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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
PyTorch extensions for fast R&D prototyping and Kaggle farming
| Date | Stars |
|---|---|
| 2026-07-24 | 1575 |
| 2026-07-25 | 1575 |
| 2026-07-28 | 1575 |
| 2026-07-30 | 1575 |
| 2026-08-06 | 1575 |
Today
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Momentum
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growth rate 0.00%/day
# Important Update
On February 24th, 2022, Russia declared war and invaded peaceful Ukraine.
After the annexation of Crimea and the occupation of the Donbas region, Putin's regime decided to destroy Ukrainian nationality.
Ukrainians show fierce resistance and demonstrate to the entire world what it's like to fight for the nation's independence.
Ukraine's government launched a website to help russian mothers, wives & sisters find their beloved ones killed or captured in Ukraine - https://200rf.com & https://t.me/rf200_now (Telegram channel).
Our goal is to inform those still in Russia & Belarus, so they refuse to assault Ukraine.
Help us get maximum exposure to what is happening in Ukraine, violence, and inhuman acts of terror that the "Russian World" has brought to Ukraine.
This is a comprehensive Wiki on how you can help end this war: https://how-to-help-ukraine-now.super.site/
Official channels
* [Official account of the Parliament of Ukraine](https://t.me/verkhovnaradaofukraine)
* [Ministry of Defence](https://www.facebook.com/MinistryofDefence.UA)
* [Office of the president](https://www.facebook.com/president.gov.ua)
* [Cabinet of Ministers of Ukraine](https://www.facebook.com/KabminUA)
* [Center of strategic communications](https://www.facebook.com/StratcomCentreUA)
* [Minister of Foreign Affairs of Ukraine](https://twitter.com/DmytroKuleba)
Glory to Ukraine!
# Pytorch-toolbelt
A `pytorch-toolbelt` is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming:
## What's inside
* Easy model building using flexible encoder-decoder architecture.
* Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more.
* GPU-friendly test-time augmentation TTA for segmentation and classification
* GPU-friendly inference on huge (5000x5000) images
* Every-day common routines (fix/restore random seed, filesystem utils, metrics)
* Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more.
* Extras for [Catalyst](https://github.com/catalyst-team/catalyst) library (Visualization of batch predictions, additional metrics)
Showcase: [Catalyst, Albumentations, Pytorch Toolbelt example: Semantic Segmentation @ CamVid](https://colab.research.google.com/drive/1OUPJYU7TzH5Vz1si6FBkooackuIlzaGr#scrollTo=GUWuiO5K3aUm)
# Why
Honest answer is "I needed a convenient way to re-use code for my Kaggle career".
During 2018 I achieved a [Kaggle Master](https://www.kaggle.com/bloodaxe) badge and this been a long path.
Very often I found myself re-using most of the old pipelines over and over again.
At some point it crystallized into this repository.
This lib is not meant to replace catalyst / ignite / fast.ai high-level frameworks. Instead it's designed to complement them.
# Installation
`pip install pytorch_toolbelt`
# How do I ...
## Model creation
### Create Encoder-Decoder U-Net model
Below a code snippet that creates vanilla U-Net model for binary segmentation.
By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed `0`) to coarse (low-resolution) feature maps.
Access to all intermediate feature maps is beneficial if you want to apply deep supervision losses on them or encoder-decoder of object detection task,
where access to intermediate feature maps is necessary.
```python
from torch import nn
from pytorch_toolbelt.modules import encoders as E
from pytorch_toolbelt.modules import decoders as D
class UNet(nn.Module):
def __init__(self, input_channels, num_classes):
super().__init__()
self.encoder = E.UnetEncoder(in_channels=input_channels, out_channels=32, growth_factor=2)
self.decoder = D.UNetDecoder(self.encoder.channels, decoder_features=32)
self.logits = nn.Conv2d(self.decoder.channels[0], num_classes, kernel_size=1)
def forward(self, x):
x = self.encoder(x)
x = Excerpt of 9,884 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:99c9a19626e1c910, topic:object-detection, topic:image-segmentation, topic:image-classification
matched fp:99c9a19626e1c910, topic:deep-learning, topic:pytorch