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]
 81%|████████  | 36.0M/44.7M [00:00<00:00, 375MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 369MB/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: 0.5594 Acc: 0.7254
val Loss: 0.2481 Acc: 0.9020

Epoch 1/24
----------
train Loss: 0.5451 Acc: 0.8074
val Loss: 0.5673 Acc: 0.7647

Epoch 2/24
----------
train Loss: 0.5334 Acc: 0.8033
val Loss: 0.3431 Acc: 0.8824

Epoch 3/24
----------
train Loss: 0.4767 Acc: 0.8115
val Loss: 0.3264 Acc: 0.8889

Epoch 4/24
----------
train Loss: 0.4601 Acc: 0.7828
val Loss: 0.2225 Acc: 0.9412

Epoch 5/24
----------
train Loss: 0.3765 Acc: 0.8484
val Loss: 0.3174 Acc: 0.8627

Epoch 6/24
----------
train Loss: 0.4425 Acc: 0.8115
val Loss: 0.2693 Acc: 0.8889

Epoch 7/24
----------
train Loss: 0.3136 Acc: 0.8566
val Loss: 0.2286 Acc: 0.9412

Epoch 8/24
----------
train Loss: 0.3313 Acc: 0.8525
val Loss: 0.2050 Acc: 0.9412

Epoch 9/24
----------
train Loss: 0.2889 Acc: 0.8525
val Loss: 0.2111 Acc: 0.9412

Epoch 10/24
----------
train Loss: 0.3741 Acc: 0.8238
val Loss: 0.2257 Acc: 0.9281

Epoch 11/24
----------
train Loss: 0.2514 Acc: 0.8934
val Loss: 0.2240 Acc: 0.9281

Epoch 12/24
----------
train Loss: 0.3438 Acc: 0.8525
val Loss: 0.2083 Acc: 0.9346

Epoch 13/24
----------
train Loss: 0.3936 Acc: 0.8484
val Loss: 0.2204 Acc: 0.9216

Epoch 14/24
----------
train Loss: 0.2552 Acc: 0.8852
val Loss: 0.2001 Acc: 0.9542

Epoch 15/24
----------
train Loss: 0.2425 Acc: 0.8975
val Loss: 0.2192 Acc: 0.9150

Epoch 16/24
----------
train Loss: 0.2586 Acc: 0.8975
val Loss: 0.2210 Acc: 0.9216

Epoch 17/24
----------
train Loss: 0.2012 Acc: 0.9139
val Loss: 0.2112 Acc: 0.9085

Epoch 18/24
----------
train Loss: 0.1865 Acc: 0.9344
val Loss: 0.2172 Acc: 0.9281

Epoch 19/24
----------
train Loss: 0.2691 Acc: 0.8811
val Loss: 0.2005 Acc: 0.9346

Epoch 20/24
----------
train Loss: 0.3122 Acc: 0.8566
val Loss: 0.2282 Acc: 0.9216

Epoch 21/24
----------
train Loss: 0.2144 Acc: 0.9098
val Loss: 0.2126 Acc: 0.9346

Epoch 22/24
----------
train Loss: 0.2392 Acc: 0.9016
val Loss: 0.1957 Acc: 0.9542

Epoch 23/24
----------
train Loss: 0.2739 Acc: 0.8566
val Loss: 0.2079 Acc: 0.9477

Epoch 24/24
----------
train Loss: 0.2522 Acc: 0.8852
val Loss: 0.1986 Acc: 0.9477

Training complete in 0m 34s
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: 0.9500 Acc: 0.5738
val Loss: 1.1426 Acc: 0.5294

Epoch 1/24
----------
train Loss: 0.5411 Acc: 0.7787
val Loss: 0.1943 Acc: 0.9477

Epoch 2/24
----------
train Loss: 0.5243 Acc: 0.7869
val Loss: 0.3216 Acc: 0.8824

Epoch 3/24
----------
train Loss: 0.6095 Acc: 0.7541
val Loss: 0.2284 Acc: 0.9216

Epoch 4/24
----------
train Loss: 0.4528 Acc: 0.8115
val Loss: 0.1691 Acc: 0.9542

Epoch 5/24
----------
train Loss: 0.4546 Acc: 0.8115
val Loss: 0.1728 Acc: 0.9477

Epoch 6/24
----------
train Loss: 0.4183 Acc: 0.8074
val Loss: 0.4620 Acc: 0.8497

Epoch 7/24
----------
train Loss: 0.4638 Acc: 0.8074
val Loss: 0.2249 Acc: 0.9216

Epoch 8/24
----------
train Loss: 0.3140 Acc: 0.8648
val Loss: 0.2377 Acc: 0.9020

Epoch 9/24
----------
train Loss: 0.3540 Acc: 0.8525
val Loss: 0.2209 Acc: 0.9281

Epoch 10/24
----------
train Loss: 0.3057 Acc: 0.8689
val Loss: 0.1856 Acc: 0.9477

Epoch 11/24
----------
train Loss: 0.3752 Acc: 0.8074
val Loss: 0.2368 Acc: 0.9281

Epoch 12/24
----------
train Loss: 0.2737 Acc: 0.8770
val Loss: 0.2024 Acc: 0.9412

Epoch 13/24
----------
train Loss: 0.3315 Acc: 0.8730
val Loss: 0.1906 Acc: 0.9477

Epoch 14/24
----------
train Loss: 0.4263 Acc: 0.8156
val Loss: 0.2175 Acc: 0.9281

Epoch 15/24
----------
train Loss: 0.3743 Acc: 0.8238
val Loss: 0.2057 Acc: 0.9412

Epoch 16/24
----------
train Loss: 0.3784 Acc: 0.8279
val Loss: 0.2270 Acc: 0.9346

Epoch 17/24
----------
train Loss: 0.3382 Acc: 0.8525
val Loss: 0.2136 Acc: 0.9346

Epoch 18/24
----------
train Loss: 0.3874 Acc: 0.8525
val Loss: 0.1902 Acc: 0.9477

Epoch 19/24
----------
train Loss: 0.3405 Acc: 0.8238
val Loss: 0.1935 Acc: 0.9542

Epoch 20/24
----------
train Loss: 0.3387 Acc: 0.8361
val Loss: 0.2465 Acc: 0.9281

Epoch 21/24
----------
train Loss: 0.3141 Acc: 0.8648
val Loss: 0.2071 Acc: 0.9216

Epoch 22/24
----------
train Loss: 0.3089 Acc: 0.8689
val Loss: 0.2455 Acc: 0.9216

Epoch 23/24
----------
train Loss: 0.2944 Acc: 0.8607
val Loss: 0.2271 Acc: 0.9150

Epoch 24/24
----------
train Loss: 0.2828 Acc: 0.8811
val Loss: 0.2269 Acc: 0.9281

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.