Note
Click here to download the full example code
Transfer Learning for Computer Vision Tutorial¶
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 initializaion, 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
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
plt.ion() # interactive mode
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
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Visualize a few images¶
Let’s visualize a few training images so as to understand the data augmentations.
def imshow(inp, title=None):
"""Imshow 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])

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()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(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('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
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('predicted: {}'.format(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(pretrained=True)
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)
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)
Out:
Epoch 0/24
----------
train Loss: 0.6750 Acc: 0.6557
val Loss: 0.3182 Acc: 0.9150
Epoch 1/24
----------
train Loss: 0.9820 Acc: 0.7172
val Loss: 0.4363 Acc: 0.8627
Epoch 2/24
----------
train Loss: 0.6248 Acc: 0.7869
val Loss: 0.4056 Acc: 0.8693
Epoch 3/24
----------
train Loss: 0.5376 Acc: 0.7582
val Loss: 1.0370 Acc: 0.7386
Epoch 4/24
----------
train Loss: 0.6330 Acc: 0.7951
val Loss: 0.3141 Acc: 0.8824
Epoch 5/24
----------
train Loss: 0.5061 Acc: 0.8074
val Loss: 0.6154 Acc: 0.8366
Epoch 6/24
----------
train Loss: 0.4283 Acc: 0.7992
val Loss: 0.3225 Acc: 0.8562
Epoch 7/24
----------
train Loss: 0.3968 Acc: 0.8361
val Loss: 0.2257 Acc: 0.9150
Epoch 8/24
----------
train Loss: 0.2702 Acc: 0.8770
val Loss: 0.2366 Acc: 0.9085
Epoch 9/24
----------
train Loss: 0.3437 Acc: 0.8730
val Loss: 0.2389 Acc: 0.9216
Epoch 10/24
----------
train Loss: 0.2744 Acc: 0.8852
val Loss: 0.2374 Acc: 0.9150
Epoch 11/24
----------
train Loss: 0.3440 Acc: 0.8811
val Loss: 0.2333 Acc: 0.9216
Epoch 12/24
----------
train Loss: 0.2161 Acc: 0.9057
val Loss: 0.2339 Acc: 0.9346
Epoch 13/24
----------
train Loss: 0.2950 Acc: 0.8648
val Loss: 0.2525 Acc: 0.9281
Epoch 14/24
----------
train Loss: 0.3029 Acc: 0.8484
val Loss: 0.2462 Acc: 0.9346
Epoch 15/24
----------
train Loss: 0.3016 Acc: 0.8770
val Loss: 0.2564 Acc: 0.9150
Epoch 16/24
----------
train Loss: 0.3248 Acc: 0.8566
val Loss: 0.2462 Acc: 0.9216
Epoch 17/24
----------
train Loss: 0.2103 Acc: 0.9221
val Loss: 0.2425 Acc: 0.9281
Epoch 18/24
----------
train Loss: 0.2811 Acc: 0.8893
val Loss: 0.2421 Acc: 0.9346
Epoch 19/24
----------
train Loss: 0.2590 Acc: 0.8811
val Loss: 0.2326 Acc: 0.9216
Epoch 20/24
----------
train Loss: 0.2769 Acc: 0.8975
val Loss: 0.2476 Acc: 0.9150
Epoch 21/24
----------
train Loss: 0.2881 Acc: 0.8730
val Loss: 0.2396 Acc: 0.9281
Epoch 22/24
----------
train Loss: 0.2263 Acc: 0.9385
val Loss: 0.2348 Acc: 0.9216
Epoch 23/24
----------
train Loss: 0.2500 Acc: 0.8852
val Loss: 0.2533 Acc: 0.9281
Epoch 24/24
----------
train Loss: 0.2726 Acc: 0.8934
val Loss: 0.2399 Acc: 0.9281
Training complete in 1m 10s
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(pretrained=True)
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)
Out:
Epoch 0/24
----------
train Loss: 0.5475 Acc: 0.7049
val Loss: 0.2228 Acc: 0.9346
Epoch 1/24
----------
train Loss: 0.5115 Acc: 0.7500
val Loss: 0.1902 Acc: 0.9477
Epoch 2/24
----------
train Loss: 0.6709 Acc: 0.7172
val Loss: 0.1790 Acc: 0.9412
Epoch 3/24
----------
train Loss: 0.6136 Acc: 0.7582
val Loss: 0.2086 Acc: 0.9412
Epoch 4/24
----------
train Loss: 0.5677 Acc: 0.7541
val Loss: 0.1837 Acc: 0.9477
Epoch 5/24
----------
train Loss: 0.5572 Acc: 0.7828
val Loss: 0.2249 Acc: 0.9281
Epoch 6/24
----------
train Loss: 0.6167 Acc: 0.7582
val Loss: 0.1822 Acc: 0.9477
Epoch 7/24
----------
train Loss: 0.4875 Acc: 0.7705
val Loss: 0.1784 Acc: 0.9477
Epoch 8/24
----------
train Loss: 0.4510 Acc: 0.7787
val Loss: 0.1803 Acc: 0.9542
Epoch 9/24
----------
train Loss: 0.4077 Acc: 0.8115
val Loss: 0.2130 Acc: 0.9542
Epoch 10/24
----------
train Loss: 0.4449 Acc: 0.8156
val Loss: 0.1671 Acc: 0.9477
Epoch 11/24
----------
train Loss: 0.3937 Acc: 0.8361
val Loss: 0.1738 Acc: 0.9542
Epoch 12/24
----------
train Loss: 0.3437 Acc: 0.8279
val Loss: 0.1774 Acc: 0.9477
Epoch 13/24
----------
train Loss: 0.3363 Acc: 0.8770
val Loss: 0.1814 Acc: 0.9477
Epoch 14/24
----------
train Loss: 0.3235 Acc: 0.8607
val Loss: 0.1764 Acc: 0.9542
Epoch 15/24
----------
train Loss: 0.3990 Acc: 0.8156
val Loss: 0.1865 Acc: 0.9542
Epoch 16/24
----------
train Loss: 0.3650 Acc: 0.8279
val Loss: 0.1655 Acc: 0.9542
Epoch 17/24
----------
train Loss: 0.4488 Acc: 0.8074
val Loss: 0.1767 Acc: 0.9542
Epoch 18/24
----------
train Loss: 0.4024 Acc: 0.8115
val Loss: 0.1831 Acc: 0.9542
Epoch 19/24
----------
train Loss: 0.3496 Acc: 0.8238
val Loss: 0.1875 Acc: 0.9477
Epoch 20/24
----------
train Loss: 0.3476 Acc: 0.8566
val Loss: 0.1993 Acc: 0.9542
Epoch 21/24
----------
train Loss: 0.3115 Acc: 0.8566
val Loss: 0.1907 Acc: 0.9477
Epoch 22/24
----------
train Loss: 0.3791 Acc: 0.8115
val Loss: 0.1726 Acc: 0.9477
Epoch 23/24
----------
train Loss: 0.4003 Acc: 0.8074
val Loss: 0.2054 Acc: 0.9542
Epoch 24/24
----------
train Loss: 0.3599 Acc: 0.8279
val Loss: 0.1880 Acc: 0.9542
Training complete in 0m 33s
Best val Acc: 0.954248
visualize_model(model_conv)
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.
Total running time of the script: ( 1 minutes 51.384 seconds)