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Jan 31 • 5 tweets • 3 min read
Wanna try out the ConvNeXt models in @TensorFlow / #keras, inspect them, fine-tune them, etc.? Well, here they are: github.com/sayakpaul/Conv…

Includes a total of 15 I-1k and I-21k ConvNeXt models + conversion scripts, off-the-shelf inference, and fine-tuning code.

1/
These models ARE NOT opaque. You can load them like so and inspect whatever you need to:

2/ Image
Here's the full disclosure on the accuracy scores. Differences are mainly for library implementation differences. But happy to stand corrected if someone has other suggestions.

3/ Image
Contributions are welcome: github.com/sayakpaul/Conv…

Would love to also have the isotropic models implemented and converted.

4/
Huge thanks to @7vasudevgupta who helped me spot the transposition bug and @gusthema for discussions.

Models will soon be released from TF-Hub.

5/

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More from @RisingSayak

Sayak Paul Profile picture
Jan 18
Q: If I implement a paper, there are likely lots of implementations of that already existing. How do I make it worthwhile?

⬇️
1> I think the learning aspect should precede any other aspect in this regard. Whether or not it's gonna be worthwhile shouldn't matter if you are up for the learning challenge.
2> But if you want to make the implementation a part of your project portfolio, the following things could be helpful.

2.1> You could pick up papers that are a bit off the grid from the conventional ones while still being in your territory. You should also enjoy working on it.
Read 5 tweets
Sayak Paul Profile picture
Dec 23, 2021
Implementing a paper is helpful in so many ways. Get to

* Know the work inside out including the implementation details.
* Study amazing resources to further your understanding.
* Read a lot of code for references. Sometimes, the official codebases are amazing.

1/
Oftentimes, an idea seems fairly simple but when it comes to implementation details, things start to get messier. This is the learning, folks!

If the original impl. is messy, you might be able to make it elegant, simpler, and in turn, better.

2/
For me, implementing existing works has helped me become a better practitioner and also a better believer. It's almost always never easy but that's the real fun. It boosts your confidence and also your knowledge.

3/
Read 5 tweets
Sayak Paul Profile picture
Nov 1, 2021
@soumikRakshit96 and I have been working on this project for a while now. Today, we are delighted to share our progress.

Point cloud segmentation in the wild with @TensorFlow:

github.com/soumik12345/po…

1/
Our repository comes with full TPU support. You can also use multiple GPUs with mixed-precision (when supported). Here's a blog post to get started:

keras.io/examples/visio…

Thanks to @fchollet for your reviews.

2/
We provide standalone scripts and also notebooks for training and testing our models. We open-source all the experimental results and pre-trained models:

github.com/soumik12345/po…

3/ Image
Read 4 tweets
Sayak Paul Profile picture
Jun 1, 2021
Recipes that I find to be beneficial when working in low-data/imbalance regimes (vision):

* Use a weighted loss function &/or focal loss.
* Either use simpler/shallower models or use models that are known to work well in these cases. Ex: SimCLRV2, Big Transfer, DINO, etc.

1/n
* Use MixUp or CutMix in the augmentation pipeline to relax the space of marginals.
* Ensure a certain percent of minority class data is always present during each mini-batch. In @TensorFlow, this can be done using `rejection_resampling`.

tensorflow.org/guide/data#rej…

2/n
* Use semi-supervised learning recipes that combine the benefits of self-supervision and few-shot learning. Ex: PAWS by @facebookai.
* Use of SWA is generally advised for better generalization but its use in these regimes is particularly useful.

3/n
Read 4 tweets
Sayak Paul Profile picture
Apr 26, 2021
New #Keras example is up on *consistency regularization*or an important recipe for semi-supervised learning and tackling distribution shifts as shown in *Noisy Student Training*.

keras.io/examples/visio…

1/n
This example provides a template for performing semi-supervised / weakly supervised learning. A few things one can plug right in:

* Incorporate more data while training the student.
* Filter the high-confidence predictions while training the student.

2/n
The example uses Stochastic Weight Averaging during training the teacher to induce geometric ensembling. With elements like Stochastic Dropout, the performance might even be better.

Here are the full experiments: git.io/JO55v.
Read 5 tweets
Sayak Paul Profile picture
Dec 2, 2020
Got the @TensorFlow Developer Certification.

Thanks to the #ML @GoogleDevExpert program for sponsoring the exam.

In this thread, I will summarize my experience.

⬇️
If you use @TensorFlow in your work moderately, I think you already have the prerequisites. Definitely take the *TensorFlow in Practice* specialization by @lmoroney & @DeepLearningAI_. It will get you up to speed.

Study the contents rigorously.
Review the certificate handbook carefully. It really has all the information you need to know about the certification - tensorflow.org/extras/cert/TF….

* Install @pycharm & get sufficiently comfortable with it.
* Set up the exam environment properly.
Read 6 tweets

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