Learn How to Build a Simple Image Classification Model with Feather

Table of Contents:

1. Introduction
2. File Directory
3. Feather Script Working
4. Special Init Function
5. File Upload Component
6. Run Image Classification Function
7. Numpy Array Conversion
8. Torchvision's Resnet
9. Transformations and Model Loading
10. Model Outputs Aggregation
11. Classification Component
12. Summary of Build Method
13. Global Name and Model Update
14. Description and Steps
15. Required Files for Feather

Introduction

In this video breakdown, we will dive into the line-by-line analysis of the Feather script. This script is responsible for the simple image classification model and its functioning. Let's explore the important aspects and functionality of the Feather script.

1. File Directory

Before delving into the details of the Feather script, let's take a look at the file directory. We have two main files: ftr.py and main.py. The main.py file contains the actual model and is responsible for running the model, collating the outputs, and returning them. Now, let's open the file and understand how the Feather script works.

2. Feather Script Working

The Feather script starts with a special init function, which is the first function to be executed. This function is responsible for displaying the components in the first step. We want a file upload component, so let's run the script and observe the code's mapping to the visual interface. The file upload component allows us to upload image files for classification.

3. Special Init Function

The special init function plays a crucial role in initializing the script. It takes no arguments but returns the inputs for the next function. Any initialization or data required for the subsequent functions should be included in the return of the init function. In our case, the return of init will become the inputs for the next function.

4. File Upload Component

The file upload component is responsible for accepting image files. It displays a title, such as "Upload Files for Image Classification," which is passed as a parameter. The component only accepts image files. Once the component is rendered, the input is passed to the next function.

5. Run Image Classification Function

The run image classification function receives the uploader, which contains a list of uploaded images. Each image is represented as a dictionary with a name and data. The uploaded.get feather function converts the images into numpy arrays. We focus on retrieving the image data, not the file names, for running the model.

6. Numpy Array Conversion

To prepare the images for the model, we use a list comprehension to iterate over the list of images. The images are then passed into the run model function. In this example, we utilize Torchvision's Resnet model, which is imported and included in the script's transformations section.

7. Torchvision's Resnet

The Torchvision's Resnet model is used to perform the image classification. Once the model is loaded, the run model function is responsible for transforming the images into tensors and passing them to the model. Line 20 handles the transformation, while the remaining lines aggregate the model outputs and return them.

8. Transformations and Model Loading

Within the script, we define the necessary transformations for the inputs to the model. These transformations Shape the inputs accordingly. Additionally, we load the Resnet model and import the ImageNet classes.

9. Model Outputs Aggregation

After classification, the outputs are passed to another component. We display the images and their corresponding classification using the view component. The text displayed for each image represents the classification provided by our Resnet model. Finally, we return the component, marking the last step on our Feather script's visual interface.

10. Classification Component

The classification component displays the images and their respective classifications. It utilizes the view component and provides the necessary text to display the classifications. This component enhances user interaction and understanding of the image classification model's performance.

11. Summary of Build Method

Let's summarize the build method covered so far. We first define a global name for the model associated with the user account. If the script is updated or the model weights are changed, visitors to the classification URL will always interact with the latest version of the model. Additionally, we add a description to be shown on the marketplace's "My Models" page.

12. Global Name and Model Update

The global name ensures that the latest version of the model is published to users who visit the URL. Any model with the same name as the one associated with your user account will be updated with the latest published version. This feature enables seamless updates and ensures users always benefit from the latest enhancements to the model.

13. Description and Steps

The description provides additional information about the model and its functionality. It appears on the marketplace's "My Models" page, giving potential users a clear understanding of the model's capabilities. Lastly, we have the steps, which are a list of functions. In our case, the only function we are using is the run image classification function.

14. Required Files for Feather

For the Feather script to run the model successfully, we require three specific files. These files are:

1. "__file" (the Feather script itself)
2. "main" document (contains the model execution)
3. "imagenet classes" (holds labeled information for the classes)

We pass these files to the file bundle method, ensuring all necessary components are Present to run the model.

Highlights

• The Feather script is responsible for a simple image classification model.
• The script contains an init function to initialize and display components.
• The file upload component accepts image files for classification.
• The run image classification function processes the uploaded images.
• Torchvision's Resnet model is used for classification.
• Transformations are applied to prepare the images for the model.
• The model outputs are aggregated and displayed using the classification component.
• The build method ensures the latest model version is published.
• A description provides information about the model on the marketplace.
• The Feather script requires specific files for successful model execution.

FAQ

Q: Can I use any name for the init function?\ A: Yes, you can use any name for the init function as long as you pass the actual function name to the special init parameter.

Q: How does Feather convert images to numpy arrays?\ A: Feather utilizes the uploaded.get feather function to convert the uploaded images into numpy arrays.

Q: What model is used for image classification in the script?\ A: The script utilizes Torchvision's Resnet model for image classification.

Q: What files are required to run the Feather script successfully?\ A: The Feather script requires "__file" (the script itself), "main" document (containing the model execution), and "imagenet classes" for labeled information.

Q: How does the build method ensure the latest version of the model is published?\ A: The build method uses a global name associated with the user account to update any model with the same name, ensuring the latest version is published and accessible.

Q: Can I customize the description of the model on the marketplace?\ A: Yes, you can provide a description that will be shown on the marketplace's "My Models" page, giving users a clear understanding of the model's functionality.