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Using Deep Learning to rank and tag millions of hotel images15/11/2018 - PyParis 2018
Christopher Lennan (Senior Data Scientist)@chris_lennan
Tanuj Jain (Data Scientist)@tjainn #idealoTech
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Agenda
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1. idealo.de
2. Business Motivation
3. Models and Training
4. Image Tagging
5. Image Aesthetics
6. Summary
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Some Key Facts
18 More than 18 years experience
700 “idealos” from 40 nations
Active in 6 different countries (DE, AT, ES, IT, FR, UK)
16 million users/month 1
50.000 shops
Over 330 million offers for 2 million products
Tüv certified comparison portal 2
Germany's 4th largest eCommerce website
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Motivation
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idealo hotel price comparisonhotel.idealo.de
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● 2.306.658 accommodations
● 308.519.299 images
● ~ 133 images per
accommodation
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Importance of Photography for Hotels
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“.. after price, photography is the most important factor for travelers and prospects scanning OTA sites..”
“.. Photography plays a role of 60% in the decision to book with a particular hotel ..”
“.. study published today by TripAdvisor, it would seem like photos have the greatest impact driving engagement from travelers researching on hotel and B&B pages ..”
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Position: 19Position: 1
Current image placement
Image Aesthetics
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Image AestheticsCurrent image placement
Position: 17Position: 3
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Beautiful images should appear earlier in the gallery
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Ensure different areas get depicted
1 2 3 4
5 6 7 8
Bedroom Bathroom
Restaurant Facade Fitness Studio Kitchen
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Understanding Image Content
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1. Tag the image with the hotel property area
2. Predict aesthetic quality
Two part problem
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Models & Training
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Transfer Learning
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● Use pre-trained CNN that was trained on millions of images
(e.g. MobileNet or VGG16)
● Replace top layers so that the output fits with classification task
● Train existing and new layer weights
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Transfer LearningCNN architecture (VGG16)
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Training regime
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1. Only train the newly added dense layers with high learning rate
2. Then train all layers with low learning rate
Goal: Do not juggle around the pre-trained convolutional weights too much
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Training regime
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● CEL generally used for “one-class” ground truth classifications (e.g. image tagging)
● CEL ignores inter-class relationships between score buckets
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Loss functionsCross-entropy loss (CEL)
source: https://ssq.github.io/2017/02/06/Udacity%20MLND%20Notebook/
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Loss functions
● For ordered classes, classification settings can outperform regressions
● Training on datasets with intrinsic ordering can benefit from EMD loss objective
Earth Mover’s Distance (EMD)
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Local AWS
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GPU training workflow
ECRpushCustom
AMIdatasets
nvidia-docker
EC2GPU
instance
launchDockerMachine
train script
Docker image
build
Dockerfile
SSHevaluation script
DockerMachine
EC2GPU
instance
launch
Jupyter notebook
Setup
Train
Evaluate launch evaluation container with nvidia-docker
pull image
copy existing model
S3launch training container with nvidia-docker
store train outputs
pull image
copy existing model
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Image Tagging
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Tagging Problem
● Given an image, tag it as belonging to a single class
● Multiclass classification model with classes:○ Bedroom○ Bathroom○ Foyer○ Restaurant○ Swimming Pool○ Kitchen○ View of Exterior (Facade)○ Reception
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Multiple Datasets
Will go over them one-by-one and see:
● Dataset properties● Results● Issues
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Wellness Dataset
● Idealo in-house pre-labelled images
● Mostly pictures of 2 or 3 stars properties
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Wellness Dataset
● Balanced: Equal sample count in all categories for all sets
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Wellness Dataset: Metrics
Top-1- accuracy: 86%
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Wellness Dataset: Wrong PredictionsTrue Class of these images: BATHROOM, Predicted as: RECEPTION
Rectangular structure = Reception with high probability→ BIAS!
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Wellness Dataset: Wrong PredictionsTrue Class of these images: BATHROOM
Wrong true label of images→ NOISE in the dataset!
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Correcting Bias
● Augmentation operations, same for every class:○ Random cropping○ Rotation○ Horizontal flipping
● Data enrichment:○ External data from google images
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Augmented Wellness + Google Dataset: Metrics
Top-1- accuracy: 88%
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Gotta Clean!
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Cleaning Dataset
● Hand-cleaned each category:
○ Deleted pictures that do not belong in its category
○ Removed duplicates (presence of duplicates can give us wrong
metrics)
○ Added more images from external sources for classes with a small
number of images left after cleaning
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Cleaned Data: Metrics
Top-1- accuracy: 91%
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Cleaned Dataset: Results
● Bathroom vs. Reception confusion has almost vanished!
● View_of_exterior vs Pool confusion has reduced
● Foyer performance:
○ Most misclassifications of Foyer gets assigned to
Reception
○ This is human problem as well!
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Foyer or Reception?
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Learnings so far
● The model can only be as good as the data (cleaning)
● Foyer is a hard category to predict
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Understanding Model Decisions
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Understanding Decisions: Class Activation Maps
● Use the penultimate Global Average Pooling Layer (GAP) to get class activation map
● Highlights discriminative region that lead to a classification
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Insights With CAMSwimming Pool misclassified as Bathroom
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CAM
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Insights With CAMSwimming Pool misclassified as Bathroom
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CAM
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Insights With CAMSwimming Pool misclassified as Bathroom
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CAM
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Insights With CAMSwimming Pool misclassified as Bathroom
Using rails to misidentify Pool as Bathroom.
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Insights With CAMBathroom correct classification
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CAM
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Insights With CAMBathroom correct classification
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CAM
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Insights With CAMBathroom correct classification
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CAM
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Insights With CAMBathroom correct classification
Using faucets to correctly identify Bathroom.
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Learnings so far
● Attribution techniques like CAM lend interpretability
● CAM can drive data collection in specific directions
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Tagging Next Steps
1. Add still more data
a. Explore manual tagging options for training
(Example: Amazon Mechanical Turk)
2. Add more classes
a. Fitness Studio
b. Conference Room
c. Other
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Image Aesthetics
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Ground Truth Labels
For the NIMA model we need “true” probability distribution over all classes for each image:
● AVA dataset: we have frequencies over all classes for each image
→ normalize frequencies to get “true” probability distribution
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(6.151 / 1.334)
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Iterations
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We have gone through two iterations of the aesthetic model:
● First iteration - Train on AVA Dataset
● Second iteration - Fine-tune first iteration model on in-house labelled data
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Results - first iteration
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Linear correlation coefficient (LCC): 0.5987Spearman's correlation coefficient (SCRR): 0.6072Earth Mover's Distance: 0.2018Accuracy (threshold at 5): 0.74
Aesthetic model - MobileNet
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Examples - first iterationAesthetic model
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Examples - first iterationAesthetic model
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Examples - first iterationAesthetic model
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Examples - first iterationAesthetic model
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Examples - first iterationAesthetic model
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Results - second iteration
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● We built a simple labeling application
● http://image-aesthetic-labelling-app-nima.apps.eu.idealo.com/
● ~ 12 people from idealo Reise and Data Science labeled
○ 1000 hotel images for aesthetics
● We fine-tuned the aesthetic model with 800 training images
● Built aesthetic test dataset with 200 images
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Results - second iteration
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Linear correlation coefficient (LCC): 0.7986Spearman's correlation coefficient (SCRR): 0.7743Earth Mover's Distance: 0.1236Accuracy (threshold at 5): 0.85
Aesthetic model - MobileNet
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Examples - second iterationAesthetic model
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Examples - second iterationAesthetic model
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Examples - second iterationAesthetic model
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Examples - second iterationAesthetic model
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Examples - second iterationAesthetic model
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ProductionAesthetic model
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ProductionAesthetic model
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● To date we have scored ~280 million images
● Distribution of scores (sample of 1 million scores):
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Production - Low Scores Aesthetic model
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Production - Medium Scores Aesthetic model
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Production - High Scores Aesthetic model
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Understanding Model Decisions
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Convolutional Filter Visualisations
Layer 23 MobileNet original
MobileNet Aesthetic
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Convolutional Filter Visualisations
Layer 51 MobileNet original
MobileNet Aesthetic
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Convolutional Filter Visualisations
Layer 79 MobileNet original
MobileNet Aesthetic
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Aesthetic Learnings
● Hotel specific labeled data is key - Aesthetic model improved markedly from 800
additional training samples
● NIMA only requires few samples to achieve good results (EMD loss)
● Labeled hotel images also important for test set (model evaluation)
● Training on GPU significantly improved training time (~30 fold)
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● Continue labeling images for aesthetic classifier
● Introduce new desirable biases in labeling (e.g. low technical quality == low aesthetics)
● Improve prediction speed of models (e.g. lighter CNN architectures)
Aesthetics Next Steps
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Summary
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● Transfer learning allowed us to train image tagging and aesthetic classifiers with a few
thousand domain specific samples
● Showed the importance of having noise-free data for quality predictions
● Use of attribution & visualization techniques helps understand model decisions and
improve them
Summary
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Check us out! #idealoTech
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https://github.com/idealo https://medium.com/idealo-tech-blog
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We’re hiring!
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Data Engineers, DevOps Engineers across different teamsCheck out our job postings: jobs.idealo.de
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Tanuj [email protected]@tjainn
Christopher [email protected]@chris_lennan
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THE END
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