RegNetY

**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):

$$ u\_{j} = w\_{0} + w\_{a}\cdot{j} $$

For **RegNetX** authors have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier).

For **RegNetY** authors make one change, which is to include [Squeeze-and-Excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).

## How do I use this model on an image?
To load a pretrained model:

```python
import timm
model = timm.create_model('regnety_002', pretrained=True)
model.eval()
```

To load and preprocess the image:
```python 
import urllib
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

config = resolve_data_config({}, model=model)
transform = create_transform(**config)

url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
urllib.request.urlretrieve(url, filename)
img = Image.open(filename).convert('RGB')
tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```

To get the model predictions:
```python
import torch
with torch.no_grad():
    out = model(tensor)
probabilities = torch.nn.functional.softmax(out[0], dim=0)
print(probabilities.shape)
# prints: torch.Size([1000])
```
The context provides specific details about both RegNetX and RegNetY models. It mentions that RegNetY includes Squeeze-and-Excitation blocks, which is a key difference from RegNetX. Additionally, it outlines some parameter restrictions for RegNetX, such as the bottleneck ratio and width multiplier, but does not mention these for RegNetY. The context clearly states the main difference between the two models, which is the inclusion of Squeeze-and-Excitation blocks in RegNetY. Therefore, the question is answerable with the given context.


The question about the key difference between RegNetX and RegNetY models is quite useful for machine learning developers, especially those working with the Hugging Face ecosystem. Understanding the differences between these models can help developers choose the right architecture for their specific NLP tasks. RegNet models are a family of neural networks designed for efficient training and inference, and knowing the distinctions between different variants like RegNetX and RegNetY can aid in optimizing performance and resource usage. This knowledge is particularly relevant for developers looking to implement state-of-the-art models in their applications.


The question "What is the key difference between RegNetX and RegNetY models?" is context-independent because it clearly asks for a comparison between two specific models, RegNetX and RegNetY. An operator with access to documentation or knowledge about these models can understand and answer the question without needing additional context or information. The question does not refer to any specific setting or document, making it self-contained.

