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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', 'bees', '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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100%|██████████| 44.7M/44.7M [00:00<00:00, 438MB/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: 0.6042 Acc: 0.6762
val Loss: 0.2212 Acc: 0.9216
Epoch 1/24
----------
train Loss: 0.6711 Acc: 0.7172
val Loss: 0.2308 Acc: 0.9216
Epoch 2/24
----------
train Loss: 0.5335 Acc: 0.8320
val Loss: 0.2095 Acc: 0.9150
Epoch 3/24
----------
train Loss: 0.5405 Acc: 0.8156
val Loss: 0.2868 Acc: 0.8758
Epoch 4/24
----------
train Loss: 0.6138 Acc: 0.7951
val Loss: 0.2656 Acc: 0.8889
Epoch 5/24
----------
train Loss: 0.4244 Acc: 0.8074
val Loss: 0.2632 Acc: 0.8889
Epoch 6/24
----------
train Loss: 0.3988 Acc: 0.8484
val Loss: 0.4063 Acc: 0.8824
Epoch 7/24
----------
train Loss: 0.3948 Acc: 0.8197
val Loss: 0.3230 Acc: 0.8758
Epoch 8/24
----------
train Loss: 0.3505 Acc: 0.8484
val Loss: 0.2563 Acc: 0.8889
Epoch 9/24
----------
train Loss: 0.2893 Acc: 0.8770
val Loss: 0.2137 Acc: 0.9020
Epoch 10/24
----------
train Loss: 0.2979 Acc: 0.8730
val Loss: 0.2110 Acc: 0.9150
Epoch 11/24
----------
train Loss: 0.2412 Acc: 0.9057
val Loss: 0.2280 Acc: 0.8889
Epoch 12/24
----------
train Loss: 0.2037 Acc: 0.9221
val Loss: 0.2258 Acc: 0.8954
Epoch 13/24
----------
train Loss: 0.2628 Acc: 0.8730
val Loss: 0.2029 Acc: 0.9150
Epoch 14/24
----------
train Loss: 0.2249 Acc: 0.9057
val Loss: 0.2129 Acc: 0.9281
Epoch 15/24
----------
train Loss: 0.3233 Acc: 0.8525
val Loss: 0.2122 Acc: 0.9085
Epoch 16/24
----------
train Loss: 0.2039 Acc: 0.9098
val Loss: 0.2056 Acc: 0.9150
Epoch 17/24
----------
train Loss: 0.3282 Acc: 0.8320
val Loss: 0.1975 Acc: 0.9150
Epoch 18/24
----------
train Loss: 0.2423 Acc: 0.9098
val Loss: 0.2018 Acc: 0.9216
Epoch 19/24
----------
train Loss: 0.2070 Acc: 0.9180
val Loss: 0.2031 Acc: 0.9150
Epoch 20/24
----------
train Loss: 0.2379 Acc: 0.9098
val Loss: 0.2212 Acc: 0.9216
Epoch 21/24
----------
train Loss: 0.2744 Acc: 0.8566
val Loss: 0.2183 Acc: 0.9150
Epoch 22/24
----------
train Loss: 0.2533 Acc: 0.8975
val Loss: 0.2243 Acc: 0.9020
Epoch 23/24
----------
train Loss: 0.2836 Acc: 0.8934
val Loss: 0.1995 Acc: 0.9150
Epoch 24/24
----------
train Loss: 0.3101 Acc: 0.8648
val Loss: 0.2064 Acc: 0.9020
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: 0.6578 Acc: 0.6557
val Loss: 0.2122 Acc: 0.9281
Epoch 1/24
----------
train Loss: 0.5144 Acc: 0.7582
val Loss: 0.1674 Acc: 0.9673
Epoch 2/24
----------
train Loss: 0.5684 Acc: 0.7541
val Loss: 0.2962 Acc: 0.8889
Epoch 3/24
----------
train Loss: 0.3955 Acc: 0.8279
val Loss: 0.1898 Acc: 0.9412
Epoch 4/24
----------
train Loss: 0.5245 Acc: 0.7951
val Loss: 0.1705 Acc: 0.9608
Epoch 5/24
----------
train Loss: 0.3450 Acc: 0.8484
val Loss: 0.1973 Acc: 0.9477
Epoch 6/24
----------
train Loss: 0.4477 Acc: 0.8320
val Loss: 0.2887 Acc: 0.9020
Epoch 7/24
----------
train Loss: 0.4129 Acc: 0.8279
val Loss: 0.1717 Acc: 0.9608
Epoch 8/24
----------
train Loss: 0.3201 Acc: 0.8730
val Loss: 0.1652 Acc: 0.9542
Epoch 9/24
----------
train Loss: 0.2915 Acc: 0.8893
val Loss: 0.1646 Acc: 0.9477
Epoch 10/24
----------
train Loss: 0.3311 Acc: 0.8730
val Loss: 0.1572 Acc: 0.9542
Epoch 11/24
----------
train Loss: 0.4210 Acc: 0.8074
val Loss: 0.2338 Acc: 0.9216
Epoch 12/24
----------
train Loss: 0.3885 Acc: 0.8238
val Loss: 0.1663 Acc: 0.9477
Epoch 13/24
----------
train Loss: 0.3609 Acc: 0.8607
val Loss: 0.1506 Acc: 0.9608
Epoch 14/24
----------
train Loss: 0.4351 Acc: 0.7828
val Loss: 0.1597 Acc: 0.9542
Epoch 15/24
----------
train Loss: 0.3308 Acc: 0.8566
val Loss: 0.1736 Acc: 0.9542
Epoch 16/24
----------
train Loss: 0.2854 Acc: 0.8934
val Loss: 0.1516 Acc: 0.9608
Epoch 17/24
----------
train Loss: 0.4018 Acc: 0.8443
val Loss: 0.1558 Acc: 0.9412
Epoch 18/24
----------
train Loss: 0.3849 Acc: 0.8197
val Loss: 0.1690 Acc: 0.9412
Epoch 19/24
----------
train Loss: 0.3671 Acc: 0.8361
val Loss: 0.1638 Acc: 0.9608
Epoch 20/24
----------
train Loss: 0.3751 Acc: 0.8238
val Loss: 0.1664 Acc: 0.9608
Epoch 21/24
----------
train Loss: 0.2596 Acc: 0.8770
val Loss: 0.1952 Acc: 0.9346
Epoch 22/24
----------
train Loss: 0.3292 Acc: 0.8648
val Loss: 0.1510 Acc: 0.9542
Epoch 23/24
----------
train Loss: 0.3271 Acc: 0.8607
val Loss: 0.1663 Acc: 0.9542
Epoch 24/24
----------
train Loss: 0.3614 Acc: 0.8607
val Loss: 0.1763 Acc: 0.9477
Training complete in 0m 28s
Best val Acc: 0.967320
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 7.162 seconds)
