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 cpu 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%|########2 | 36.8M/44.7M [00:00<00:00, 385MB/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: 0.5195 Acc: 0.7131
val Loss: 0.2958 Acc: 0.8693

Epoch 1/24
----------
train Loss: 0.5952 Acc: 0.7705
val Loss: 0.2038 Acc: 0.9412

Epoch 2/24
----------
train Loss: 0.5209 Acc: 0.7869
val Loss: 0.5844 Acc: 0.8301

Epoch 3/24
----------
train Loss: 0.6792 Acc: 0.7828
val Loss: 0.4606 Acc: 0.8497

Epoch 4/24
----------
train Loss: 0.6394 Acc: 0.7705
val Loss: 0.2001 Acc: 0.9346

Epoch 5/24
----------
train Loss: 0.5375 Acc: 0.7828
val Loss: 0.3369 Acc: 0.8889

Epoch 6/24
----------
train Loss: 0.6512 Acc: 0.7910
val Loss: 0.2547 Acc: 0.9085

Epoch 7/24
----------
train Loss: 0.3136 Acc: 0.8402
val Loss: 0.2262 Acc: 0.9085

Epoch 8/24
----------
train Loss: 0.3179 Acc: 0.8607
val Loss: 0.2482 Acc: 0.9020

Epoch 9/24
----------
train Loss: 0.2527 Acc: 0.8934
val Loss: 0.2028 Acc: 0.9216

Epoch 10/24
----------
train Loss: 0.3907 Acc: 0.8402
val Loss: 0.1922 Acc: 0.9346

Epoch 11/24
----------
train Loss: 0.2142 Acc: 0.9098
val Loss: 0.1969 Acc: 0.9346

Epoch 12/24
----------
train Loss: 0.2817 Acc: 0.8934
val Loss: 0.1999 Acc: 0.9412

Epoch 13/24
----------
train Loss: 0.3028 Acc: 0.8852
val Loss: 0.1995 Acc: 0.9346

Epoch 14/24
----------
train Loss: 0.3364 Acc: 0.8525
val Loss: 0.1948 Acc: 0.9346

Epoch 15/24
----------
train Loss: 0.3015 Acc: 0.8525
val Loss: 0.2063 Acc: 0.9150

Epoch 16/24
----------
train Loss: 0.3415 Acc: 0.8443
val Loss: 0.2055 Acc: 0.9412

Epoch 17/24
----------
train Loss: 0.2726 Acc: 0.8934
val Loss: 0.1817 Acc: 0.9412

Epoch 18/24
----------
train Loss: 0.2550 Acc: 0.8934
val Loss: 0.1927 Acc: 0.9412

Epoch 19/24
----------
train Loss: 0.3075 Acc: 0.8607
val Loss: 0.2169 Acc: 0.9150

Epoch 20/24
----------
train Loss: 0.2657 Acc: 0.8730
val Loss: 0.1932 Acc: 0.9412

Epoch 21/24
----------
train Loss: 0.2504 Acc: 0.9016
val Loss: 0.1827 Acc: 0.9477

Epoch 22/24
----------
train Loss: 0.2634 Acc: 0.8770
val Loss: 0.1829 Acc: 0.9412

Epoch 23/24
----------
train Loss: 0.2766 Acc: 0.8811
val Loss: 0.2275 Acc: 0.9150

Epoch 24/24
----------
train Loss: 0.1964 Acc: 0.9139
val Loss: 0.1898 Acc: 0.9542

Training complete in 4m 46s
Best val Acc: 0.954248

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: 0.5269 Acc: 0.7295
val Loss: 0.3301 Acc: 0.8627

Epoch 1/24
----------
train Loss: 0.4728 Acc: 0.7869
val Loss: 0.1882 Acc: 0.9346

Epoch 2/24
----------
train Loss: 0.5078 Acc: 0.7910
val Loss: 0.2336 Acc: 0.9085

Epoch 3/24
----------
train Loss: 0.4478 Acc: 0.7951
val Loss: 0.6025 Acc: 0.7843

Epoch 4/24
----------
train Loss: 0.5025 Acc: 0.7582
val Loss: 0.1724 Acc: 0.9477

Epoch 5/24
----------
train Loss: 0.4349 Acc: 0.7746
val Loss: 0.2675 Acc: 0.9020

Epoch 6/24
----------
train Loss: 0.4907 Acc: 0.8074
val Loss: 0.1820 Acc: 0.9412

Epoch 7/24
----------
train Loss: 0.3584 Acc: 0.8443
val Loss: 0.1775 Acc: 0.9477

Epoch 8/24
----------
train Loss: 0.3461 Acc: 0.8443
val Loss: 0.1663 Acc: 0.9412

Epoch 9/24
----------
train Loss: 0.3435 Acc: 0.8320
val Loss: 0.1812 Acc: 0.9542

Epoch 10/24
----------
train Loss: 0.3565 Acc: 0.8484
val Loss: 0.1746 Acc: 0.9542

Epoch 11/24
----------
train Loss: 0.3739 Acc: 0.8361
val Loss: 0.1782 Acc: 0.9346

Epoch 12/24
----------
train Loss: 0.3544 Acc: 0.8607
val Loss: 0.1979 Acc: 0.9346

Epoch 13/24
----------
train Loss: 0.4074 Acc: 0.8320
val Loss: 0.1600 Acc: 0.9412

Epoch 14/24
----------
train Loss: 0.3483 Acc: 0.8402
val Loss: 0.1785 Acc: 0.9281

Epoch 15/24
----------
train Loss: 0.3430 Acc: 0.8730
val Loss: 0.1811 Acc: 0.9477

Epoch 16/24
----------
train Loss: 0.3852 Acc: 0.8279
val Loss: 0.1902 Acc: 0.9477

Epoch 17/24
----------
train Loss: 0.4035 Acc: 0.7992
val Loss: 0.1599 Acc: 0.9412

Epoch 18/24
----------
train Loss: 0.3891 Acc: 0.8238
val Loss: 0.1866 Acc: 0.9477

Epoch 19/24
----------
train Loss: 0.3276 Acc: 0.8607
val Loss: 0.1816 Acc: 0.9412

Epoch 20/24
----------
train Loss: 0.3562 Acc: 0.8484
val Loss: 0.1835 Acc: 0.9412

Epoch 21/24
----------
train Loss: 0.2691 Acc: 0.8934
val Loss: 0.1633 Acc: 0.9477

Epoch 22/24
----------
train Loss: 0.3320 Acc: 0.8484
val Loss: 0.1932 Acc: 0.9346

Epoch 23/24
----------
train Loss: 0.3173 Acc: 0.8525
val Loss: 0.1837 Acc: 0.9412

Epoch 24/24
----------
train Loss: 0.3681 Acc: 0.8238
val Loss: 0.1849 Acc: 0.9412

Training complete in 2m 16s
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.