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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', 'bees']](../_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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94%|█████████▍| 42.0M/44.7M [00:00<00:00, 440MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 440MB/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: 0.5075 Acc: 0.7254
val Loss: 0.2244 Acc: 0.9150
Epoch 1/24
----------
train Loss: 0.4585 Acc: 0.8074
val Loss: 0.1648 Acc: 0.9412
Epoch 2/24
----------
train Loss: 0.4327 Acc: 0.8443
val Loss: 0.5714 Acc: 0.7974
Epoch 3/24
----------
train Loss: 0.5568 Acc: 0.7910
val Loss: 0.3270 Acc: 0.8824
Epoch 4/24
----------
train Loss: 0.5948 Acc: 0.7746
val Loss: 0.2346 Acc: 0.9281
Epoch 5/24
----------
train Loss: 0.4609 Acc: 0.8197
val Loss: 0.8636 Acc: 0.7190
Epoch 6/24
----------
train Loss: 0.4420 Acc: 0.8320
val Loss: 0.7018 Acc: 0.7386
Epoch 7/24
----------
train Loss: 0.4595 Acc: 0.7951
val Loss: 0.2814 Acc: 0.8954
Epoch 8/24
----------
train Loss: 0.4211 Acc: 0.8320
val Loss: 0.2179 Acc: 0.9150
Epoch 9/24
----------
train Loss: 0.3660 Acc: 0.8525
val Loss: 0.2075 Acc: 0.9346
Epoch 10/24
----------
train Loss: 0.3280 Acc: 0.8525
val Loss: 0.2043 Acc: 0.9346
Epoch 11/24
----------
train Loss: 0.3211 Acc: 0.8730
val Loss: 0.2006 Acc: 0.9412
Epoch 12/24
----------
train Loss: 0.3737 Acc: 0.8443
val Loss: 0.2126 Acc: 0.9216
Epoch 13/24
----------
train Loss: 0.3341 Acc: 0.8566
val Loss: 0.2349 Acc: 0.9216
Epoch 14/24
----------
train Loss: 0.2991 Acc: 0.8607
val Loss: 0.2201 Acc: 0.9477
Epoch 15/24
----------
train Loss: 0.3445 Acc: 0.8361
val Loss: 0.2149 Acc: 0.9477
Epoch 16/24
----------
train Loss: 0.3053 Acc: 0.8689
val Loss: 0.2115 Acc: 0.9542
Epoch 17/24
----------
train Loss: 0.3485 Acc: 0.8443
val Loss: 0.2034 Acc: 0.9216
Epoch 18/24
----------
train Loss: 0.2373 Acc: 0.8689
val Loss: 0.2180 Acc: 0.9412
Epoch 19/24
----------
train Loss: 0.3226 Acc: 0.8648
val Loss: 0.2183 Acc: 0.9346
Epoch 20/24
----------
train Loss: 0.2617 Acc: 0.8893
val Loss: 0.2054 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.3308 Acc: 0.8730
val Loss: 0.2425 Acc: 0.9150
Epoch 22/24
----------
train Loss: 0.2216 Acc: 0.8975
val Loss: 0.2293 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.2600 Acc: 0.8770
val Loss: 0.2253 Acc: 0.9281
Epoch 24/24
----------
train Loss: 0.2355 Acc: 0.9180
val Loss: 0.1997 Acc: 0.9412
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: 0.7019 Acc: 0.6475
val Loss: 0.4131 Acc: 0.7908
Epoch 1/24
----------
train Loss: 0.5243 Acc: 0.7582
val Loss: 0.2365 Acc: 0.9150
Epoch 2/24
----------
train Loss: 0.5448 Acc: 0.7746
val Loss: 0.4899 Acc: 0.8039
Epoch 3/24
----------
train Loss: 0.4544 Acc: 0.8361
val Loss: 0.1791 Acc: 0.9542
Epoch 4/24
----------
train Loss: 0.3719 Acc: 0.8525
val Loss: 0.1892 Acc: 0.9477
Epoch 5/24
----------
train Loss: 0.4131 Acc: 0.7992
val Loss: 0.2137 Acc: 0.9281
Epoch 6/24
----------
train Loss: 0.4003 Acc: 0.8033
val Loss: 0.1961 Acc: 0.9412
Epoch 7/24
----------
train Loss: 0.4037 Acc: 0.8238
val Loss: 0.2061 Acc: 0.9281
Epoch 8/24
----------
train Loss: 0.3968 Acc: 0.8279
val Loss: 0.1997 Acc: 0.9346
Epoch 9/24
----------
train Loss: 0.3689 Acc: 0.8197
val Loss: 0.1852 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.3343 Acc: 0.8607
val Loss: 0.1913 Acc: 0.9477
Epoch 11/24
----------
train Loss: 0.2938 Acc: 0.8689
val Loss: 0.2090 Acc: 0.9542
Epoch 12/24
----------
train Loss: 0.3602 Acc: 0.8689
val Loss: 0.1947 Acc: 0.9412
Epoch 13/24
----------
train Loss: 0.3965 Acc: 0.8197
val Loss: 0.1883 Acc: 0.9477
Epoch 14/24
----------
train Loss: 0.3890 Acc: 0.8484
val Loss: 0.1870 Acc: 0.9542
Epoch 15/24
----------
train Loss: 0.3271 Acc: 0.8648
val Loss: 0.1822 Acc: 0.9412
Epoch 16/24
----------
train Loss: 0.4212 Acc: 0.8156
val Loss: 0.1789 Acc: 0.9477
Epoch 17/24
----------
train Loss: 0.3315 Acc: 0.8484
val Loss: 0.1867 Acc: 0.9542
Epoch 18/24
----------
train Loss: 0.3221 Acc: 0.8689
val Loss: 0.1905 Acc: 0.9346
Epoch 19/24
----------
train Loss: 0.3383 Acc: 0.8156
val Loss: 0.1856 Acc: 0.9542
Epoch 20/24
----------
train Loss: 0.2690 Acc: 0.8893
val Loss: 0.1840 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.2966 Acc: 0.8648
val Loss: 0.1854 Acc: 0.9477
Epoch 22/24
----------
train Loss: 0.3556 Acc: 0.8607
val Loss: 0.1844 Acc: 0.9542
Epoch 23/24
----------
train Loss: 0.3067 Acc: 0.8607
val Loss: 0.1863 Acc: 0.9477
Epoch 24/24
----------
train Loss: 0.3042 Acc: 0.8566
val Loss: 0.1920 Acc: 0.9477
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.
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