Udacity project: Dog breed classification
Project: Write an Algorithm for a Dog Identification App
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
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The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.
Why We're Here
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).
In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
The Road Ahead
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
- Step 0: Import Datasets
- Step 1: Detect Humans
- Step 2: Detect Dogs
- Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
- Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 5: Write your Algorithm
- Step 6: Test Your Algorithm
Step 0: Import Datasets
Make sure that you've downloaded the required human and dog datasets:
Note: if you are using the Udacity workspace, you DO NOT need to re-download these - they can be found in the
/data folder as noted in the cell below.- Download the dog dataset. Unzip the folder and place it in this project's home directory, at the location
/dog_images. - Download the human dataset. Unzip the folder and place it in the home directory, at location
/lfw.
Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.
In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays
human_files and dog_files.
import numpy as np
from glob import glob
# load filenames for human and dog images
human_files = np.array(glob("/data/lfw/*/*"))
dog_files = np.array(glob("/data/dog_images/*/*/*"))
# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
import numpy as np
from glob import glob
# load filenames for human and dog images
human_files = np.array(glob("/data/lfw/*/*"))
dog_files = np.array(glob("/data/dog_images/*/*/*"))
# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
There are 13233 total human images. There are 8351 total dog images.
import cv2 import matplotlib.pyplot as plt %matplotlib inline # extract pre-trained face detector face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml') # load color (BGR) image img = cv2.imread(human_files[0]) # convert BGR image to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # find faces in image faces = face_cascade.detectMultiScale(gray) # print number of faces detected in the image print('Number of faces detected:', len(faces)) # get bounding box for each detected face for (x,y,w,h) in faces: # add bounding box to color image cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) # convert BGR image to RGB for plotting cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # display the image, along with bounding box plt.imshow(cv_rgb) plt.show()# returns "True" if face is detected in image stored at img_path def face_detector(img_path): img = cv2.imread(img_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray) return len(faces) > 0from tqdm import tqdm human_files_short = human_files[:100] dog_files_short = dog_files[:100] #-#-# Do NOT modify the code above this line. #-#-# ## TODO: Test the performance of the=0-97h . 567u . face_detector algorithm ## on the images in human_files_short and dog_files_short. human_faces = [face_detector(image) for image in human_files_short].count(True)/len(human_files_short) dog_faces = [face_detector(image) for image in dog_files_short].count(True)/len(dog_files_short) print('percentage of faces in face images {0:.0%}'.format(human_faces)) print('percentage of faces in dog images {0:.0%}'.format(dog_faces))### (Optional) ### TODO: Test performance of anotherface detection algorithm. ### Feel free to use as many code cells as needed.import torch import torchvision.models as models # define VGG16 model VGG16 = models.vgg16(pretrained=True) # check if CUDA is available use_cuda = torch.cuda.is_available() # move model to GPU if CUDA is available if use_cuda: VGG16 = VGG16.cuda()from PIL import Image import torchvision.transforms as transforms vgg_transform = transforms.Compose([transforms.Resize(255), transforms.CenterCrop((224,224)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) def VGG16_predict(img_path): ''' Use pre-trained VGG-16 model to obtain index corresponding to predicted ImageNet class for image at specified path Args: img_path: path to an image Returns: Index corresponding to VGG-16 model's prediction ''' ## TODO: Complete the function. ## Load and pre-process an image from the given img_path ## Return the *index* of the predicted class for that image img = Image.open(img_path) image = vgg_transform(img).unsqueeze(0).cuda() output = VGG16(image) _, predicted = torch.max(output, 1) index = int(predicted.data) return index # predicted class indexVGG16_predict('/data/dog_images/train/001.Affenpinscher/Affenpinscher_00001.jpg')252### returns "True" if a dog is detected in the image stored at img_path def dog_detector(img_path): ## TODO: Complete the function. if VGG16_predict(img_path) in range(151,269): return True return Falsedog_detector('images/Labrador_retriever_06449.jpg')### TODO: Test the performance of the dog_detector function ### on the images in human_files_short and dog_files_short. dog_det_dog_short = [dog_detector(image) for image in dog_files_short].count(True)/len(dog_files_short) dog_det_human_short = [dog_detector(image) for image in human_files_short].count(True)/len(human_files_short)In [12]:print('detected dogs in dog images {0:.0%}'.format(dog_det_dog_short)) print('detected dogs in human images {0:.0%}'.format( dog_det_human_short))In [13]:### (Optional) ### TODO: Report the performance of another pre-trained network. ### Feel free to use as many code cells as needed.Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 10%. In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.
Brittany Welsh Springer Spaniel
Curly-Coated Retriever American Water Spaniel
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We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset
Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located atdog_images/train,dog_images/valid, anddog_images/test, respectively). You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!import os from torchvision import datasets from PIL import ImageFile ### TODO: Write data loaders for training, validation, and test sets ## Specify appropriate transforms, and batch_sizes batch_size = 16 num_workers = 0 ImageFile.LOAD_TRUNCATED_IMAGES = True data_dir = '/data/dog_images/' train_dir = os.path.join(data_dir, 'train/') valid_dir = os.path.join(data_dir, 'valid/') test_dir = os.path.join(data_dir, 'test/') 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])]), 'test': transforms.Compose([transforms.Resize(size=(224,224)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) }train_data = datasets.ImageFolder(train_dir, transform=data_transforms['train']) valid_data = datasets.ImageFolder(valid_dir, transform=data_transforms['val']) test_data = datasets.ImageFolder(test_dir, transform=data_transforms['test']) train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=num_workers, shuffle=True) valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size, num_workers=num_workers, shuffle=False) test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=num_workers, shuffle=False) loaders_scratch = { 'train': train_loader, 'valid': valid_loader, 'test': test_loader }import torch.nn as nn import torch.nn.functional as F num_classes = 133 # total classes of dog breeds # define the CNN architecture class Net(nn.Module): ### TODO: choose an architecture, and complete the class def __init__(self): super(Net, self).__init__() ## Define layers of a CNN #input channels 3: RGB # output channels:16 nb filters self.conv1 = torch.nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1) self.bn1 = nn.BatchNorm2d(16) self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1) self.bn2 = nn.BatchNorm2d(32) self.conv3 = torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) self.bn3 = nn.BatchNorm2d(64) self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0) in_size = 64 * 28 * 28 self.fc1 = torch.nn.Linear(in_size, 64) # 64 input features, 133 output features for our 133 defined classes self.fc2 = torch.nn.Linear(64, num_classes) self.dropout = nn.Dropout(0.5) def forward(self, x): # Computes the activation of the first convolution x = F.elu(self.bn1(self.conv1(x))) x = self.pool(x) x = F.elu(self.bn2(self.conv2(x))) x = self.pool(x) x = F.elu(self.bn3(self.conv3(x))) x = self.pool(x) x = x.view(-1, 64 * 28 * 28) x = F.elu(self.fc1(x)) x = self.dropout(x) x = self.fc2(x) return (x) #-#-# You so NOT have to modify the code below this line. #-#-# # instantiate the CNN model_scratch = Net() print(model_scratch) # move tensors to GPU if CUDA is available if use_cuda: model_scratch.cuda()import torch.optim as optim ### TODO: select loss function criterion_scratch = nn.CrossEntropyLoss() ### TODO: select optimizer optimizer_scratch = optim.SGD(model_scratch.parameters(), lr=0.03)def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path): """returns trained model""" # initialize tracker for minimum validation loss last_validation_loss = None if last_validation_loss is not None: valid_loss_min = last_validation_loss else: valid_loss_min = np.Inf for epoch in range(1, n_epochs+1): # initialize variables to monitor training and validation loss train_loss = 0.0 valid_loss = 0.0 ################### # train the model # ################### model.train() for batch_idx, (data, target) in enumerate(loaders['train']): # move to GPU if use_cuda: data, target = data.cuda(), target.cuda() ## find the loss and update the model parameters accordingly ## record the average training loss, using something like ## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss)) # initialize weights to zero optimizer.zero_grad() output = model(data) # calculate loss loss = criterion(output, target) # back prop loss.backward() # grad optimizer.step() train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss)) ###################### # validate the model # ###################### model.eval() for batch_idx, (data, target) in enumerate(loaders['valid']): # move to GPU if use_cuda: data, target = data.cuda(), target.cuda() ## update the average validation loss output = model(data) loss = criterion(output, target) valid_loss = valid_loss + ((1 / (batch_idx + 1)) * (loss.data - valid_loss)) # print training/validation statistics print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format( epoch, train_loss, valid_loss )) ## TODO: save the model if validation loss has decreased if valid_loss < valid_loss_min: torch.save(model.state_dict(), save_path) print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format( valid_loss_min, valid_loss)) valid_loss_min = valid_loss # return trained model return model# train the model model_scratch = train(100, loaders_scratch, model_scratch, optimizer_scratch, criterion_scratch, use_cuda, 'model_scratch2.pt')# load the model that got the best validation accuracy model_scratch.load_state_dict(torch.load('model_scratch2.pt'))def test(loaders, model, criterion, use_cuda): # monitor test loss and accuracy test_loss = 0. correct = 0. total = 0. model.eval() for batch_idx, (data, target) in enumerate(loaders['test']): # move to GPU if use_cuda: data, target = data.cuda(), target.cuda() # forward pass: compute predicted outputs by passing inputs to the model output = model(data) # calculate the loss loss = criterion(output, target) # update average test loss test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss)) # convert output probabilities to predicted class pred = output.data.max(1, keepdim=True)[1] # compare predictions to true label correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy()) total += data.size(0) print('Test Loss: {:.6f}\n'.format(test_loss)) print('\nTest Accuracy: %2d%% (%2d/%2d)' % ( 100. * correct / total, correct, total)) # call test function test(loaders_scratch, model_scratch, criterion_scratch, use_cuda)## TODO: Specify data loaders loaders_transfer = loaders_scratch.copy()import torchvision.models as models import torch.nn as nn ## TODO: Specify model architecture model_transfer = models.resnet50(pretrained=True) for param in model_transfer.parameters(): param.requires_grad = False model_transfer.fc = nn.Linear(2048, 133, bias=True) fc_parameters = model_transfer.fc.parameters() for param in fc_parameters: param.requires_grad = True if use_cuda: model_transfer = model_transfer.cuda()criterion_transfer = nn.CrossEntropyLoss() # criterion_transfer = criterion_scratch optimizer_transfer = optim.SGD(model_transfer.fc.parameters(), lr=0.001)# train the model model_transfer = train(50, loaders_transfer, model_transfer, optimizer_transfer, criterion_transfe-p r, use_cuda, 'model_transfer.pt')# load the model that got the best validation accuracy (uncomment the line below) model_transfer.load_state_dict(torch.load('model_transfer.pt'))test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)### TODO: Write a function that takes a path to an image as input ### and returns the dog breed that is predicted by the model. # list of class names by index, i.e. a name can be accessed like class_names[0] class_names = [item[4:].replace("_", " ") for item in loaders_transfer['train'].dataset.classes] def predict_breed_transfer(img_path): # load the image and return the predicted breed img = Image.open(img_path) image = vgg_transform(img).unsqueeze(0).cuda() output = model_transfer(image) _, predicted = torch.max(output, 1) softmax = nn.Softmax(dim=1) probs = softmax(output) prob = torch.topk(probs, 1) prob = float(("{0:.2f}".format(round(float(prob[0][0]),2)))) index = int(predicted.data) return class_names[index], probpredict_breed_transfer('/data/dog_images/train/001.Affenpinscher/Affenpinscher_00001.jpg')('Affenpinscher', 0.94)
