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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', 'ants']](../_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]
91%|█████████ | 40.6M/44.7M [00:00<00:00, 425MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 425MB/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: 0.6780 Acc: 0.6311
val Loss: 0.3114 Acc: 0.8627
Epoch 1/24
----------
train Loss: 0.3890 Acc: 0.8238
val Loss: 0.3099 Acc: 0.9020
Epoch 2/24
----------
train Loss: 0.4650 Acc: 0.8238
val Loss: 0.3090 Acc: 0.8693
Epoch 3/24
----------
train Loss: 0.4869 Acc: 0.8197
val Loss: 0.5179 Acc: 0.7843
Epoch 4/24
----------
train Loss: 0.5576 Acc: 0.7705
val Loss: 0.2435 Acc: 0.9085
Epoch 5/24
----------
train Loss: 0.4818 Acc: 0.8402
val Loss: 0.2447 Acc: 0.9281
Epoch 6/24
----------
train Loss: 0.6305 Acc: 0.7951
val Loss: 0.3298 Acc: 0.8693
Epoch 7/24
----------
train Loss: 0.3889 Acc: 0.8443
val Loss: 0.2582 Acc: 0.9085
Epoch 8/24
----------
train Loss: 0.3196 Acc: 0.8648
val Loss: 0.2452 Acc: 0.9216
Epoch 9/24
----------
train Loss: 0.3941 Acc: 0.8197
val Loss: 0.2110 Acc: 0.9216
Epoch 10/24
----------
train Loss: 0.3556 Acc: 0.8443
val Loss: 0.2630 Acc: 0.8954
Epoch 11/24
----------
train Loss: 0.2780 Acc: 0.9016
val Loss: 0.2580 Acc: 0.8824
Epoch 12/24
----------
train Loss: 0.3266 Acc: 0.8770
val Loss: 0.1896 Acc: 0.9346
Epoch 13/24
----------
train Loss: 0.2993 Acc: 0.8730
val Loss: 0.2269 Acc: 0.9085
Epoch 14/24
----------
train Loss: 0.3276 Acc: 0.8607
val Loss: 0.2006 Acc: 0.9216
Epoch 15/24
----------
train Loss: 0.2422 Acc: 0.9057
val Loss: 0.2195 Acc: 0.9281
Epoch 16/24
----------
train Loss: 0.2567 Acc: 0.8975
val Loss: 0.1980 Acc: 0.9281
Epoch 17/24
----------
train Loss: 0.2789 Acc: 0.8975
val Loss: 0.2222 Acc: 0.9216
Epoch 18/24
----------
train Loss: 0.2855 Acc: 0.8648
val Loss: 0.3206 Acc: 0.8693
Epoch 19/24
----------
train Loss: 0.2834 Acc: 0.8852
val Loss: 0.2215 Acc: 0.9216
Epoch 20/24
----------
train Loss: 0.3117 Acc: 0.8893
val Loss: 0.2064 Acc: 0.9216
Epoch 21/24
----------
train Loss: 0.2871 Acc: 0.8402
val Loss: 0.2184 Acc: 0.9020
Epoch 22/24
----------
train Loss: 0.3210 Acc: 0.8484
val Loss: 0.2220 Acc: 0.9281
Epoch 23/24
----------
train Loss: 0.2398 Acc: 0.8934
val Loss: 0.1987 Acc: 0.9346
Epoch 24/24
----------
train Loss: 0.2915 Acc: 0.8648
val Loss: 0.1962 Acc: 0.9346
Training complete in 0m 36s
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: 0.5706 Acc: 0.7295
val Loss: 0.2541 Acc: 0.9085
Epoch 1/24
----------
train Loss: 0.7583 Acc: 0.6721
val Loss: 0.3547 Acc: 0.8431
Epoch 2/24
----------
train Loss: 0.4617 Acc: 0.7869
val Loss: 0.1767 Acc: 0.9281
Epoch 3/24
----------
train Loss: 0.4614 Acc: 0.8156
val Loss: 0.3423 Acc: 0.8562
Epoch 4/24
----------
train Loss: 0.6974 Acc: 0.7459
val Loss: 0.2170 Acc: 0.9020
Epoch 5/24
----------
train Loss: 0.5164 Acc: 0.7869
val Loss: 0.4195 Acc: 0.8693
Epoch 6/24
----------
train Loss: 0.5006 Acc: 0.7992
val Loss: 0.4520 Acc: 0.8497
Epoch 7/24
----------
train Loss: 0.5162 Acc: 0.7869
val Loss: 0.2207 Acc: 0.9412
Epoch 8/24
----------
train Loss: 0.3542 Acc: 0.8525
val Loss: 0.1754 Acc: 0.9608
Epoch 9/24
----------
train Loss: 0.3795 Acc: 0.8566
val Loss: 0.2112 Acc: 0.9346
Epoch 10/24
----------
train Loss: 0.3934 Acc: 0.8361
val Loss: 0.1967 Acc: 0.9412
Epoch 11/24
----------
train Loss: 0.4154 Acc: 0.8320
val Loss: 0.1990 Acc: 0.9412
Epoch 12/24
----------
train Loss: 0.3468 Acc: 0.8361
val Loss: 0.2204 Acc: 0.9346
Epoch 13/24
----------
train Loss: 0.3983 Acc: 0.8484
val Loss: 0.1824 Acc: 0.9412
Epoch 14/24
----------
train Loss: 0.3648 Acc: 0.8525
val Loss: 0.1846 Acc: 0.9477
Epoch 15/24
----------
train Loss: 0.3261 Acc: 0.8607
val Loss: 0.2118 Acc: 0.9346
Epoch 16/24
----------
train Loss: 0.3768 Acc: 0.8279
val Loss: 0.1944 Acc: 0.9412
Epoch 17/24
----------
train Loss: 0.3663 Acc: 0.8320
val Loss: 0.2029 Acc: 0.9477
Epoch 18/24
----------
train Loss: 0.3631 Acc: 0.8402
val Loss: 0.1946 Acc: 0.9477
Epoch 19/24
----------
train Loss: 0.3570 Acc: 0.8566
val Loss: 0.1795 Acc: 0.9412
Epoch 20/24
----------
train Loss: 0.2966 Acc: 0.8730
val Loss: 0.1910 Acc: 0.9281
Epoch 21/24
----------
train Loss: 0.3587 Acc: 0.8525
val Loss: 0.1988 Acc: 0.9346
Epoch 22/24
----------
train Loss: 0.3266 Acc: 0.8443
val Loss: 0.1781 Acc: 0.9477
Epoch 23/24
----------
train Loss: 0.4182 Acc: 0.8156
val Loss: 0.1715 Acc: 0.9608
Epoch 24/24
----------
train Loss: 0.3062 Acc: 0.8852
val Loss: 0.1882 Acc: 0.9542
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.
Total running time of the script: (1 minutes 6.100 seconds)
