- Creating a custom image classifier is like training a smart puppy to distinguish between dandelions and daisies. πΌ It’s all about making your model learn new tricks on your own data set. πΆ Don’t forget to test your model with fresh data to make sure it’s not just a one-trick pony! πΎ#AI #ImageClassification #PyTorch
Introduction π
In today’s video, I will walk you through the process of creating a custom image classifier using ResNet-18 and Pytorch. Image classification is the task of categorizing a single image into a specific category. We will be using a dataset containing images of daisies and dandelions as an example to demonstrate the steps involved in creating our custom image classifier.
Understanding the Dataset πΌ
Before we dive into coding, let’s take a moment to understand the dataset structure. The dataset is organized into two folders: ‘train’ and ‘val’. Within each of these folders, we have subfolders containing images of daisies and dandelions. It’s important to ensure that the folder names correspond to the class labels, as these will be used by our model for classification.
| Class Name |
|---|
| Daisy |
| Dandelion |
Data Preprocessing πΌοΈ
In order to prepare our dataset for training, we need to perform data augmentation. This involves techniques such as random resizing and horizontal flipping to provide a variety of scenarios for our model to learn from. Additionally, we need to convert the data into tensors and normalize it to ensure consistency in scale.
The data loaders are responsible for loading the training and validation datasets in mini-batches for efficient processing.
Training the Model π
With the dataset preprocessed, we are now ready to train our custom image classifier. We’ll be using a pre-trained ResNet-18 model and fine-tuning it on our custom dataset to distinguish between daisies and dandelions.
Freezing Layers
We freeze all the layers in the pre-trained model except for the final classification layer. This allows us to retain the knowledge from the pre-trained model while adapting it to our specific task.
| Layer | Trainable |
|---|---|
| Conv Layers | No |
| FC Layer | Yes |
Loss Calculation and Optimization π
For the loss calculation, we utilize the cross-entropy loss, which is commonly used in classification problems. Additionally, we use stochastic gradient descent as the optimizer to update the model’s weights based on the calculated gradients.
Training and Validation π
During the training process, we keep track of the training and validation loss, as well as the accuracy of our model. It’s important to evaluate the model’s performance on unseen data to ensure its effectiveness in real-world scenarios.
Testing the Model π§ͺ
Testing our model on an example image allows us to verify its ability to correctly classify unseen data. By loading the trained model and preprocessing the test image, we can obtain the model’s prediction for the given input.
I hope this video has been helpful in understanding the process of creating a custom image classification model using Pytorch and ResNet-18. Thank you for watching! π
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