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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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: 0.5258 Acc: 0.6844
val Loss: 0.2126 Acc: 0.9412

Epoch 1/24
----------
train Loss: 0.5213 Acc: 0.7951
val Loss: 0.2353 Acc: 0.9085

Epoch 2/24
----------
train Loss: 0.5678 Acc: 0.7664
val Loss: 0.2952 Acc: 0.8824

Epoch 3/24
----------
train Loss: 0.7332 Acc: 0.7377
val Loss: 0.5259 Acc: 0.8235

Epoch 4/24
----------
train Loss: 0.6601 Acc: 0.7664
val Loss: 0.2725 Acc: 0.9085

Epoch 5/24
----------
train Loss: 0.6248 Acc: 0.7705
val Loss: 0.3625 Acc: 0.8562

Epoch 6/24
----------
train Loss: 0.5097 Acc: 0.7828
val Loss: 0.2591 Acc: 0.8824

Epoch 7/24
----------
train Loss: 0.3782 Acc: 0.8484
val Loss: 0.2304 Acc: 0.9216

Epoch 8/24
----------
train Loss: 0.2927 Acc: 0.8689
val Loss: 0.2114 Acc: 0.9216

Epoch 9/24
----------
train Loss: 0.2640 Acc: 0.8975
val Loss: 0.2387 Acc: 0.9281

Epoch 10/24
----------
train Loss: 0.2609 Acc: 0.8770
val Loss: 0.2199 Acc: 0.9281

Epoch 11/24
----------
train Loss: 0.3431 Acc: 0.8361
val Loss: 0.2352 Acc: 0.9346

Epoch 12/24
----------
train Loss: 0.2362 Acc: 0.8975
val Loss: 0.2180 Acc: 0.9150

Epoch 13/24
----------
train Loss: 0.2783 Acc: 0.8852
val Loss: 0.2114 Acc: 0.9150

Epoch 14/24
----------
train Loss: 0.3161 Acc: 0.8525
val Loss: 0.2322 Acc: 0.9281

Epoch 15/24
----------
train Loss: 0.2464 Acc: 0.8893
val Loss: 0.2484 Acc: 0.9150

Epoch 16/24
----------
train Loss: 0.3293 Acc: 0.8197
val Loss: 0.2301 Acc: 0.9281

Epoch 17/24
----------
train Loss: 0.3128 Acc: 0.8525
val Loss: 0.2274 Acc: 0.9281

Epoch 18/24
----------
train Loss: 0.2847 Acc: 0.8893
val Loss: 0.2087 Acc: 0.9216

Epoch 19/24
----------
train Loss: 0.3354 Acc: 0.8566
val Loss: 0.2056 Acc: 0.9216

Epoch 20/24
----------
train Loss: 0.2304 Acc: 0.8811
val Loss: 0.2206 Acc: 0.8954

Epoch 21/24
----------
train Loss: 0.2365 Acc: 0.9057
val Loss: 0.2090 Acc: 0.9216

Epoch 22/24
----------
train Loss: 0.3119 Acc: 0.8607
val Loss: 0.2091 Acc: 0.9150

Epoch 23/24
----------
train Loss: 0.2522 Acc: 0.8975
val Loss: 0.2135 Acc: 0.9216

Epoch 24/24
----------
train Loss: 0.2618 Acc: 0.8811
val Loss: 0.2064 Acc: 0.9150

Training complete in 0m 35s
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: 0.5863 Acc: 0.6885
val Loss: 0.2419 Acc: 0.9281

Epoch 1/24
----------
train Loss: 0.5459 Acc: 0.7541
val Loss: 0.1531 Acc: 0.9673

Epoch 2/24
----------
train Loss: 0.5481 Acc: 0.7664
val Loss: 0.3643 Acc: 0.8301

Epoch 3/24
----------
train Loss: 0.4392 Acc: 0.8156
val Loss: 0.2023 Acc: 0.9150

Epoch 4/24
----------
train Loss: 0.4231 Acc: 0.7664
val Loss: 0.2373 Acc: 0.9216

Epoch 5/24
----------
train Loss: 0.5503 Acc: 0.7664
val Loss: 0.5207 Acc: 0.8235

Epoch 6/24
----------
train Loss: 0.6116 Acc: 0.7746
val Loss: 0.2869 Acc: 0.9150

Epoch 7/24
----------
train Loss: 0.4510 Acc: 0.8320
val Loss: 0.2009 Acc: 0.9542

Epoch 8/24
----------
train Loss: 0.4136 Acc: 0.8279
val Loss: 0.1948 Acc: 0.9477

Epoch 9/24
----------
train Loss: 0.3508 Acc: 0.8484
val Loss: 0.2148 Acc: 0.9477

Epoch 10/24
----------
train Loss: 0.3134 Acc: 0.8648
val Loss: 0.1969 Acc: 0.9608

Epoch 11/24
----------
train Loss: 0.3983 Acc: 0.8443
val Loss: 0.1882 Acc: 0.9608

Epoch 12/24
----------
train Loss: 0.3406 Acc: 0.8566
val Loss: 0.2092 Acc: 0.9477

Epoch 13/24
----------
train Loss: 0.3366 Acc: 0.8320
val Loss: 0.2264 Acc: 0.9216

Epoch 14/24
----------
train Loss: 0.3443 Acc: 0.8115
val Loss: 0.1897 Acc: 0.9608

Epoch 15/24
----------
train Loss: 0.3234 Acc: 0.8648
val Loss: 0.2172 Acc: 0.9412

Epoch 16/24
----------
train Loss: 0.3214 Acc: 0.8566
val Loss: 0.2113 Acc: 0.9542

Epoch 17/24
----------
train Loss: 0.3561 Acc: 0.8566
val Loss: 0.1696 Acc: 0.9542

Epoch 18/24
----------
train Loss: 0.3825 Acc: 0.8443
val Loss: 0.1902 Acc: 0.9608

Epoch 19/24
----------
train Loss: 0.3675 Acc: 0.8115
val Loss: 0.2156 Acc: 0.9477

Epoch 20/24
----------
train Loss: 0.3628 Acc: 0.8361
val Loss: 0.1960 Acc: 0.9542

Epoch 21/24
----------
train Loss: 0.3001 Acc: 0.8525
val Loss: 0.2121 Acc: 0.9412

Epoch 22/24
----------
train Loss: 0.3164 Acc: 0.8893
val Loss: 0.1897 Acc: 0.9542

Epoch 23/24
----------
train Loss: 0.2429 Acc: 0.8934
val Loss: 0.1794 Acc: 0.9477

Epoch 24/24
----------
train Loss: 0.3523 Acc: 0.8525
val Loss: 0.1852 Acc: 0.9542

Training complete in 0m 27s
Best val Acc: 0.967320

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.