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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]
 93%|█████████▎| 41.6M/44.7M [00:00<00:00, 436MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 435MB/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: 0.6876 Acc: 0.6516
val Loss: 0.2167 Acc: 0.9216

Epoch 1/24
----------
train Loss: 0.6087 Acc: 0.7336
val Loss: 0.2261 Acc: 0.9085

Epoch 2/24
----------
train Loss: 0.5607 Acc: 0.8033
val Loss: 0.1920 Acc: 0.9412

Epoch 3/24
----------
train Loss: 0.5029 Acc: 0.8279
val Loss: 0.6307 Acc: 0.7908

Epoch 4/24
----------
train Loss: 0.5186 Acc: 0.7951
val Loss: 0.3852 Acc: 0.8431

Epoch 5/24
----------
train Loss: 0.5697 Acc: 0.7664
val Loss: 0.2862 Acc: 0.8889

Epoch 6/24
----------
train Loss: 0.4943 Acc: 0.8279
val Loss: 0.6180 Acc: 0.7908

Epoch 7/24
----------
train Loss: 0.4483 Acc: 0.8402
val Loss: 0.3981 Acc: 0.8431

Epoch 8/24
----------
train Loss: 0.4108 Acc: 0.8730
val Loss: 0.3314 Acc: 0.8758

Epoch 9/24
----------
train Loss: 0.3041 Acc: 0.8689
val Loss: 0.3008 Acc: 0.9020

Epoch 10/24
----------
train Loss: 0.4690 Acc: 0.8279
val Loss: 0.3462 Acc: 0.8824

Epoch 11/24
----------
train Loss: 0.2346 Acc: 0.9057
val Loss: 0.3523 Acc: 0.8627

Epoch 12/24
----------
train Loss: 0.3772 Acc: 0.8566
val Loss: 0.2681 Acc: 0.9150

Epoch 13/24
----------
train Loss: 0.3624 Acc: 0.8402
val Loss: 0.2949 Acc: 0.9020

Epoch 14/24
----------
train Loss: 0.2796 Acc: 0.8770
val Loss: 0.2892 Acc: 0.9150

Epoch 15/24
----------
train Loss: 0.2598 Acc: 0.8893
val Loss: 0.2968 Acc: 0.8824

Epoch 16/24
----------
train Loss: 0.3036 Acc: 0.8770
val Loss: 0.2869 Acc: 0.9085

Epoch 17/24
----------
train Loss: 0.2900 Acc: 0.8730
val Loss: 0.3072 Acc: 0.8758

Epoch 18/24
----------
train Loss: 0.3374 Acc: 0.8607
val Loss: 0.2777 Acc: 0.9085

Epoch 19/24
----------
train Loss: 0.1955 Acc: 0.9426
val Loss: 0.2772 Acc: 0.9020

Epoch 20/24
----------
train Loss: 0.2323 Acc: 0.8852
val Loss: 0.2628 Acc: 0.9150

Epoch 21/24
----------
train Loss: 0.2843 Acc: 0.8811
val Loss: 0.2867 Acc: 0.9216

Epoch 22/24
----------
train Loss: 0.3526 Acc: 0.8566
val Loss: 0.3129 Acc: 0.8954

Epoch 23/24
----------
train Loss: 0.3484 Acc: 0.8525
val Loss: 0.2808 Acc: 0.9216

Epoch 24/24
----------
train Loss: 0.2506 Acc: 0.9057
val Loss: 0.2959 Acc: 0.8824

Training complete in 0m 38s
Best val Acc: 0.941176

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: 0.5508 Acc: 0.7008
val Loss: 0.6137 Acc: 0.6732

Epoch 1/24
----------
train Loss: 0.5485 Acc: 0.7828
val Loss: 0.1771 Acc: 0.9608

Epoch 2/24
----------
train Loss: 0.3960 Acc: 0.7992
val Loss: 0.1834 Acc: 0.9542

Epoch 3/24
----------
train Loss: 0.4546 Acc: 0.7951
val Loss: 0.1763 Acc: 0.9542

Epoch 4/24
----------
train Loss: 0.5309 Acc: 0.7541
val Loss: 0.2239 Acc: 0.9216

Epoch 5/24
----------
train Loss: 0.4699 Acc: 0.8279
val Loss: 0.1643 Acc: 0.9542

Epoch 6/24
----------
train Loss: 0.4172 Acc: 0.8115
val Loss: 0.1801 Acc: 0.9542

Epoch 7/24
----------
train Loss: 0.3311 Acc: 0.8443
val Loss: 0.1746 Acc: 0.9542

Epoch 8/24
----------
train Loss: 0.2895 Acc: 0.8811
val Loss: 0.1981 Acc: 0.9412

Epoch 9/24
----------
train Loss: 0.3244 Acc: 0.8361
val Loss: 0.2076 Acc: 0.9412

Epoch 10/24
----------
train Loss: 0.2859 Acc: 0.8689
val Loss: 0.1810 Acc: 0.9477

Epoch 11/24
----------
train Loss: 0.3884 Acc: 0.8279
val Loss: 0.2366 Acc: 0.9346

Epoch 12/24
----------
train Loss: 0.2616 Acc: 0.8730
val Loss: 0.2082 Acc: 0.9281

Epoch 13/24
----------
train Loss: 0.3277 Acc: 0.8648
val Loss: 0.1605 Acc: 0.9542

Epoch 14/24
----------
train Loss: 0.3534 Acc: 0.8566
val Loss: 0.1625 Acc: 0.9608

Epoch 15/24
----------
train Loss: 0.2948 Acc: 0.8525
val Loss: 0.1813 Acc: 0.9542

Epoch 16/24
----------
train Loss: 0.3075 Acc: 0.8525
val Loss: 0.1810 Acc: 0.9412

Epoch 17/24
----------
train Loss: 0.3419 Acc: 0.8402
val Loss: 0.1695 Acc: 0.9542

Epoch 18/24
----------
train Loss: 0.3013 Acc: 0.8607
val Loss: 0.1760 Acc: 0.9542

Epoch 19/24
----------
train Loss: 0.3232 Acc: 0.8689
val Loss: 0.1785 Acc: 0.9477

Epoch 20/24
----------
train Loss: 0.3151 Acc: 0.8730
val Loss: 0.1760 Acc: 0.9542

Epoch 21/24
----------
train Loss: 0.2950 Acc: 0.8648
val Loss: 0.1855 Acc: 0.9542

Epoch 22/24
----------
train Loss: 0.3288 Acc: 0.8770
val Loss: 0.1769 Acc: 0.9542

Epoch 23/24
----------
train Loss: 0.2598 Acc: 0.8730
val Loss: 0.1899 Acc: 0.9477

Epoch 24/24
----------
train Loss: 0.2699 Acc: 0.8852
val Loss: 0.1871 Acc: 0.9542

Training complete in 0m 29s
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.