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Add Computer Vision Models #1682

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Add Computer Vision Models #1682

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gmalivenko
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https://github.com/nerox8664/awesome-computer-vision-models

This is a list with popular machine learning models with corresponding metrics like flops / number of parameters / declared accuracy.

Reviewed #1622, #1640.

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Go to the top and read it again.

@sindresorhus
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Thanks for making an Awesome list! 🙌

It looks like you didn't read the guidelines closely enough. I noticed multiple things that are not followed. Try going through the list point for point to ensure you follow it. I spent a lot of time creating the guidelines so I wouldn't have to comment on common mistakes, and rather spend my time improving Awesome.

@gmalivenko
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✔ Linting

@gmalivenko
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And removed license name from readme.md.

@gmalivenko
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unicorn

@akegaviar akegaviar mentioned this pull request Jan 10, 2020
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@sergey-pimenov sergey-pimenov left a comment

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Description of your awesome list must be common explanation of list subject.
A list description should not begin with the words "A list of...", "Awesome list of..." etc.
In your case just explain what is "Computer Vision Models". This explanation will be the description of your list.

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@Delta456 Delta456 left a comment

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I think it should be like A curated list of popular classification, segmentation and detection models with corresponding evaluation metrics by using tensorflow.js.

@Delta456 Delta456 mentioned this pull request Jan 20, 2020
@gmalivenko
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unicorn

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@xserban xserban left a comment

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The list is under 'Deep Learning', although a 'Computer Vision' class already exists.
The model's list seems more suitable to be under Computer vision, rather than Deep Learning.

@gmalivenko
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unicorn

@gmalivenko
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@sindresorhus

@sindresorhus
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Still doesn't follow the guidelines. Please read through each item.

@sindresorhus
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I decided to rename master branch to main, and apparently that permanently closes all PRs. Ugh. Sorry about that. Can you reopen this PR targeting the main branch? Include a link to this PR in the new one, so we can quickly see what was discussed previously.

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5 participants