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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])
![['ants', 'ants', 'bees', 'ants']](../_images/sphx_glr_transfer_learning_tutorial_001.png)
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: 0.5555 Acc: 0.7172
val Loss: 0.2248 Acc: 0.9346
Epoch 1/24
----------
train Loss: 0.5569 Acc: 0.7623
val Loss: 0.2059 Acc: 0.9150
Epoch 2/24
----------
train Loss: 0.3794 Acc: 0.8361
val Loss: 0.2652 Acc: 0.9085
Epoch 3/24
----------
train Loss: 0.5047 Acc: 0.8033
val Loss: 0.7269 Acc: 0.7386
Epoch 4/24
----------
train Loss: 0.4896 Acc: 0.8197
val Loss: 0.2691 Acc: 0.8824
Epoch 5/24
----------
train Loss: 0.6649 Acc: 0.7828
val Loss: 0.1788 Acc: 0.9542
Epoch 6/24
----------
train Loss: 0.7093 Acc: 0.7664
val Loss: 0.2629 Acc: 0.8824
Epoch 7/24
----------
train Loss: 0.4124 Acc: 0.8607
val Loss: 0.2945 Acc: 0.9020
Epoch 8/24
----------
train Loss: 0.3055 Acc: 0.8811
val Loss: 0.2641 Acc: 0.9085
Epoch 9/24
----------
train Loss: 0.3147 Acc: 0.8852
val Loss: 0.2376 Acc: 0.9085
Epoch 10/24
----------
train Loss: 0.2746 Acc: 0.8893
val Loss: 0.2657 Acc: 0.9085
Epoch 11/24
----------
train Loss: 0.3460 Acc: 0.8607
val Loss: 0.2617 Acc: 0.9150
Epoch 12/24
----------
train Loss: 0.2783 Acc: 0.8975
val Loss: 0.2773 Acc: 0.9085
Epoch 13/24
----------
train Loss: 0.2749 Acc: 0.8975
val Loss: 0.2353 Acc: 0.9150
Epoch 14/24
----------
train Loss: 0.3622 Acc: 0.8443
val Loss: 0.2741 Acc: 0.9085
Epoch 15/24
----------
train Loss: 0.2757 Acc: 0.8852
val Loss: 0.2292 Acc: 0.9216
Epoch 16/24
----------
train Loss: 0.2774 Acc: 0.8893
val Loss: 0.2442 Acc: 0.9150
Epoch 17/24
----------
train Loss: 0.2316 Acc: 0.9057
val Loss: 0.2472 Acc: 0.9150
Epoch 18/24
----------
train Loss: 0.1729 Acc: 0.9385
val Loss: 0.2617 Acc: 0.9150
Epoch 19/24
----------
train Loss: 0.3305 Acc: 0.8730
val Loss: 0.2599 Acc: 0.9020
Epoch 20/24
----------
train Loss: 0.2455 Acc: 0.9180
val Loss: 0.2343 Acc: 0.9216
Epoch 21/24
----------
train Loss: 0.2983 Acc: 0.8607
val Loss: 0.2741 Acc: 0.9085
Epoch 22/24
----------
train Loss: 0.2959 Acc: 0.8811
val Loss: 0.2341 Acc: 0.9216
Epoch 23/24
----------
train Loss: 0.3422 Acc: 0.8730
val Loss: 0.2384 Acc: 0.9085
Epoch 24/24
----------
train Loss: 0.3103 Acc: 0.8689
val Loss: 0.2307 Acc: 0.9281
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: 0.7305 Acc: 0.6352
val Loss: 0.1869 Acc: 0.9150
Epoch 1/24
----------
train Loss: 0.5002 Acc: 0.7828
val Loss: 0.2263 Acc: 0.9281
Epoch 2/24
----------
train Loss: 0.4981 Acc: 0.8033
val Loss: 0.2626 Acc: 0.9085
Epoch 3/24
----------
train Loss: 0.5071 Acc: 0.7869
val Loss: 0.3526 Acc: 0.8889
Epoch 4/24
----------
train Loss: 0.3014 Acc: 0.8770
val Loss: 0.1911 Acc: 0.9412
Epoch 5/24
----------
train Loss: 0.3081 Acc: 0.8361
val Loss: 0.1920 Acc: 0.9542
Epoch 6/24
----------
train Loss: 0.3891 Acc: 0.8361
val Loss: 0.4000 Acc: 0.8627
Epoch 7/24
----------
train Loss: 0.6116 Acc: 0.7336
val Loss: 0.1843 Acc: 0.9542
Epoch 8/24
----------
train Loss: 0.3597 Acc: 0.8607
val Loss: 0.1793 Acc: 0.9477
Epoch 9/24
----------
train Loss: 0.3758 Acc: 0.8197
val Loss: 0.1740 Acc: 0.9542
Epoch 10/24
----------
train Loss: 0.4304 Acc: 0.8238
val Loss: 0.1901 Acc: 0.9542
Epoch 11/24
----------
train Loss: 0.3309 Acc: 0.8689
val Loss: 0.1769 Acc: 0.9608
Epoch 12/24
----------
train Loss: 0.3570 Acc: 0.8443
val Loss: 0.1916 Acc: 0.9542
Epoch 13/24
----------
train Loss: 0.3031 Acc: 0.8730
val Loss: 0.2070 Acc: 0.9477
Epoch 14/24
----------
train Loss: 0.3512 Acc: 0.8484
val Loss: 0.1895 Acc: 0.9477
Epoch 15/24
----------
train Loss: 0.3269 Acc: 0.8730
val Loss: 0.2028 Acc: 0.9542
Epoch 16/24
----------
train Loss: 0.3594 Acc: 0.8402
val Loss: 0.1846 Acc: 0.9608
Epoch 17/24
----------
train Loss: 0.2602 Acc: 0.9016
val Loss: 0.1910 Acc: 0.9542
Epoch 18/24
----------
train Loss: 0.4326 Acc: 0.7951
val Loss: 0.1810 Acc: 0.9542
Epoch 19/24
----------
train Loss: 0.2477 Acc: 0.8975
val Loss: 0.2231 Acc: 0.9346
Epoch 20/24
----------
train Loss: 0.2881 Acc: 0.8770
val Loss: 0.1778 Acc: 0.9608
Epoch 21/24
----------
train Loss: 0.4013 Acc: 0.7951
val Loss: 0.2179 Acc: 0.9542
Epoch 22/24
----------
train Loss: 0.3019 Acc: 0.8648
val Loss: 0.1868 Acc: 0.9542
Epoch 23/24
----------
train Loss: 0.3794 Acc: 0.8320
val Loss: 0.1763 Acc: 0.9608
Epoch 24/24
----------
train Loss: 0.3398 Acc: 0.8361
val Loss: 0.1858 Acc: 0.9477
Training complete in 0m 27s
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.
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