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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', 'ants', '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]
77%|███████▋ | 34.2M/44.7M [00:00<00:00, 359MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 356MB/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: 0.5383 Acc: 0.7582
val Loss: 0.2400 Acc: 0.9150
Epoch 1/24
----------
train Loss: 0.6612 Acc: 0.7049
val Loss: 0.2789 Acc: 0.8889
Epoch 2/24
----------
train Loss: 0.4956 Acc: 0.7787
val Loss: 0.2725 Acc: 0.9281
Epoch 3/24
----------
train Loss: 0.5563 Acc: 0.7869
val Loss: 0.3456 Acc: 0.8562
Epoch 4/24
----------
train Loss: 0.5488 Acc: 0.8033
val Loss: 0.4654 Acc: 0.8301
Epoch 5/24
----------
train Loss: 0.4854 Acc: 0.8279
val Loss: 0.2111 Acc: 0.9216
Epoch 6/24
----------
train Loss: 0.4316 Acc: 0.8238
val Loss: 0.2159 Acc: 0.9281
Epoch 7/24
----------
train Loss: 0.3514 Acc: 0.8566
val Loss: 0.2101 Acc: 0.9412
Epoch 8/24
----------
train Loss: 0.3453 Acc: 0.8402
val Loss: 0.1986 Acc: 0.9542
Epoch 9/24
----------
train Loss: 0.2343 Acc: 0.9139
val Loss: 0.1729 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.3676 Acc: 0.8525
val Loss: 0.1929 Acc: 0.9346
Epoch 11/24
----------
train Loss: 0.3005 Acc: 0.8770
val Loss: 0.1690 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.1788 Acc: 0.9262
val Loss: 0.1696 Acc: 0.9477
Epoch 13/24
----------
train Loss: 0.2593 Acc: 0.8975
val Loss: 0.1641 Acc: 0.9608
Epoch 14/24
----------
train Loss: 0.2956 Acc: 0.8811
val Loss: 0.1584 Acc: 0.9608
Epoch 15/24
----------
train Loss: 0.2332 Acc: 0.8852
val Loss: 0.2310 Acc: 0.9150
Epoch 16/24
----------
train Loss: 0.2983 Acc: 0.8607
val Loss: 0.2390 Acc: 0.9150
Epoch 17/24
----------
train Loss: 0.2191 Acc: 0.9139
val Loss: 0.1751 Acc: 0.9542
Epoch 18/24
----------
train Loss: 0.2530 Acc: 0.8852
val Loss: 0.1548 Acc: 0.9608
Epoch 19/24
----------
train Loss: 0.3006 Acc: 0.8689
val Loss: 0.1620 Acc: 0.9673
Epoch 20/24
----------
train Loss: 0.2676 Acc: 0.8770
val Loss: 0.1640 Acc: 0.9542
Epoch 21/24
----------
train Loss: 0.2727 Acc: 0.8811
val Loss: 0.1583 Acc: 0.9542
Epoch 22/24
----------
train Loss: 0.2874 Acc: 0.8852
val Loss: 0.1583 Acc: 0.9608
Epoch 23/24
----------
train Loss: 0.3327 Acc: 0.8525
val Loss: 0.1881 Acc: 0.9346
Epoch 24/24
----------
train Loss: 0.2764 Acc: 0.8934
val Loss: 0.1627 Acc: 0.9542
Training complete in 0m 36s
Best val Acc: 0.967320
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: 0.5541 Acc: 0.6967
val Loss: 0.2244 Acc: 0.9477
Epoch 1/24
----------
train Loss: 0.4356 Acc: 0.7746
val Loss: 0.1714 Acc: 0.9412
Epoch 2/24
----------
train Loss: 0.4905 Acc: 0.7705
val Loss: 0.1843 Acc: 0.9412
Epoch 3/24
----------
train Loss: 0.5680 Acc: 0.7377
val Loss: 0.5141 Acc: 0.7908
Epoch 4/24
----------
train Loss: 0.6891 Acc: 0.7377
val Loss: 0.2521 Acc: 0.9216
Epoch 5/24
----------
train Loss: 0.5030 Acc: 0.7951
val Loss: 0.1955 Acc: 0.9346
Epoch 6/24
----------
train Loss: 0.3205 Acc: 0.8770
val Loss: 0.2080 Acc: 0.9216
Epoch 7/24
----------
train Loss: 0.3427 Acc: 0.8525
val Loss: 0.2006 Acc: 0.9281
Epoch 8/24
----------
train Loss: 0.3267 Acc: 0.8730
val Loss: 0.1904 Acc: 0.9281
Epoch 9/24
----------
train Loss: 0.3482 Acc: 0.8484
val Loss: 0.1910 Acc: 0.9346
Epoch 10/24
----------
train Loss: 0.3194 Acc: 0.8730
val Loss: 0.2025 Acc: 0.9346
Epoch 11/24
----------
train Loss: 0.3777 Acc: 0.8320
val Loss: 0.1905 Acc: 0.9346
Epoch 12/24
----------
train Loss: 0.3407 Acc: 0.8566
val Loss: 0.1974 Acc: 0.9346
Epoch 13/24
----------
train Loss: 0.3115 Acc: 0.8525
val Loss: 0.1856 Acc: 0.9412
Epoch 14/24
----------
train Loss: 0.2774 Acc: 0.8811
val Loss: 0.2078 Acc: 0.9281
Epoch 15/24
----------
train Loss: 0.3548 Acc: 0.8361
val Loss: 0.2437 Acc: 0.9281
Epoch 16/24
----------
train Loss: 0.3664 Acc: 0.8320
val Loss: 0.2366 Acc: 0.9281
Epoch 17/24
----------
train Loss: 0.3551 Acc: 0.8402
val Loss: 0.2239 Acc: 0.9281
Epoch 18/24
----------
train Loss: 0.3053 Acc: 0.8648
val Loss: 0.2042 Acc: 0.9281
Epoch 19/24
----------
train Loss: 0.3508 Acc: 0.8402
val Loss: 0.1879 Acc: 0.9346
Epoch 20/24
----------
train Loss: 0.3385 Acc: 0.8402
val Loss: 0.1766 Acc: 0.9477
Epoch 21/24
----------
train Loss: 0.3555 Acc: 0.8566
val Loss: 0.1911 Acc: 0.9412
Epoch 22/24
----------
train Loss: 0.3570 Acc: 0.8279
val Loss: 0.2168 Acc: 0.9346
Epoch 23/24
----------
train Loss: 0.2764 Acc: 0.8934
val Loss: 0.1901 Acc: 0.9216
Epoch 24/24
----------
train Loss: 0.3432 Acc: 0.8361
val Loss: 0.1983 Acc: 0.9216
Training complete in 0m 27s
Best val Acc: 0.947712
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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