identify problematic operations affected by quantization. Sort ops by error metrics to find quality drops, get insights per layer, and compare different quantization results to find the ideal model size-quality trade-off. | Use Model Explorer to better understand the output from your benchmarking and debugging tools. Gain insights into which ops can run on GPU, sort ops by latency, and compare per-op performance across accelerators. |\n\n### Support for large models\n\nModel Explorer is designed to render large models seamlessly. Thousands of\nnodes? No problem. The GPU-based rendering engine is capable of scaling up to\nsmoothly render even very large models. And Model Explorer's unique approach to\ncollapsing layers like a system of files and folders means that it's faster and\neasier to navigate.\n\n### Features designed to help you work faster\n\n| **Search** | **Split View** | **Data Overlays** |\n|-------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|\n| Powerful regex-based search helps you locate, filter, and highlight specific nodes. | Load models side by side in the same tab for easy comparison. | Load custom, node-specific data into Model Explorer to quickly identify hot spots and other issues with your model. |\n| With the click of a button, export an image of the graph to share with your team. | Save your location in the graph by adding bookmarks, making it easy to jump between areas. | View tensor shapes, trace inputs and outputs, highlight identical layers, see child node counts, and more. |\n\n### Two ways to use Model Explorer\n\n| **Run it locally** | **Run it in a Colab notebook** |\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Follow the easy [installation instructions](https://github.com/google-ai-edge/model-explorer/wiki/1.-Installation) on GitHub to set up Model Explorer on your local machine. It runs in a browser window and all your data stays local. Supports Linux, Mac and Windows. | Model Explorer runs well in Colab, meaning you can integrate it into your existing model development workflow. Try the [demo notebook](https://github.com/google-ai-edge/model-explorer/blob/main/example_colabs/quick_start.ipynb) or follow the [installation instructions](https://github.com/google-ai-edge/model-explorer/wiki/5.-Run-in-Colab-Notebook) to add it to your own. |"]]
