Absolutely. We'll update it.

Thanks!
Jonathan

On Fri, Dec 3, 2021, 2:24 PM Victor Miura <[email protected]> wrote:

> This specification references "tflite" model files.  Could you please add
> a reference to the specification of the model format that would be exposed?
>
> Thanks,
> Victor
>
> On Fri, Dec 3, 2021 at 11:50 AM 'Jonathan Bingham' via blink-dev <
> [email protected]> wrote:
>
>> Contact emails
>>
>> [email protected], [email protected], [email protected]
>>
>> Explainer
>>
>>
>> https://github.com/webmachinelearning/model-loader/blob/master/explainer.md
>>
>> Specification
>>
>> https://webmachinelearning.github.io/model-loader/#api
>>
>> Summary
>>
>> Run machine learning models natively, taking advantage of hardware
>> acceleration. Currently, javascript libraries must parse a model and call
>> WebGL, WebGPU and WASM for the individual compute operations. Providing a
>> model loader API gains performance by optimizing in the browser and
>> potentially taking advantage of new ML processors like TPUs.
>>
>>
>> Blink component
>>
>> Blink>WebML
>> <https://bugs.chromium.org/p/chromium/issues/list?q=component:Blink%3EWebML>
>>
>> Motivation
>>
>> Model Loader is a proposed web API to load a custom, pre-trained machine
>> learning model in a standard format, compile it for the available hardware,
>> and apply it to example data in JavaScript in order to perform inference,
>> like classification, regression, or ranking. The idea is to make it as easy
>> as possible for web developers to use a custom, pre-built machine learning
>> model in their web app, across devices and browsers.
>>
>> Performing inference locally can:
>>
>> - Preserve privacy, by not shipping user data across the network
>>
>> - Improve performance, by eliminating network latency, running models
>> natively and taking advantage of hardware acceleration, including
>> specialized hardware not available with WebGL, WebGPU or WASM.
>>
>> - Provide a fallback if network access is unavailable, possibly using a
>> smaller and lower quality model
>>
>> The graph-based Web NN API and the Model Loader API are complementary
>> approaches. We'll need to do some benchmarking to understand if there are
>> performance differences, and get feedback from developers to see if it's
>> valuable to offer both types of API.
>>
>> Initial public proposal
>>
>> https://webmachinelearning.github.io/model-loader-intro/
>>
>> TAG review
>>
>> TAG review status
>>
>> Pending
>>
>> Risks
>>
>> The Model Loader API is currently included as a Tentative Specification
>> in the Web Machine Learning Working Group Charter.
>>
>>
>> Interoperability and Compatibility
>>
>> Gecko: No signals
>>
>> WebKit: No signals
>>
>>
>> Chrome/TensorFlow: Positive
>>
>> Intel : Collaborating in Working Group
>>
>> Microsoft/ONNX: Collaborating in Working Group
>>
>> PyTorch: No signals
>>
>> Salesforce: Collaborating in Working Group
>>
>>
>>
>> Debuggability
>>
>> Is this feature fully tested by web-platform-tests
>> <https://chromium.googlesource.com/chromium/src/+/master/docs/testing/web_platform_tests.md>
>> ?
>>
>> No
>>
>> Flag name
>>
>> Requires code in //chrome?
>>
>> False
>>
>> Tracking bug
>>
>> https://bugs.chromium.org/p/chromium/issues/detail?id=1263240
>>
>> Estimated milestones
>>
>> No milestones specified
>>
>>
>> Link to entry on the Chrome Platform Status
>>
>> https://www.chromestatus.com/feature/6505078216196096
>>
>> This intent message was generated by Chrome Platform Status
>> <https://www.chromestatus.com/>.
>>
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>> .
>>
>
>
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