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Deep learning and computer vision

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Tess Ferrandez
Tess Ferrandez

Updated version of the Practical Guide to Deep Learning with some actual projects, and our process of working through deep learning projects - less end to end, more joining several models together

Engineering
1 of 83
DEEP LEARNING IN PRACTICE Tess Ferrandez – Microsoft - @TessFerrandez
TIM MITYA YANA VITO CLAUS TESS
SHOTS ON GOAL
DETECTING CANCER
SHOPLIFTING
DEEP LEARNING What’s so magical about it
int EstimatePrice(...){ price = 100000 + 67000 * area_in_sqm + 200000 * has_pool + 100000 * new_kitchen + 5000 * neighborhood_quality; return price; } Price = b + w1*area_in_sqm + w2*has_pool + ...
[LINEAR REGRESSION]
GIVEN ENOUGH SAMPLES, A NEURAL NETWORK WILL FIND THE PATTERN
GIVEN ENOUGH SAMPLES, A NEURAL NETWORK WILL FIND THE PATTERN
SEQUENCESPACE CONVOLUTIONAL NN RECURRENT NNDENSE NN
[ 0.01949719, 0.09399229, -0.01618082, -0.00876935, 0.03146157, 0.06853894, 0.00096175, -0.06854118, -0.04771797, -0.05296798, 0.02119147, 0.00511259, 0.13726683 INTERMEDIATE REPRESENTATIONEMBEDDINGSECRET CODE
RECOMMEND A BOOK
-1.0 1.0 ADULTYOUTH
ADULT FICTION (-0.6, 0.4) * (0.7, 0.9) *
FICTION ADULT MATH REFERENCES US-CENTRIC CHICK LIT FUNNY SCI-FI LAWYERS WOULD BRAD PITT PLAY A CHARACTER IN THE MOVIE? EMBEDDING [ 0.01949719, 0.09399229, -0.01618082, -0.00876935, 0.03146157, 0.06853894, 0.00096175, -0.06854118, -0.04771797, -0.05296798, 0.02119147, 0.00511259, 0.1372668
WORD EMBEDDINGS
[ 0.01949719, 0.09399229, -0.01618082, -0.00876935, 0.03146157, 0.06853894, 0.00096175, -0.06854118, -0.04771797, -0.05296798, 0.02119147, 0.00511259, 0.1372668
[FACENET] T-SNE Projection of 128D to 2D
FACE RECOGNITION DEMO
SEGMENTATION encoder decoder
encoder decoder
encoder decoder
IN PRACTICE The secrets behind the magic
Time for the Epoch Training data Validation data
MODEL LOSS ACCURACY BASIC 0.2507 91.05%
OOPSIE DOOPSIE! We’re overfitting
Chihuahua the movie [DATA AUGMENTATION]
[DROPOUT]
MODEL LOSS ACCURACY BASIC 0.2507 91.05% AUGMENTATION 0.1988 93.68%
MODEL LOSS ACCURACY BASIC 0.2507 91.05% AUGMENTATION 0.1988 93.68% TRANSFER LEARN 0.01253 99.47%
APPLIED MACHINE LEARNING When the magic is gone, and we’re left with Software Engineering
UNDERSTAND THE BUSINESS NEEDS
WHAT IS THE PROBLEM? HOW WILL THE MODEL BE USED? / REQUIREMENTS HOW IS IT DONE TODAY? IS IT FEASIBLE? ETHICAL CONCERNS UNDERSTAND THE BUSINESS NEEDS
UNDERSTAND THE BUSINESS NEEDS MINE CLEAN EXPLORE
UNDERSTAND THE BUSINESS NEEDS MINE CLEAN EXPLORE ENGINEER MODEL DEPLOY
LOTS OF LABLED SAMPLES and NO CONSEQUENTIAL DECISIONS SHOTS ON GOAL
LOUD CROWD GOAL VISIBLE SPEED/DIRECTION PLAYER DENSITY PLAYER POSES
SCENE CHANGES GOAL IN VIEW NEGATIVE SAMPLING
5S VIDEOS - AROUND ACTION NEGATIVE SAMPLES FROM ATTACKS
VGG EMBEDDINGS
90+% MODEL ACCURACY GRASS? GOAL? SCENE CHANGE
ShotNoShot https://github.com/tyiannak/pyAudioAnalysis AUDIO
PEOPLE CLUSTERS - SIZES
https://github.com/fizyr/keras-retinanet model_path = 'c:/Tess/source/vision_samples/models/resnet50_coco_best_v2.1.0.h5' model = models.load_model(model_path, backbone_name='resnet50’) image_path = 'C:/Tess/source/vision_samples/data/images/basket_image.jpg' image = read_image_bgr(image_path) image = preprocess_image(image) image, scale = resize_image(image) # process image boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0)) from keras_retinanet import models from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
GOAL / NO GOAL
SCENE CHANGE DETECTION
FOCUSED OPTICAL FLOW ON PLAYERS
DETECTING CANCER VERY FEW POSITIVE SAMPLES EXTREME ACCURACY NEEDS POTENTIAL FOR BIAS
HARD TO DIFFERENTIATE ONLY PARTIALLY LABLED EXTREMELY LARGE IMAGES
COLOR SEGMENTATION
CONVEX HULL
SHOPLIFTING VERY FEW POSITIVE SAMPLES VERY FEW SAMPLES PER ACTION TYPE VERY SENSITIVE TO BIAS
COVERED FACES ALONE MEN 20-40 HOODIES SHOPLIFTING POSES MEN 20-40 HOODIESCOVERED FACES ALONE
12:32:00CHRISTMAS FISH EYE DETECT not PREDICT HUD ARTIFACTS
NEGATIVE SAMPLES FROM SAME VIDEOS PEOPLE SHOPPING
POSE DETECTION
BACKGROUND SUBTRACTION
CLASSIFICATION AT THE BOX LEVEL
A LITTLE DOMAIN KNOWLEDGE GOES A LONG WAY
KISSKeep it Simple …
DEEP LEARNING IN PRACTICE Tess Ferrandez – Microsoft - @TessFerrandez

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