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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', '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
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92%|█████████▏| 41.0M/44.7M [00:00<00:00, 430MB/s]
100%|██████████| 44.7M/44.7M [00:00<00:00, 430MB/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: 0.4709 Acc: 0.7582
val Loss: 0.2238 Acc: 0.9216
Epoch 1/24
----------
train Loss: 0.4134 Acc: 0.7869
val Loss: 0.2879 Acc: 0.9216
Epoch 2/24
----------
train Loss: 0.5426 Acc: 0.7582
val Loss: 0.3038 Acc: 0.9085
Epoch 3/24
----------
train Loss: 0.7099 Acc: 0.7582
val Loss: 0.5305 Acc: 0.8039
Epoch 4/24
----------
train Loss: 0.5100 Acc: 0.8074
val Loss: 0.2876 Acc: 0.8954
Epoch 5/24
----------
train Loss: 0.5686 Acc: 0.7828
val Loss: 0.2339 Acc: 0.9085
Epoch 6/24
----------
train Loss: 0.5875 Acc: 0.7705
val Loss: 0.3473 Acc: 0.8954
Epoch 7/24
----------
train Loss: 0.3657 Acc: 0.8402
val Loss: 0.3048 Acc: 0.9020
Epoch 8/24
----------
train Loss: 0.3652 Acc: 0.8361
val Loss: 0.2933 Acc: 0.9085
Epoch 9/24
----------
train Loss: 0.2453 Acc: 0.8811
val Loss: 0.3061 Acc: 0.9020
Epoch 10/24
----------
train Loss: 0.3240 Acc: 0.8525
val Loss: 0.3011 Acc: 0.9085
Epoch 11/24
----------
train Loss: 0.2825 Acc: 0.9016
val Loss: 0.2796 Acc: 0.9150
Epoch 12/24
----------
train Loss: 0.2774 Acc: 0.8811
val Loss: 0.2675 Acc: 0.9085
Epoch 13/24
----------
train Loss: 0.2923 Acc: 0.8607
val Loss: 0.2731 Acc: 0.9150
Epoch 14/24
----------
train Loss: 0.3124 Acc: 0.8689
val Loss: 0.2595 Acc: 0.9150
Epoch 15/24
----------
train Loss: 0.3103 Acc: 0.8852
val Loss: 0.2901 Acc: 0.8954
Epoch 16/24
----------
train Loss: 0.2513 Acc: 0.8770
val Loss: 0.2691 Acc: 0.9150
Epoch 17/24
----------
train Loss: 0.3265 Acc: 0.8525
val Loss: 0.2696 Acc: 0.9281
Epoch 18/24
----------
train Loss: 0.2786 Acc: 0.8648
val Loss: 0.2660 Acc: 0.9150
Epoch 19/24
----------
train Loss: 0.2329 Acc: 0.8975
val Loss: 0.3181 Acc: 0.8889
Epoch 20/24
----------
train Loss: 0.2606 Acc: 0.8893
val Loss: 0.2584 Acc: 0.9150
Epoch 21/24
----------
train Loss: 0.2244 Acc: 0.9057
val Loss: 0.2720 Acc: 0.9216
Epoch 22/24
----------
train Loss: 0.2758 Acc: 0.8648
val Loss: 0.2874 Acc: 0.9020
Epoch 23/24
----------
train Loss: 0.2389 Acc: 0.8975
val Loss: 0.2696 Acc: 0.9216
Epoch 24/24
----------
train Loss: 0.2503 Acc: 0.8934
val Loss: 0.2822 Acc: 0.9150
Training complete in 0m 37s
Best val Acc: 0.928105
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: 0.6066 Acc: 0.6803
val Loss: 0.2514 Acc: 0.9150
Epoch 1/24
----------
train Loss: 0.6344 Acc: 0.7172
val Loss: 0.2838 Acc: 0.9150
Epoch 2/24
----------
train Loss: 0.5140 Acc: 0.8033
val Loss: 0.2130 Acc: 0.9281
Epoch 3/24
----------
train Loss: 0.6261 Acc: 0.7377
val Loss: 0.1814 Acc: 0.9412
Epoch 4/24
----------
train Loss: 0.4771 Acc: 0.7746
val Loss: 0.2656 Acc: 0.8954
Epoch 5/24
----------
train Loss: 0.4019 Acc: 0.8402
val Loss: 0.3460 Acc: 0.8758
Epoch 6/24
----------
train Loss: 0.4129 Acc: 0.8279
val Loss: 0.2070 Acc: 0.9281
Epoch 7/24
----------
train Loss: 0.3735 Acc: 0.8648
val Loss: 0.1903 Acc: 0.9412
Epoch 8/24
----------
train Loss: 0.2837 Acc: 0.8934
val Loss: 0.1995 Acc: 0.9412
Epoch 9/24
----------
train Loss: 0.3682 Acc: 0.8361
val Loss: 0.1921 Acc: 0.9412
Epoch 10/24
----------
train Loss: 0.3369 Acc: 0.8689
val Loss: 0.2047 Acc: 0.9412
Epoch 11/24
----------
train Loss: 0.4118 Acc: 0.8115
val Loss: 0.2244 Acc: 0.9281
Epoch 12/24
----------
train Loss: 0.3582 Acc: 0.8607
val Loss: 0.1889 Acc: 0.9346
Epoch 13/24
----------
train Loss: 0.3554 Acc: 0.8402
val Loss: 0.2004 Acc: 0.9346
Epoch 14/24
----------
train Loss: 0.3883 Acc: 0.8443
val Loss: 0.2377 Acc: 0.9281
Epoch 15/24
----------
train Loss: 0.2490 Acc: 0.8934
val Loss: 0.1923 Acc: 0.9412
Epoch 16/24
----------
train Loss: 0.3422 Acc: 0.8689
val Loss: 0.2054 Acc: 0.9346
Epoch 17/24
----------
train Loss: 0.3417 Acc: 0.8770
val Loss: 0.1793 Acc: 0.9477
Epoch 18/24
----------
train Loss: 0.3841 Acc: 0.8115
val Loss: 0.1886 Acc: 0.9477
Epoch 19/24
----------
train Loss: 0.2623 Acc: 0.8811
val Loss: 0.1863 Acc: 0.9412
Epoch 20/24
----------
train Loss: 0.3516 Acc: 0.8484
val Loss: 0.1817 Acc: 0.9412
Epoch 21/24
----------
train Loss: 0.3184 Acc: 0.8648
val Loss: 0.1922 Acc: 0.9346
Epoch 22/24
----------
train Loss: 0.4132 Acc: 0.8279
val Loss: 0.1966 Acc: 0.9346
Epoch 23/24
----------
train Loss: 0.2956 Acc: 0.8811
val Loss: 0.1760 Acc: 0.9412
Epoch 24/24
----------
train Loss: 0.3005 Acc: 0.8648
val Loss: 0.1799 Acc: 0.9412
Training complete in 0m 28s
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.
Total running time of the script: (1 minutes 7.242 seconds)
