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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
<contextlib.ExitStack object at 0x7f98821a45e0>

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])
['ants', 'bees', 'bees', 'ants']

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]
 88%|########8 | 39.5M/44.7M [00:00<00:00, 414MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 415MB/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: 0.5415 Acc: 0.7418
val Loss: 0.2128 Acc: 0.9281

Epoch 1/24
----------
train Loss: 0.5028 Acc: 0.7787
val Loss: 0.4516 Acc: 0.8235

Epoch 2/24
----------
train Loss: 0.7671 Acc: 0.7254
val Loss: 0.1876 Acc: 0.9477

Epoch 3/24
----------
train Loss: 0.4618 Acc: 0.8156
val Loss: 0.5527 Acc: 0.7778

Epoch 4/24
----------
train Loss: 0.3962 Acc: 0.8156
val Loss: 0.1940 Acc: 0.9346

Epoch 5/24
----------
train Loss: 0.4974 Acc: 0.7951
val Loss: 0.2170 Acc: 0.9281

Epoch 6/24
----------
train Loss: 0.4857 Acc: 0.7746
val Loss: 0.2192 Acc: 0.9216

Epoch 7/24
----------
train Loss: 0.2680 Acc: 0.8934
val Loss: 0.1946 Acc: 0.9150

Epoch 8/24
----------
train Loss: 0.4014 Acc: 0.8402
val Loss: 0.1807 Acc: 0.9281

Epoch 9/24
----------
train Loss: 0.2858 Acc: 0.8811
val Loss: 0.1940 Acc: 0.9150

Epoch 10/24
----------
train Loss: 0.3770 Acc: 0.8197
val Loss: 0.2452 Acc: 0.9150

Epoch 11/24
----------
train Loss: 0.3201 Acc: 0.8566
val Loss: 0.1855 Acc: 0.9346

Epoch 12/24
----------
train Loss: 0.3132 Acc: 0.8730
val Loss: 0.1871 Acc: 0.9216

Epoch 13/24
----------
train Loss: 0.2619 Acc: 0.8852
val Loss: 0.1720 Acc: 0.9281

Epoch 14/24
----------
train Loss: 0.2290 Acc: 0.9016
val Loss: 0.1781 Acc: 0.9281

Epoch 15/24
----------
train Loss: 0.3000 Acc: 0.8770
val Loss: 0.1786 Acc: 0.9542

Epoch 16/24
----------
train Loss: 0.1665 Acc: 0.9549
val Loss: 0.1725 Acc: 0.9346

Epoch 17/24
----------
train Loss: 0.2258 Acc: 0.9057
val Loss: 0.1726 Acc: 0.9346

Epoch 18/24
----------
train Loss: 0.3041 Acc: 0.8648
val Loss: 0.1778 Acc: 0.9216

Epoch 19/24
----------
train Loss: 0.2456 Acc: 0.9139
val Loss: 0.1840 Acc: 0.9216

Epoch 20/24
----------
train Loss: 0.2714 Acc: 0.8852
val Loss: 0.1854 Acc: 0.9281

Epoch 21/24
----------
train Loss: 0.3034 Acc: 0.8689
val Loss: 0.1767 Acc: 0.9281

Epoch 22/24
----------
train Loss: 0.3413 Acc: 0.8648
val Loss: 0.1760 Acc: 0.9281

Epoch 23/24
----------
train Loss: 0.3145 Acc: 0.8893
val Loss: 0.1849 Acc: 0.9412

Epoch 24/24
----------
train Loss: 0.2270 Acc: 0.9180
val Loss: 0.1748 Acc: 0.9346

Training complete in 0m 35s
Best val Acc: 0.954248
visualize_model(model_ft)
predicted: ants, predicted: ants, predicted: ants, predicted: bees, predicted: ants, predicted: ants

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: 0.6382 Acc: 0.6967
val Loss: 0.2202 Acc: 0.9085

Epoch 1/24
----------
train Loss: 0.4366 Acc: 0.8033
val Loss: 0.3129 Acc: 0.8562

Epoch 2/24
----------
train Loss: 0.4774 Acc: 0.7951
val Loss: 0.3238 Acc: 0.8693

Epoch 3/24
----------
train Loss: 0.5183 Acc: 0.7664
val Loss: 0.2770 Acc: 0.8954

Epoch 4/24
----------
train Loss: 0.4923 Acc: 0.7705
val Loss: 0.1775 Acc: 0.9346

Epoch 5/24
----------
train Loss: 0.3740 Acc: 0.8525
val Loss: 0.1809 Acc: 0.9477

Epoch 6/24
----------
train Loss: 0.4162 Acc: 0.8443
val Loss: 0.2364 Acc: 0.9281

Epoch 7/24
----------
train Loss: 0.3546 Acc: 0.8607
val Loss: 0.1915 Acc: 0.9412

Epoch 8/24
----------
train Loss: 0.3522 Acc: 0.8402
val Loss: 0.1860 Acc: 0.9477

Epoch 9/24
----------
train Loss: 0.4244 Acc: 0.8279
val Loss: 0.2172 Acc: 0.9281

Epoch 10/24
----------
train Loss: 0.3660 Acc: 0.8525
val Loss: 0.2034 Acc: 0.9477

Epoch 11/24
----------
train Loss: 0.3069 Acc: 0.8525
val Loss: 0.2174 Acc: 0.9216

Epoch 12/24
----------
train Loss: 0.2625 Acc: 0.8934
val Loss: 0.1836 Acc: 0.9412

Epoch 13/24
----------
train Loss: 0.3361 Acc: 0.8566
val Loss: 0.1939 Acc: 0.9542

Epoch 14/24
----------
train Loss: 0.3465 Acc: 0.8484
val Loss: 0.1809 Acc: 0.9542

Epoch 15/24
----------
train Loss: 0.2952 Acc: 0.8689
val Loss: 0.2085 Acc: 0.9281

Epoch 16/24
----------
train Loss: 0.3063 Acc: 0.8811
val Loss: 0.1769 Acc: 0.9542

Epoch 17/24
----------
train Loss: 0.3667 Acc: 0.8484
val Loss: 0.1860 Acc: 0.9477

Epoch 18/24
----------
train Loss: 0.3548 Acc: 0.8320
val Loss: 0.1815 Acc: 0.9412

Epoch 19/24
----------
train Loss: 0.2830 Acc: 0.8566
val Loss: 0.1972 Acc: 0.9477

Epoch 20/24
----------
train Loss: 0.2709 Acc: 0.8770
val Loss: 0.1955 Acc: 0.9346

Epoch 21/24
----------
train Loss: 0.3727 Acc: 0.8484
val Loss: 0.1840 Acc: 0.9542

Epoch 22/24
----------
train Loss: 0.3630 Acc: 0.8279
val Loss: 0.1791 Acc: 0.9542

Epoch 23/24
----------
train Loss: 0.3171 Acc: 0.8689
val Loss: 0.2002 Acc: 0.9412

Epoch 24/24
----------
train Loss: 0.3524 Acc: 0.8402
val Loss: 0.1895 Acc: 0.9477

Training complete in 0m 28s
Best val Acc: 0.954248
visualize_model(model_conv)

plt.ioff()
plt.show()
predicted: ants, predicted: ants, predicted: bees, predicted: ants, predicted: ants, predicted: ants

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()
Predicted: bees

Further Learning#

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

Total running time of the script: ( 1 minutes 4.527 seconds)

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