ndrean
I tried the 3 “standard” Image-To-Text models for image captioninng: “facebook/deit-base-distilled-patch16-224” and “microsoft/resnet-50” and "“google/vit-base-patch16-224”.
Results are poor, at least as low as the score ![]()
This car is predicted as:
%{predictions: [%{label: "speedboat", score: 0.136890709400177}]}
Among the 280 proposed I2T models by Hugging Face, does anyone has a recommendation?
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jonatanklosko
Are you looking for “Image classification” (pick one of predefined labels) or “Image-to-text” (describe image with text)? Also, how are you making the prediction? You can use Livebook Neural network smart cell to explore the various models, here’s one example per each of the tasks:
ndrean
I did not try Neural Network, just Image classification with Bumblebee from a Phoenix app.
But your Livebook prediction seems much better. I may need to import this into the Phoenix app. What is your set up? The following fails:
jonatanklosko
For
kino_bumblebee0.4.0 (released today) you want to use Livebook main (we will have a new release next week).jonatanklosko
How did you arrive at that score? Using the example from the docs:
ndrean
With the Livebook, yes, but I used this in Phoenix:
ndrean
This doesn’t compile neither, or I don’t know how:
{:kino_bumblebee, git: "https://github.com/livebook-dev/kino_bumblebee", branch: "main", override: true}jonatanklosko
I think this is a bug in Vix, the binary is laid out as
{height, width, channels}, but Vix returns{width, height, channels}.You can use kino_bumblebee
0.4.0and livebook main. What error are you getting?jonatanklosko
I reported the bug at Wrong shape returned from write_to_tensor/1 · Issue #126 · akash-akya/vix · GitHub : )
ndrean
It is in fact in
Nx.shapethat I did not pay attention… Thanks for finding this one.gives me:
jonatanklosko
Yeah, you need to either run Livebook main (until the release next week) or use kino
~> 0.10.0and kino_bumblebee~> 0.3.0, sorry for the inconvenience : )