Image Classification using pre-trained DenseNet model in PyTorch | by Bhimraj Yadav

Bhimraj Yadav
3 min readMay 8, 2022


In this tutorial, you will learn how to classify images using a pre-trained DenseNet model in Pytorch. DenseNet is trained on more than a million images from the ImageNet database. This model can classify images into 1000 object categories, such as keyboard, mouse, pencil, etc.

Figure 1: A 5-layer dense block with a growth rate of k = 4
Figure 2: A deep DenseNet with three dense blocks.

Try it out in Google Colab.

GitHub Link: Image-Recognition-App-using-FastAPI-and-PyTorch

6 Simple Steps to be followed:

  1. Install and import required libraries
  2. Download an example Image and ImageNet output classes and load them
  3. Apply transformations to the image
  4. Load DenseNet model
  5. Get the final result/output.
  6. Plot the result

Step 1: Install and import required libraries

# Optional Part: To create an environment
python -m venv venv
source venv/bin/activate
# install these libraries
pip install torch torchvision Pillow matplotlib

After installing these required libraries, we can import them easily as shown below.

import torch
from PIL import Image
from torchvision import transforms,models

import matplotlib.pyplot as plt

Step 2: Download an example Image and ImageNet output classes and load them.

# Download an example image from the pytorch website
!wget "" -O "dog.jpg"
# Download ImageNet labels
# Read the image classes
with open("imagenet_classes.txt", "r") as f:
img_classes = [s.strip() for s in f.readlines()]
print("Total Image Classes :{}".format(len(img_classes)))
#displaying first five classes

# Printed Output
Total Image Classes :1000
['tench', 'goldfish', 'great white shark', 'tiger shark', 'hammerhead']

Step 3: Apply transformations to the image

# transforming input image
filename = "dog.jpg"
input_image =
preprocess = transforms.Compose([
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
input_tensor = preprocess(input_image)# create a mini-batch as expected by the model
input_batch = input_tensor.unsqueeze(0)

Step 4: Load densenet model

# model
model = models.densenet121(pretrained=True)
# sets the model in evaluation mode
# For more info about evaluation:# move the input and model to GPU for speed if availableif torch.cuda.is_available():
input_batch ='cuda')'cuda')
Figure 3: DenseNet Model

Step 5: Get the final result/output

with torch.no_grad():
output = model(input_batch)
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
probabilities = torch.nn.functional.softmax(output[0], dim=0)
# Show top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 5)
for id,prob in zip(top5_catid,top5_prob):
print(f'Image class: {img_classes[id]}\t----- {prob * 100:.4f} %')
# Printed Output
Image class: Samoyed ----- 86.7997 %
Image class: white wolf ----- 4.3434 %
Image class: keeshond ----- 2.5546 %
Image class: Arctic fox ----- 2.3647 %
Image class: Pomeranian ----- 1.7686 %

Step 5: Plot the result

classes = [img_classes[id] for id in top5_catid]
probabilities = [prob * 100 for prob in top5_prob]
# creating the bar plot
fig, (ax1,ax2) = plt.subplots(1,2,figsize=(24, 8))
ax1.set_title('Input Image of Dog')
ax1.imshow(input_image), probabilities)
ax2.set_xlabel("Top 5 Classes")
ax2.set_title("Image Classification")
Figure 4: Input Image vs Output Result


[1]: Huang et al. (2017). Densely Connected Convolutional Networks. Available:

[2]: DENSENET By Pytorch Team. Available:

[3]: DenseNet. Available:


Figure 5: DenseNet architectures for ImageNet
Figure 6: DenseNet task usage distribution
Video Tutorial: Image Recognition App using FastAPI and PyTorch
Figure 6: ImageNet Models usage over time.



Bhimraj Yadav

Computer Engineer | Python, ML/DL and NextJs Developer