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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 86%|########6 | 38.6M/44.7M [00:00<00:00, 404MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 407MB/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: 0.6218 Acc: 0.6926
val Loss: 0.3464 Acc: 0.8758

Epoch 1/24
----------
train Loss: 0.5487 Acc: 0.7869
val Loss: 0.1855 Acc: 0.9216

Epoch 2/24
----------
train Loss: 0.4013 Acc: 0.8156
val Loss: 0.1497 Acc: 0.9477

Epoch 3/24
----------
train Loss: 0.4881 Acc: 0.8115
val Loss: 0.4030 Acc: 0.8693

Epoch 4/24
----------
train Loss: 0.5914 Acc: 0.7664
val Loss: 0.2261 Acc: 0.9216

Epoch 5/24
----------
train Loss: 0.5291 Acc: 0.7869
val Loss: 0.5779 Acc: 0.8039

Epoch 6/24
----------
train Loss: 0.3918 Acc: 0.8320
val Loss: 0.2220 Acc: 0.9346

Epoch 7/24
----------
train Loss: 0.3697 Acc: 0.8893
val Loss: 0.2451 Acc: 0.9216

Epoch 8/24
----------
train Loss: 0.2350 Acc: 0.9016
val Loss: 0.1990 Acc: 0.9412

Epoch 9/24
----------
train Loss: 0.2775 Acc: 0.8934
val Loss: 0.2026 Acc: 0.9346

Epoch 10/24
----------
train Loss: 0.2171 Acc: 0.9303
val Loss: 0.2016 Acc: 0.9281

Epoch 11/24
----------
train Loss: 0.3634 Acc: 0.8730
val Loss: 0.2475 Acc: 0.9020

Epoch 12/24
----------
train Loss: 0.3298 Acc: 0.8770
val Loss: 0.2053 Acc: 0.9281

Epoch 13/24
----------
train Loss: 0.3311 Acc: 0.8484
val Loss: 0.1840 Acc: 0.9346

Epoch 14/24
----------
train Loss: 0.2481 Acc: 0.8893
val Loss: 0.1756 Acc: 0.9281

Epoch 15/24
----------
train Loss: 0.2191 Acc: 0.8975
val Loss: 0.1920 Acc: 0.9216

Epoch 16/24
----------
train Loss: 0.2893 Acc: 0.8566
val Loss: 0.2052 Acc: 0.9150

Epoch 17/24
----------
train Loss: 0.3362 Acc: 0.8525
val Loss: 0.1913 Acc: 0.9281

Epoch 18/24
----------
train Loss: 0.3063 Acc: 0.8730
val Loss: 0.1765 Acc: 0.9412

Epoch 19/24
----------
train Loss: 0.2999 Acc: 0.8730
val Loss: 0.2120 Acc: 0.9150

Epoch 20/24
----------
train Loss: 0.2937 Acc: 0.8852
val Loss: 0.1975 Acc: 0.9281

Epoch 21/24
----------
train Loss: 0.2352 Acc: 0.9057
val Loss: 0.1825 Acc: 0.9281

Epoch 22/24
----------
train Loss: 0.3455 Acc: 0.8484
val Loss: 0.1682 Acc: 0.9412

Epoch 23/24
----------
train Loss: 0.2991 Acc: 0.8770
val Loss: 0.1842 Acc: 0.9216

Epoch 24/24
----------
train Loss: 0.2255 Acc: 0.9139
val Loss: 0.1965 Acc: 0.9150

Training complete in 0m 36s
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: 0.6561 Acc: 0.6680
val Loss: 0.4757 Acc: 0.7908

Epoch 1/24
----------
train Loss: 0.5143 Acc: 0.7705
val Loss: 0.3296 Acc: 0.8627

Epoch 2/24
----------
train Loss: 0.5264 Acc: 0.7541
val Loss: 0.3223 Acc: 0.8497

Epoch 3/24
----------
train Loss: 0.3307 Acc: 0.8566
val Loss: 0.1926 Acc: 0.9542

Epoch 4/24
----------
train Loss: 0.5105 Acc: 0.7869
val Loss: 0.1961 Acc: 0.9346

Epoch 5/24
----------
train Loss: 0.3560 Acc: 0.8525
val Loss: 0.2017 Acc: 0.9477

Epoch 6/24
----------
train Loss: 0.4767 Acc: 0.7992
val Loss: 0.2807 Acc: 0.9020

Epoch 7/24
----------
train Loss: 0.3991 Acc: 0.8525
val Loss: 0.1893 Acc: 0.9542

Epoch 8/24
----------
train Loss: 0.3278 Acc: 0.8852
val Loss: 0.2009 Acc: 0.9477

Epoch 9/24
----------
train Loss: 0.3477 Acc: 0.8443
val Loss: 0.2094 Acc: 0.9542

Epoch 10/24
----------
train Loss: 0.3540 Acc: 0.8566
val Loss: 0.2008 Acc: 0.9477

Epoch 11/24
----------
train Loss: 0.3399 Acc: 0.8566
val Loss: 0.1956 Acc: 0.9542

Epoch 12/24
----------
train Loss: 0.3787 Acc: 0.8402
val Loss: 0.1913 Acc: 0.9477

Epoch 13/24
----------
train Loss: 0.3322 Acc: 0.8484
val Loss: 0.1921 Acc: 0.9412

Epoch 14/24
----------
train Loss: 0.3780 Acc: 0.8320
val Loss: 0.1977 Acc: 0.9542

Epoch 15/24
----------
train Loss: 0.3966 Acc: 0.8279
val Loss: 0.2176 Acc: 0.9281

Epoch 16/24
----------
train Loss: 0.3967 Acc: 0.8156
val Loss: 0.2143 Acc: 0.9216

Epoch 17/24
----------
train Loss: 0.3896 Acc: 0.8238
val Loss: 0.1923 Acc: 0.9477

Epoch 18/24
----------
train Loss: 0.3369 Acc: 0.8484
val Loss: 0.2020 Acc: 0.9477

Epoch 19/24
----------
train Loss: 0.3962 Acc: 0.8279
val Loss: 0.2015 Acc: 0.9477

Epoch 20/24
----------
train Loss: 0.3681 Acc: 0.8115
val Loss: 0.2243 Acc: 0.9346

Epoch 21/24
----------
train Loss: 0.3154 Acc: 0.8770
val Loss: 0.1939 Acc: 0.9477

Epoch 22/24
----------
train Loss: 0.3628 Acc: 0.8566
val Loss: 0.2177 Acc: 0.9477

Epoch 23/24
----------
train Loss: 0.2745 Acc: 0.8934
val Loss: 0.1939 Acc: 0.9542

Epoch 24/24
----------
train Loss: 0.3598 Acc: 0.8320
val Loss: 0.2129 Acc: 0.9477

Training complete in 0m 27s
Best val Acc: 0.954248

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.