---
layout: hub_detail
background-class: hub-background
body-class: hub
title: SimpleNet
summary: Lets Keep it simple, Using simple architectures to outperform deeper and more complex architectures
category: researchers
image: simplenet.jpg
author: Seyyed Hossein Hasanpour
tags: [vision, scriptable]
github-link: https://github.com/Coderx7/SimpleNet_Pytorch/blob/master/imagenet/simplenet.py
github-id: coderx7/simplenet_pytorch
featured_image_1: simplenet.jpg
featured_image_2: no-image
accelerator: cuda-optional
order: 10
demo-model-link: https://huggingface.co/spaces/coderx7/simplenet
---

```python
import torch
model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_5m_m1", pretrained=True)
# or any of these variants
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_5m_m2", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_9m_m1", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_9m_m2", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m1_05", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m2_05", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m1_075", pretrained=True)
# model = torch.hub.load("coderx7/simplenet_pytorch:v1.0.0", "simplenetv1_small_m2_075", pretrained=True)
model.eval()
```

All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.
The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`
and `std = [0.229, 0.224, 0.225]`.

Here's a sample execution.

```python
# Download an example image from the pytorch website
import urllib
url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)
```

```python
# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

# move the input and model to GPU for speed if available
if torch.cuda.is_available():
    input_batch = input_batch.to('cuda')
    model.to('cuda')

with torch.no_grad():
    output = model(input_batch)
# Tensor of shape 1000, with confidence scores over ImageNet's 1000 classes
print(output[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
probabilities = torch.nn.functional.softmax(output[0], dim=0)
print(probabilities)
```

```
# Download ImageNet labels
!wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
```

```
# Read the categories
with open("imagenet_classes.txt", "r") as f:
    categories = [s.strip() for s in f.readlines()]
# Show top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 5)
for i in range(top5_prob.size(0)):
    print(categories[top5_catid[i]], top5_prob[i].item())
```

### Model Description

SimpleNet models were proposed in "Lets Keep it simple, Using simple architectures to outperform deeper and more complex architectures".  
Here we have the 8 versions of simplenet models, which contains 1.5m, 3.2m, 5.7m and 9.5m parameters respectively.  
Detailed model architectures can be found in Table 1 and Table 2.  
Their 1-crop errors on ImageNet dataset with pretrained models are listed below.  

The m2 variants    

|      Model structure       | Top-1 errors   | Top-5 errors |
| :------------------------- | :-----------:  | :-----------:|
|  simplenetv1_small_m2_05   |     38.33      |     16.512   |
|  simplenetv1_small_m2_075  |     31.494     |     11.85    |
|  simplenetv1_5m_m2         |     27.97      |     9.676    |
|  simplenetv1_9m_m2         |     25.77      |     8.252    |

The m1 variants   

|      Model structure       | Top-1 errors   | Top-5 errors |
| :------------------------- | :-----------:  | :-----------:|
|  simplenetv1_small_m1_05   |     38.878     |     17.012   |
|  simplenetv1_small_m1_075  |     32.216     |     12.282   |
|  simplenetv1_5m_m1         |     28.452     |     10.06    |
|  simplenetv1_9m_m1         |     26.208     |     8.514    |

### References

 - [Lets Keep it simple, Using simple architectures to outperform deeper and more complex architectures](https://arxiv.org/abs/1608.06037)