ResNet50
Explore the process of training a ResNet50 image classification model using PyTorch. Understand dataset organization, model initialization, optimizer setup, data augmentations like mixup, loss functions, and checkpoint management. Gain practical insight into running training scripts, managing evaluation metrics, and preparing the model for inference.
We'll cover the following...
Note: The script in this lesson is not runnable due to a lack of GPU support.
The training process is straightforward because the PyTorch Image Model has its own configurable training script.
Organize the datasets into the following folder structure:
datasets
├── train
│ ├── class1
│ ├── class2
│ ├── class3
│ └── classN
└── val
├── class1
├── class2
├── class3
└── classN
We’ve included the training script and our custom models under the app folder. Let’s look at the following interactive terminal:
The script is taken from the timm’s official repository. We can run the script to train image classification models.
Note: The training script requires at least ...