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I have downloaded the AffectNet dataset. It consists of npy files in the below folder format.
images
--->
0.jpg
annotations
-->
0_exp.npy
0_aro.npy
0_val.npy
0_lnd.npy
…
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Hi,
I have built with Ludwig support a sentiment regressor that has in input text and in output returns two float values. My model features configuration is this:
```
"input_features": [ {"na…
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When you're done, write your takeaways from the chapter in a comment on this issue, then remove yourself as an Assignee. Last one to do so closes the issue.
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Hi,
I wish to make a file with composite objects. Hence, I have a main file which contains objects of different classes (face, eyes, eyebrows etc) - the end product being an emoji.
Here's the …
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- [x] aggregate from the arousal / valance annotations discrete quadrant-based labels
- [x] plot all data points in one graph (in order to see tendencies and distribution over quadrants)
- [x] check…
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Thanks for sharing your work!
Could you elaborate more on how are the dimensional labels for each image derived? The labels provided in your `imdb_DimEmotion.mat` seem to be normalized between 0 a…
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I used the "I am not sure" checkbox during annotation, but found that the `unsure_arousal` and `unsure_valence` values in my final annotations were always `off`.
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Hey, I am facing an issue that while using the api in the code, when I run "run_example.py" in the api folder then it is generating me exact same csv for even different video files. What could be the …
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I'll update with the appropriate language shortly. The responses should assess valence (i.e., positive vs. negative emotion) not arousal (i.e., intense vs. not intense).
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