Image Classification in PyTorch

Introduction

Image classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from autonomous vehicles to medical imaging.

Such models are perfect to use with Gradio's image input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in Python, and it will look like this (try one of the examples!):

Let's get started!

Prerequisites

Make sure you have the gradio Python package already installed. We will be using a pretrained image classification model, so you should also have torch installed.

Step 1 — Setting up the Image Classification Model

First, we will need an image classification model. For this tutorial, we will use a pretrained Resnet-18 model, as it is easily downloadable from PyTorch Hub. You can use a different pretrained model or train your own.

import torch

model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()

Because we will be using the model for inference, we have called the .eval() method.

Step 2 — Defining a `predict` function

Next, we will need to define a function that takes in the user input, which in this case is an image, and returns the prediction. The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities. We will load the class names from this text file.

In the case of our pretrained model, it will look like this:

import requests
from PIL import Image
from torchvision import transforms

# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

def predict(inp):
  inp = transforms.ToTensor()(inp).unsqueeze(0)
  with torch.no_grad():
    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
  return confidences

Let's break this down. The function takes one parameter:

inp: the input image as a PIL image

Then, the function converts the image to a PIL Image and then eventually a PyTorch tensor, passes it through the model, and returns:

confidences: the predictions, as a dictionary whose keys are class labels and whose values are confidence probabilities

Step 3 — Creating a Gradio Interface

Now that we have our predictive function set up, we can create a Gradio Interface around it.

In this case, the input component is a drag-and-drop image component. To create this input, we use Image(type="pil") which creates the component and handles the preprocessing to convert that to a PIL image.

The output component will be a Label, which displays the top labels in a nice form. Since we don't want to show all 1,000 class labels, we will customize it to show only the top 3 images by constructing it as Label(num_top_classes=3).

Finally, we'll add one more parameter, the examples, which allows us to prepopulate our interfaces with a few predefined examples. The code for Gradio looks like this:

import gradio as gr

gr.Interface(fn=predict, 
             inputs=gr.inputs.Image(type="pil"),
             outputs=gr.outputs.Label(num_top_classes=3),
             examples=["lion.jpg", "cheetah.jpg"]).launch()

This produces the following interface, which you can try right here in your browser (try uploading your own examples!):

And you're done! That's all the code you need to build a web demo for an image classifier. If you'd like to share with others, try setting share=True when you launch() the Interface!

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