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People complaining about quality here are missing the point, this is ONNX compatible inference engine written Rust, it just using 5MB SqueezeNet from 2016 for s

People complaining about quality here are missing the point, this is ONNX compatible inference engine written Rust, it just using 5MB SqueezeNet from 2016 for simplicity. Question is, is it worth to invest time and effort into ONNX ? https://en.wikipedia.org/wiki/SqueezeNet https://en.wikipedia.org/wiki/SqueezeNet https://github.com/onnx/models?tab=readme-ov-file#image-classification- https://github.com/onnx/models?tab=readme-ov-file#image-clas... here is the same model using tensorflowjs https://hpssjellis.github.io/beginner-tensorflowjs-examples-in-javascript/pre-trained/31-squeezenet.html https://hpssjellis.github.io/beginner-tensorflowjs-examples-... https://t-shaped.nl/posts/running-ai-models-in-the-browser-using-wonnx https://t-shaped.nl/posts/running-ai-models-in-the-browser-u...

brk 3y ago
Missing the point? When the classifications are horribly bad, what is the point? I can write a random phrase generator in FAR less than 5MB that would have the same overall accuracy as this.
- dns_snek 3y ago
  The point you're missing that this is a just a demo for Wonnx, an inference runtime for ONNX models. You can plug your own model into it, it's a general-purpose inference runtime that runs in the browser.
- cchance 3y ago
  The point is you can swap in any ONNX model, instead of the 5mb shitty one
FpUser 3y ago
>"this is ONNX compatible inference engine written Rust" Ah, the fact that it is written in Holy Rust instantly absolves abysmal quality.
- SpaceNugget 3y ago
  No, the inference accuracy of the image classifier is dependent on the model used and this is a demo of the code executing the model in a browser with GPU acceleration not the model itself. You can plug and play any model in the onnx format e.g. https://github.com/onnx/models https://github.com/onnx/models. As a comparison, complaining about the "abysmal quality" of the dummy model on display here is like saying blender is bad 3d modeling software after opening it for the first time because all it models is a blank cube.
- deleted 3y ago
  [deleted]
- cchance 3y ago
  No the point is it's ONNX and can be pointed to any ONNX model, wow people have really gotten dumb on HN lately
cchance 3y ago
Seriously HN is feeling more like youtube comments section lately I don't know what happened.

brk 3y ago

Missing the point? When the classifications are horribly bad, what is the point? I can write a random phrase generator in FAR less than 5MB that would have the same overall accuracy as this.

dns_snek 3y ago
The point you're missing that this is a just a demo for Wonnx, an inference runtime for ONNX models. You can plug your own model into it, it's a general-purpose inference runtime that runs in the browser.
cchance 3y ago
The point is you can swap in any ONNX model, instead of the 5mb shitty one

FpUser 3y ago

>"this is ONNX compatible inference engine written Rust" Ah, the fact that it is written in Holy Rust instantly absolves abysmal quality.

SpaceNugget 3y ago
No, the inference accuracy of the image classifier is dependent on the model used and this is a demo of the code executing the model in a browser with GPU acceleration not the model itself. You can plug and play any model in the onnx format e.g. https://github.com/onnx/models https://github.com/onnx/models. As a comparison, complaining about the "abysmal quality" of the dummy model on display here is like saying blender is bad 3d modeling software after opening it for the first time because all it models is a blank cube.
deleted 3y ago
[deleted]
cchance 3y ago
No the point is it's ONNX and can be pointed to any ONNX model, wow people have really gotten dumb on HN lately

cchance 3y ago

Seriously HN is feeling more like youtube comments section lately I don't know what happened.