Transfer Learning for Computer Vision Tutorial

Created On: Mar 24, 2017 | Last Updated: Jan 27, 2025 | Last Verified: Nov 05, 2024

Author: Sasank Chilamkurthy

In this tutorial, you will learn how to train a convolutional neural network for image classification using transfer learning. You can read more about the transfer learning at cs231n notes

Quoting these notes,

In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest.

These two major transfer learning scenarios look as follows:

• Finetuning the ConvNet: Instead of random initialization, we initialize the network with a pretrained network, like the one that is trained on imagenet 1000 dataset. Rest of the training looks as usual.

• ConvNet as fixed feature extractor: Here, we will freeze the weights for all of the network except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained.

# License: BSD
# Author: Sasank Chilamkurthy

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
from PIL import Image
from tempfile import TemporaryDirectory

cudnn.benchmark = True
plt.ion()   # interactive mode

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Load Data

We will use torchvision and torch.utils.data packages for loading the data.

The problem we’re going to solve today is to train a model to classify ants and bees. We have about 120 training images each for ants and bees. There are 75 validation images for each class. Usually, this is a very small dataset to generalize upon, if trained from scratch. Since we are using transfer learning, we should be able to generalize reasonably well.

This dataset is a very small subset of imagenet.

Note

Download the data from here and extract it to the current directory.

# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.

device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
print(f"Using {device} device")

Using cuda device

Visualize a few images

Let’s visualize a few training images so as to understand the data augmentations.

def imshow(inp, title=None):
    """Display image for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated


# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

imshow(out, title=[class_names[x] for x in classes])

Training the model

Now, let’s write a general function to train a model. Here, we will illustrate:

• Scheduling the learning rate

• Saving the best model

In the following, parameter scheduler is an LR scheduler object from torch.optim.lr_scheduler.

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    # Create a temporary directory to save training checkpoints
    with TemporaryDirectory() as tempdir:
        best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')

        torch.save(model.state_dict(), best_model_params_path)
        best_acc = 0.0

        for epoch in range(num_epochs):
            print(f'Epoch {epoch}/{num_epochs - 1}')
            print('-' * 10)

            # Each epoch has a training and validation phase
            for phase in ['train', 'val']:
                if phase == 'train':
                    model.train()  # Set model to training mode
                else:
                    model.eval()   # Set model to evaluate mode

                running_loss = 0.0
                running_corrects = 0

                # Iterate over data.
                for inputs, labels in dataloaders[phase]:
                    inputs = inputs.to(device)
                    labels = labels.to(device)

                    # zero the parameter gradients
                    optimizer.zero_grad()

                    # forward
                    # track history if only in train
                    with torch.set_grad_enabled(phase == 'train'):
                        outputs = model(inputs)
                        _, preds = torch.max(outputs, 1)
                        loss = criterion(outputs, labels)

                        # backward + optimize only if in training phase
                        if phase == 'train':
                            loss.backward()
                            optimizer.step()

                    # statistics
                    running_loss += loss.item() * inputs.size(0)
                    running_corrects += torch.sum(preds == labels.data)
                if phase == 'train':
                    scheduler.step()

                epoch_loss = running_loss / dataset_sizes[phase]
                epoch_acc = running_corrects.double() / dataset_sizes[phase]

                print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')

                # deep copy the model
                if phase == 'val' and epoch_acc > best_acc:
                    best_acc = epoch_acc
                    torch.save(model.state_dict(), best_model_params_path)

            print()

        time_elapsed = time.time() - since
        print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
        print(f'Best val Acc: {best_acc:4f}')

        # load best model weights
        model.load_state_dict(torch.load(best_model_params_path, weights_only=True))
    return model

Visualizing the model predictions

Generic function to display predictions for a few images

def visualize_model(model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 2, images_so_far)
                ax.axis('off')
                ax.set_title(f'predicted: {class_names[preds[j]]}')
                imshow(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)

Finetuning the ConvNet

Load a pretrained model and reset final fully connected layer.

model_ft = models.resnet18(weights='IMAGENET1K_V1')
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to ``nn.Linear(num_ftrs, len(class_names))``.
model_ft.fc = nn.Linear(num_ftrs, 2)

model_ft = model_ft.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth

  0%|          | 0.00/44.7M [00:00<?, ?B/s]
 82%|########1 | 36.5M/44.7M [00:00<00:00, 382MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 390MB/s]

Train and evaluate

It should take around 15-25 min on CPU. On GPU though, it takes less than a minute.

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                       num_epochs=25)

Epoch 0/24
----------
train Loss: 0.6422 Acc: 0.7131
val Loss: 0.2914 Acc: 0.8627

Epoch 1/24
----------
train Loss: 0.6245 Acc: 0.7582
val Loss: 0.4807 Acc: 0.8366

Epoch 2/24
----------
train Loss: 0.6308 Acc: 0.7746
val Loss: 0.2530 Acc: 0.8758

Epoch 3/24
----------
train Loss: 0.3927 Acc: 0.8443
val Loss: 0.2325 Acc: 0.9346

Epoch 4/24
----------
train Loss: 0.4391 Acc: 0.8402
val Loss: 0.3177 Acc: 0.8889

Epoch 5/24
----------
train Loss: 0.3749 Acc: 0.8525
val Loss: 0.2467 Acc: 0.9020

Epoch 6/24
----------
train Loss: 0.5919 Acc: 0.7623
val Loss: 0.8178 Acc: 0.7124

Epoch 7/24
----------
train Loss: 0.5150 Acc: 0.7992
val Loss: 0.3339 Acc: 0.8693

Epoch 8/24
----------
train Loss: 0.2950 Acc: 0.8730
val Loss: 0.3237 Acc: 0.8824

Epoch 9/24
----------
train Loss: 0.3629 Acc: 0.8525
val Loss: 0.2851 Acc: 0.9020

Epoch 10/24
----------
train Loss: 0.3240 Acc: 0.8730
val Loss: 0.2833 Acc: 0.8954

Epoch 11/24
----------
train Loss: 0.3123 Acc: 0.8484
val Loss: 0.2691 Acc: 0.9085

Epoch 12/24
----------
train Loss: 0.2865 Acc: 0.8852
val Loss: 0.2528 Acc: 0.9085

Epoch 13/24
----------
train Loss: 0.3350 Acc: 0.8689
val Loss: 0.2665 Acc: 0.9085

Epoch 14/24
----------
train Loss: 0.2864 Acc: 0.8730
val Loss: 0.2587 Acc: 0.9085

Epoch 15/24
----------
train Loss: 0.3075 Acc: 0.8648
val Loss: 0.2698 Acc: 0.9085

Epoch 16/24
----------
train Loss: 0.2830 Acc: 0.8811
val Loss: 0.2837 Acc: 0.9020

Epoch 17/24
----------
train Loss: 0.3064 Acc: 0.8730
val Loss: 0.2869 Acc: 0.8889

Epoch 18/24
----------
train Loss: 0.3082 Acc: 0.8934
val Loss: 0.2503 Acc: 0.9085

Epoch 19/24
----------
train Loss: 0.3475 Acc: 0.8361
val Loss: 0.3219 Acc: 0.8693

Epoch 20/24
----------
train Loss: 0.2595 Acc: 0.8893
val Loss: 0.2518 Acc: 0.9150

Epoch 21/24
----------
train Loss: 0.2599 Acc: 0.8852
val Loss: 0.2437 Acc: 0.9085

Epoch 22/24
----------
train Loss: 0.3348 Acc: 0.8607
val Loss: 0.2516 Acc: 0.9216

Epoch 23/24
----------
train Loss: 0.2578 Acc: 0.8811
val Loss: 0.2592 Acc: 0.9020

Epoch 24/24
----------
train Loss: 0.2024 Acc: 0.9180
val Loss: 0.2593 Acc: 0.8954

Training complete in 0m 34s
Best val Acc: 0.934641

visualize_model(model_ft)

ConvNet as fixed feature extractor

Here, we need to freeze all the network except the final layer. We need to set requires_grad = False to freeze the parameters so that the gradients are not computed in backward().

You can read more about this in the documentation here.

model_conv = torchvision.models.resnet18(weights='IMAGENET1K_V1')
for param in model_conv.parameters():
    param.requires_grad = False

# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)

model_conv = model_conv.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as
# opposed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)

Train and evaluate

On CPU this will take about half the time compared to previous scenario. This is expected as gradients don’t need to be computed for most of the network. However, forward does need to be computed.

model_conv = train_model(model_conv, criterion, optimizer_conv,
                         exp_lr_scheduler, num_epochs=25)

Epoch 0/24
----------
train Loss: 0.6113 Acc: 0.6803
val Loss: 0.4947 Acc: 0.7386

Epoch 1/24
----------
train Loss: 0.6724 Acc: 0.7172
val Loss: 0.3956 Acc: 0.8170

Epoch 2/24
----------
train Loss: 0.4843 Acc: 0.7951
val Loss: 0.2812 Acc: 0.8824

Epoch 3/24
----------
train Loss: 0.5234 Acc: 0.7787
val Loss: 0.1997 Acc: 0.9281

Epoch 4/24
----------
train Loss: 0.5225 Acc: 0.7828
val Loss: 0.2593 Acc: 0.9085

Epoch 5/24
----------
train Loss: 0.5597 Acc: 0.7910
val Loss: 0.2299 Acc: 0.9150

Epoch 6/24
----------
train Loss: 0.3251 Acc: 0.8402
val Loss: 0.3914 Acc: 0.8562

Epoch 7/24
----------
train Loss: 0.4029 Acc: 0.8484
val Loss: 0.2218 Acc: 0.9346

Epoch 8/24
----------
train Loss: 0.3081 Acc: 0.8811
val Loss: 0.2222 Acc: 0.9281

Epoch 9/24
----------
train Loss: 0.3979 Acc: 0.8197
val Loss: 0.1918 Acc: 0.9346

Epoch 10/24
----------
train Loss: 0.2638 Acc: 0.8811
val Loss: 0.2317 Acc: 0.9216

Epoch 11/24
----------
train Loss: 0.4074 Acc: 0.8197
val Loss: 0.2050 Acc: 0.9346

Epoch 12/24
----------
train Loss: 0.4025 Acc: 0.8361
val Loss: 0.1881 Acc: 0.9412

Epoch 13/24
----------
train Loss: 0.2949 Acc: 0.8934
val Loss: 0.2152 Acc: 0.9216

Epoch 14/24
----------
train Loss: 0.3770 Acc: 0.8566
val Loss: 0.1866 Acc: 0.9477

Epoch 15/24
----------
train Loss: 0.3157 Acc: 0.8689
val Loss: 0.1957 Acc: 0.9412

Epoch 16/24
----------
train Loss: 0.3921 Acc: 0.8320
val Loss: 0.1978 Acc: 0.9477

Epoch 17/24
----------
train Loss: 0.3623 Acc: 0.8361
val Loss: 0.2915 Acc: 0.8889

Epoch 18/24
----------
train Loss: 0.2907 Acc: 0.8770
val Loss: 0.1990 Acc: 0.9412

Epoch 19/24
----------
train Loss: 0.3149 Acc: 0.8607
val Loss: 0.1804 Acc: 0.9608

Epoch 20/24
----------
train Loss: 0.2382 Acc: 0.9180
val Loss: 0.2235 Acc: 0.9281

Epoch 21/24
----------
train Loss: 0.3444 Acc: 0.8484
val Loss: 0.1775 Acc: 0.9477

Epoch 22/24
----------
train Loss: 0.3096 Acc: 0.8730
val Loss: 0.2005 Acc: 0.9412

Epoch 23/24
----------
train Loss: 0.3018 Acc: 0.8484
val Loss: 0.1790 Acc: 0.9477

Epoch 24/24
----------
train Loss: 0.3685 Acc: 0.8525
val Loss: 0.1768 Acc: 0.9477

Training complete in 0m 27s
Best val Acc: 0.960784

visualize_model(model_conv)

plt.ioff()
plt.show()

Inference on custom images

Use the trained model to make predictions on custom images and visualize the predicted class labels along with the images.

def visualize_model_predictions(model,img_path):
    was_training = model.training
    model.eval()

    img = Image.open(img_path)
    img = data_transforms['val'](img)
    img = img.unsqueeze(0)
    img = img.to(device)

    with torch.no_grad():
        outputs = model(img)
        _, preds = torch.max(outputs, 1)

        ax = plt.subplot(2,2,1)
        ax.axis('off')
        ax.set_title(f'Predicted: {class_names[preds[0]]}')
        imshow(img.cpu().data[0])

        model.train(mode=was_training)

visualize_model_predictions(
    model_conv,
    img_path='data/hymenoptera_data/val/bees/72100438_73de9f17af.jpg'
)

plt.ioff()
plt.show()

Further Learning

If you would like to learn more about the applications of transfer learning, checkout our Quantized Transfer Learning for Computer Vision Tutorial.