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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])
![['bees', '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
0%| | 0.00/44.7M [00:00<?, ?B/s]
61%|██████▏ | 27.4M/44.7M [00:00<00:00, 287MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 306MB/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: 0.7065 Acc: 0.6516
val Loss: 0.7769 Acc: 0.7451
Epoch 1/24
----------
train Loss: 0.9926 Acc: 0.7172
val Loss: 0.2684 Acc: 0.9281
Epoch 2/24
----------
train Loss: 0.5517 Acc: 0.8238
val Loss: 0.3282 Acc: 0.9020
Epoch 3/24
----------
train Loss: 0.4724 Acc: 0.8320
val Loss: 0.2795 Acc: 0.8758
Epoch 4/24
----------
train Loss: 0.4430 Acc: 0.8361
val Loss: 0.3998 Acc: 0.8301
Epoch 5/24
----------
train Loss: 0.6074 Acc: 0.8074
val Loss: 0.4546 Acc: 0.8627
Epoch 6/24
----------
train Loss: 0.4868 Acc: 0.8320
val Loss: 0.3479 Acc: 0.8824
Epoch 7/24
----------
train Loss: 0.4081 Acc: 0.8197
val Loss: 0.3692 Acc: 0.8693
Epoch 8/24
----------
train Loss: 0.3736 Acc: 0.8320
val Loss: 0.2725 Acc: 0.9150
Epoch 9/24
----------
train Loss: 0.3140 Acc: 0.8689
val Loss: 0.2699 Acc: 0.9216
Epoch 10/24
----------
train Loss: 0.2839 Acc: 0.8852
val Loss: 0.2491 Acc: 0.9216
Epoch 11/24
----------
train Loss: 0.3918 Acc: 0.8402
val Loss: 0.2424 Acc: 0.9085
Epoch 12/24
----------
train Loss: 0.3633 Acc: 0.8320
val Loss: 0.2398 Acc: 0.9346
Epoch 13/24
----------
train Loss: 0.2627 Acc: 0.8934
val Loss: 0.2206 Acc: 0.9281
Epoch 14/24
----------
train Loss: 0.2878 Acc: 0.8811
val Loss: 0.2274 Acc: 0.9216
Epoch 15/24
----------
train Loss: 0.2590 Acc: 0.8852
val Loss: 0.2315 Acc: 0.9281
Epoch 16/24
----------
train Loss: 0.2542 Acc: 0.8811
val Loss: 0.2340 Acc: 0.9346
Epoch 17/24
----------
train Loss: 0.3255 Acc: 0.8648
val Loss: 0.2225 Acc: 0.9281
Epoch 18/24
----------
train Loss: 0.3098 Acc: 0.8525
val Loss: 0.2270 Acc: 0.9346
Epoch 19/24
----------
train Loss: 0.3341 Acc: 0.8402
val Loss: 0.2402 Acc: 0.9281
Epoch 20/24
----------
train Loss: 0.2925 Acc: 0.8689
val Loss: 0.2255 Acc: 0.9346
Epoch 21/24
----------
train Loss: 0.2788 Acc: 0.8811
val Loss: 0.2431 Acc: 0.9085
Epoch 22/24
----------
train Loss: 0.3141 Acc: 0.8607
val Loss: 0.2358 Acc: 0.9150
Epoch 23/24
----------
train Loss: 0.2738 Acc: 0.8648
val Loss: 0.2205 Acc: 0.9281
Epoch 24/24
----------
train Loss: 0.3102 Acc: 0.8730
val Loss: 0.2317 Acc: 0.9346
Training complete in 0m 37s
Best val Acc: 0.934641
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: 0.5599 Acc: 0.7172
val Loss: 0.2523 Acc: 0.8954
Epoch 1/24
----------
train Loss: 0.5810 Acc: 0.7500
val Loss: 0.2222 Acc: 0.9281
Epoch 2/24
----------
train Loss: 0.4307 Acc: 0.8156
val Loss: 0.1683 Acc: 0.9542
Epoch 3/24
----------
train Loss: 0.5176 Acc: 0.7992
val Loss: 0.2478 Acc: 0.8954
Epoch 4/24
----------
train Loss: 0.4536 Acc: 0.7869
val Loss: 0.3572 Acc: 0.8497
Epoch 5/24
----------
train Loss: 0.5375 Acc: 0.7910
val Loss: 0.1672 Acc: 0.9477
Epoch 6/24
----------
train Loss: 0.4168 Acc: 0.8197
val Loss: 0.1768 Acc: 0.9542
Epoch 7/24
----------
train Loss: 0.4380 Acc: 0.8115
val Loss: 0.1591 Acc: 0.9477
Epoch 8/24
----------
train Loss: 0.4712 Acc: 0.7705
val Loss: 0.1681 Acc: 0.9542
Epoch 9/24
----------
train Loss: 0.2642 Acc: 0.8852
val Loss: 0.1716 Acc: 0.9542
Epoch 10/24
----------
train Loss: 0.3244 Acc: 0.8402
val Loss: 0.1617 Acc: 0.9608
Epoch 11/24
----------
train Loss: 0.3173 Acc: 0.8689
val Loss: 0.1739 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.3422 Acc: 0.8402
val Loss: 0.1782 Acc: 0.9542
Epoch 13/24
----------
train Loss: 0.3685 Acc: 0.8402
val Loss: 0.1646 Acc: 0.9542
Epoch 14/24
----------
train Loss: 0.3684 Acc: 0.8443
val Loss: 0.1665 Acc: 0.9608
Epoch 15/24
----------
train Loss: 0.3381 Acc: 0.8852
val Loss: 0.1644 Acc: 0.9608
Epoch 16/24
----------
train Loss: 0.3674 Acc: 0.8279
val Loss: 0.1658 Acc: 0.9608
Epoch 17/24
----------
train Loss: 0.3513 Acc: 0.8566
val Loss: 0.1868 Acc: 0.9608
Epoch 18/24
----------
train Loss: 0.3579 Acc: 0.8484
val Loss: 0.1725 Acc: 0.9542
Epoch 19/24
----------
train Loss: 0.3648 Acc: 0.8197
val Loss: 0.1705 Acc: 0.9477
Epoch 20/24
----------
train Loss: 0.3076 Acc: 0.8648
val Loss: 0.1790 Acc: 0.9542
Epoch 21/24
----------
train Loss: 0.3105 Acc: 0.8566
val Loss: 0.1711 Acc: 0.9412
Epoch 22/24
----------
train Loss: 0.3049 Acc: 0.8607
val Loss: 0.1721 Acc: 0.9477
Epoch 23/24
----------
train Loss: 0.3146 Acc: 0.8525
val Loss: 0.1632 Acc: 0.9542
Epoch 24/24
----------
train Loss: 0.3265 Acc: 0.8648
val Loss: 0.1610 Acc: 0.9477
Training complete in 0m 28s
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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