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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']](https://calendarstemplates.com/../_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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100%|██████████| 44.7M/44.7M [00:00<00:00, 434MB/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: 0.5576 Acc: 0.7131
val Loss: 0.3701 Acc: 0.8235
Epoch 1/24
----------
train Loss: 0.4061 Acc: 0.8279
val Loss: 0.2086 Acc: 0.9216
Epoch 2/24
----------
train Loss: 0.5089 Acc: 0.7951
val Loss: 0.2532 Acc: 0.9216
Epoch 3/24
----------
train Loss: 0.4875 Acc: 0.8074
val Loss: 0.3262 Acc: 0.8889
Epoch 4/24
----------
train Loss: 0.6083 Acc: 0.7828
val Loss: 0.2067 Acc: 0.8954
Epoch 5/24
----------
train Loss: 0.4794 Acc: 0.7623
val Loss: 0.5692 Acc: 0.7712
Epoch 6/24
----------
train Loss: 0.3726 Acc: 0.8443
val Loss: 0.2359 Acc: 0.9085
Epoch 7/24
----------
train Loss: 0.2970 Acc: 0.8811
val Loss: 0.2451 Acc: 0.9150
Epoch 8/24
----------
train Loss: 0.2916 Acc: 0.8852
val Loss: 0.2238 Acc: 0.9216
Epoch 9/24
----------
train Loss: 0.2966 Acc: 0.8852
val Loss: 0.1878 Acc: 0.9150
Epoch 10/24
----------
train Loss: 0.2683 Acc: 0.9098
val Loss: 0.2264 Acc: 0.9216
Epoch 11/24
----------
train Loss: 0.4152 Acc: 0.8115
val Loss: 0.2194 Acc: 0.9150
Epoch 12/24
----------
train Loss: 0.2315 Acc: 0.9098
val Loss: 0.2008 Acc: 0.9216
Epoch 13/24
----------
train Loss: 0.2834 Acc: 0.8689
val Loss: 0.1975 Acc: 0.9085
Epoch 14/24
----------
train Loss: 0.2961 Acc: 0.8934
val Loss: 0.1919 Acc: 0.9150
Epoch 15/24
----------
train Loss: 0.2535 Acc: 0.8934
val Loss: 0.2099 Acc: 0.9150
Epoch 16/24
----------
train Loss: 0.2241 Acc: 0.9221
val Loss: 0.1779 Acc: 0.9412
Epoch 17/24
----------
train Loss: 0.2631 Acc: 0.8893
val Loss: 0.1909 Acc: 0.9216
Epoch 18/24
----------
train Loss: 0.2488 Acc: 0.8893
val Loss: 0.1917 Acc: 0.9346
Epoch 19/24
----------
train Loss: 0.2970 Acc: 0.8689
val Loss: 0.1793 Acc: 0.9346
Epoch 20/24
----------
train Loss: 0.2324 Acc: 0.8852
val Loss: 0.1841 Acc: 0.9412
Epoch 21/24
----------
train Loss: 0.2850 Acc: 0.8770
val Loss: 0.1849 Acc: 0.9281
Epoch 22/24
----------
train Loss: 0.2894 Acc: 0.8811
val Loss: 0.1864 Acc: 0.9216
Epoch 23/24
----------
train Loss: 0.3343 Acc: 0.8730
val Loss: 0.2191 Acc: 0.9281
Epoch 24/24
----------
train Loss: 0.3230 Acc: 0.8525
val Loss: 0.1884 Acc: 0.9412
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: 0.5213 Acc: 0.7377
val Loss: 0.1828 Acc: 0.9542
Epoch 1/24
----------
train Loss: 0.4869 Acc: 0.7705
val Loss: 0.1616 Acc: 0.9542
Epoch 2/24
----------
train Loss: 0.4520 Acc: 0.8279
val Loss: 0.2656 Acc: 0.8889
Epoch 3/24
----------
train Loss: 0.5149 Acc: 0.7705
val Loss: 0.1756 Acc: 0.9477
Epoch 4/24
----------
train Loss: 0.4439 Acc: 0.7746
val Loss: 0.1900 Acc: 0.9412
Epoch 5/24
----------
train Loss: 0.4504 Acc: 0.8238
val Loss: 0.5155 Acc: 0.8301
Epoch 6/24
----------
train Loss: 0.5483 Acc: 0.8033
val Loss: 0.1994 Acc: 0.9412
Epoch 7/24
----------
train Loss: 0.3609 Acc: 0.8443
val Loss: 0.1627 Acc: 0.9477
Epoch 8/24
----------
train Loss: 0.3533 Acc: 0.8648
val Loss: 0.1841 Acc: 0.9477
Epoch 9/24
----------
train Loss: 0.3635 Acc: 0.8443
val Loss: 0.1560 Acc: 0.9542
Epoch 10/24
----------
train Loss: 0.4537 Acc: 0.8074
val Loss: 0.1599 Acc: 0.9542
Epoch 11/24
----------
train Loss: 0.2744 Acc: 0.8852
val Loss: 0.1788 Acc: 0.9477
Epoch 12/24
----------
train Loss: 0.2942 Acc: 0.8811
val Loss: 0.1664 Acc: 0.9542
Epoch 13/24
----------
train Loss: 0.4126 Acc: 0.8115
val Loss: 0.1596 Acc: 0.9542
Epoch 14/24
----------
train Loss: 0.3262 Acc: 0.8443
val Loss: 0.1726 Acc: 0.9477
Epoch 15/24
----------
train Loss: 0.2798 Acc: 0.8730
val Loss: 0.1593 Acc: 0.9542
Epoch 16/24
----------
train Loss: 0.3882 Acc: 0.8156
val Loss: 0.1685 Acc: 0.9542
Epoch 17/24
----------
train Loss: 0.3118 Acc: 0.8648
val Loss: 0.1790 Acc: 0.9542
Epoch 18/24
----------
train Loss: 0.3508 Acc: 0.8525
val Loss: 0.1606 Acc: 0.9542
Epoch 19/24
----------
train Loss: 0.3204 Acc: 0.8689
val Loss: 0.1595 Acc: 0.9477
Epoch 20/24
----------
train Loss: 0.4382 Acc: 0.8197
val Loss: 0.1529 Acc: 0.9542
Epoch 21/24
----------
train Loss: 0.3406 Acc: 0.8361
val Loss: 0.1646 Acc: 0.9542
Epoch 22/24
----------
train Loss: 0.2547 Acc: 0.9016
val Loss: 0.1617 Acc: 0.9412
Epoch 23/24
----------
train Loss: 0.2781 Acc: 0.8852
val Loss: 0.1584 Acc: 0.9542
Epoch 24/24
----------
train Loss: 0.2635 Acc: 0.8975
val Loss: 0.2365 Acc: 0.9085
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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