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

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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: 0.4483 Acc: 0.7828
val Loss: 0.2991 Acc: 0.8889

Epoch 1/24
----------
train Loss: 0.5926 Acc: 0.7828
val Loss: 0.2619 Acc: 0.9281

Epoch 2/24
----------
train Loss: 0.6120 Acc: 0.7131
val Loss: 0.4170 Acc: 0.8366

Epoch 3/24
----------
train Loss: 0.4715 Acc: 0.7992
val Loss: 0.5719 Acc: 0.7712

Epoch 4/24
----------
train Loss: 0.4896 Acc: 0.7992
val Loss: 0.2774 Acc: 0.8889

Epoch 5/24
----------
train Loss: 0.4778 Acc: 0.8320
val Loss: 0.2282 Acc: 0.9085

Epoch 6/24
----------
train Loss: 0.6263 Acc: 0.7336
val Loss: 0.2868 Acc: 0.8954

Epoch 7/24
----------
train Loss: 0.3864 Acc: 0.8525
val Loss: 0.2228 Acc: 0.9281

Epoch 8/24
----------
train Loss: 0.3399 Acc: 0.8566
val Loss: 0.2050 Acc: 0.9281

Epoch 9/24
----------
train Loss: 0.3705 Acc: 0.8484
val Loss: 0.2040 Acc: 0.9346

Epoch 10/24
----------
train Loss: 0.3240 Acc: 0.8852
val Loss: 0.1895 Acc: 0.9412

Epoch 11/24
----------
train Loss: 0.2804 Acc: 0.8811
val Loss: 0.1997 Acc: 0.9346

Epoch 12/24
----------
train Loss: 0.3238 Acc: 0.8443
val Loss: 0.2189 Acc: 0.9150

Epoch 13/24
----------
train Loss: 0.2699 Acc: 0.8852
val Loss: 0.1920 Acc: 0.9346

Epoch 14/24
----------
train Loss: 0.2377 Acc: 0.9139
val Loss: 0.1913 Acc: 0.9346

Epoch 15/24
----------
train Loss: 0.2558 Acc: 0.8975
val Loss: 0.1832 Acc: 0.9216

Epoch 16/24
----------
train Loss: 0.2596 Acc: 0.8893
val Loss: 0.1876 Acc: 0.9412

Epoch 17/24
----------
train Loss: 0.2764 Acc: 0.8975
val Loss: 0.1832 Acc: 0.9281

Epoch 18/24
----------
train Loss: 0.3359 Acc: 0.8730
val Loss: 0.2057 Acc: 0.9346

Epoch 19/24
----------
train Loss: 0.2949 Acc: 0.8852
val Loss: 0.1906 Acc: 0.9412

Epoch 20/24
----------
train Loss: 0.2622 Acc: 0.8811
val Loss: 0.1875 Acc: 0.9477

Epoch 21/24
----------
train Loss: 0.3188 Acc: 0.8566
val Loss: 0.1809 Acc: 0.9346

Epoch 22/24
----------
train Loss: 0.3029 Acc: 0.8648
val Loss: 0.1911 Acc: 0.9412

Epoch 23/24
----------
train Loss: 0.2779 Acc: 0.8811
val Loss: 0.1886 Acc: 0.9346

Epoch 24/24
----------
train Loss: 0.2733 Acc: 0.8852
val Loss: 0.1938 Acc: 0.9477

Training complete in 0m 35s
Best val Acc: 0.947712

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: 0.5730 Acc: 0.6803
val Loss: 0.2842 Acc: 0.8693

Epoch 1/24
----------
train Loss: 0.5364 Acc: 0.7336
val Loss: 0.2228 Acc: 0.9346

Epoch 2/24
----------
train Loss: 0.6411 Acc: 0.7213
val Loss: 0.2771 Acc: 0.9085

Epoch 3/24
----------
train Loss: 0.5435 Acc: 0.7787
val Loss: 0.1731 Acc: 0.9542

Epoch 4/24
----------
train Loss: 0.4399 Acc: 0.8197
val Loss: 0.3667 Acc: 0.8366

Epoch 5/24
----------
train Loss: 0.5900 Acc: 0.7582
val Loss: 0.2571 Acc: 0.9216

Epoch 6/24
----------
train Loss: 0.5038 Acc: 0.7992
val Loss: 0.1987 Acc: 0.9608

Epoch 7/24
----------
train Loss: 0.3947 Acc: 0.8525
val Loss: 0.2360 Acc: 0.9412

Epoch 8/24
----------
train Loss: 0.3478 Acc: 0.8566
val Loss: 0.1873 Acc: 0.9477

Epoch 9/24
----------
train Loss: 0.3453 Acc: 0.8566
val Loss: 0.1929 Acc: 0.9542

Epoch 10/24
----------
train Loss: 0.3368 Acc: 0.8525
val Loss: 0.1933 Acc: 0.9542

Epoch 11/24
----------
train Loss: 0.3381 Acc: 0.8607
val Loss: 0.1820 Acc: 0.9542

Epoch 12/24
----------
train Loss: 0.3195 Acc: 0.8525
val Loss: 0.1764 Acc: 0.9608

Epoch 13/24
----------
train Loss: 0.4168 Acc: 0.8443
val Loss: 0.1920 Acc: 0.9542

Epoch 14/24
----------
train Loss: 0.3497 Acc: 0.8566
val Loss: 0.2549 Acc: 0.9281

Epoch 15/24
----------
train Loss: 0.2990 Acc: 0.8484
val Loss: 0.2058 Acc: 0.9346

Epoch 16/24
----------
train Loss: 0.3716 Acc: 0.8115
val Loss: 0.2163 Acc: 0.9412

Epoch 17/24
----------
train Loss: 0.2838 Acc: 0.8893
val Loss: 0.1750 Acc: 0.9477

Epoch 18/24
----------
train Loss: 0.3288 Acc: 0.8484
val Loss: 0.1792 Acc: 0.9608

Epoch 19/24
----------
train Loss: 0.3128 Acc: 0.8893
val Loss: 0.1772 Acc: 0.9542

Epoch 20/24
----------
train Loss: 0.3618 Acc: 0.8484
val Loss: 0.1955 Acc: 0.9542

Epoch 21/24
----------
train Loss: 0.2813 Acc: 0.8893
val Loss: 0.1843 Acc: 0.9542

Epoch 22/24
----------
train Loss: 0.2531 Acc: 0.8934
val Loss: 0.1843 Acc: 0.9542

Epoch 23/24
----------
train Loss: 0.2864 Acc: 0.8811
val Loss: 0.1782 Acc: 0.9477

Epoch 24/24
----------
train Loss: 0.3080 Acc: 0.8811
val Loss: 0.1794 Acc: 0.9542

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.