The document discusses building an image recognition application for identifying Iraqi currency denominations. It covers preparing a dataset with labeled images of each denomination under different conditions, training an object detection model using the Turi Create library, and exporting the model for use in an iOS/Android application. The key steps are preparing a dataset with 300+ labeled photos of each denomination, training an object detection model on the dataset, evaluating the model, and exporting it for use in a mobile application.

1. Husam Aamer
from

2. Image Recognition
Applications & Dataset Preparation

3. Did you see this before ?
•Cover violence or
nudity images
facebook app
AppChief

4. Did you see this before ?
•Suggests sharing
photos with
recognized facebook
friends
Moments app
AppChief

5. Did you see this before ?
•Search for photo
content in iOS and
Android
Photos
AppChief

6. Did you see this before ?
•Cutting out a person
from an image
Sticky app

7. Did you see this before ?

8. What is image recognition ?
How to build my own app ?
Why I need it ?
Is the ability of software to identify objects, places, people, writing and
actions in images.
labeling the content of images with meta-tags, performing image content
search and guiding autonomous robots, self-driving cars and accident
avoidance systems…etc
Next slides

9. How to build my own app ?
TRAIN THE MODEL
PREPARE DATASET
BUILD AND RUN
…
…

10. What do you need ?
PREPARE DATASET
Classify image ? Detect multiple objects
inside image ?
or

11. Image recognition types
&
IMAGE
CLASSIFICATION
OBJECT
DETECTION
PREPARE DATASET

12. vs
OBJECT
DETECTION
input
output
Image Image
Class labelsClass label
OBJECT
DETECTION
INSTANCE
SEGMENTATION
types
+
bounding box
+
bounding boxes
+
segmentation
PREPARE DATASET
Image recognition types
-
IMAGE
CLASSIFICATION
CLASSIFICATION
CLASSIFICATION
+ LOCALIZATION

13. vs
IMAGE
CLASSIFICATION
OBJECT
DETECTION
CLASSIFICATION
CLASSIFICATION
+ LOCALIZATION
OBJECT
DETECTION
INSTANCE
SEGMENTATION
types
PREPARE DATASET
Image recognition types
Example

14. vs
IMAGE
CLASSIFICATION
OBJECT
DETECTION
CLASSIFICATION
CLASSIFICATION
+ LOCALIZATION
OBJECT
DETECTION
INSTANCE
SEGMENTATION
types
PREPARE DATASET
Image recognition types
WE
WILL
CONTINUE
WITH
OBJECT
DETECTION

15. Let’s create a money reader model
STEP 1. Naming objects (Object labels)
PREPARE DATASET / OBJECT DETECTION
1 IQD_50000_ar
3 IQD_25000_ar
5 IQD_10000_ar
7 IQD_5000_ar
9 IQD_1000_ar
11 IQD_500_ar
13 IQD_250_ar
2 IQD_50000_en
4 IQD_25000_en
6 IQD_10000_en
8 IQD_5000_en
10 IQD_1000_en
12 IQD_500_en
14 IQD_250_en

16. Let’s create a money reader model
STEP 2. Take photos for each money face AS MUCH AS YO CAN
PREPARE DATASET / OBJECT DETECTION
For better accuracy take hundreds of photos with
• Different backgrounds
• Different positions
• Different light conditions
• Different orientations

17. Let’s create a money reader model
PREPARE DATASET / OBJECT DETECTION
• Different backgrounds
• Different positions
• Different light conditions
• Different orientations
STEP 2. Take photos for each money face AS MUCH AS YO CAN

18. STEP 2. Take photos for each money face AS MUCH AS YO CAN
Let’s create a money reader model
PREPARE DATASET / OBJECT DETECTION

19. Let’s create a money reader model
2. Take about 300 photos for each money face
PREPARE DATASET / OBJECT DETECTION

20. Let’s create a money reader model
2. Take about 300 photos for each money face
PREPARE DATASET / OBJECT DETECTION

21. Let’s create a money reader model
2. Take about 300 photos for each money face
PREPARE DATASET / OBJECT DETECTION

22. Let’s create a money reader model
STEP 3. Labeling objects inside images
PREPARE DATASET / OBJECT DETECTION
Label : IQD_50000_en
x : 6
y : 120
width : 150
heigh : 370
Objects :
Object #1

23. Let’s create a money reader model
STEP 3. Labeling objects inside images
PREPARE DATASET / OBJECT DETECTION
Label : IQD_50000_en
x : 90
y : 125
width : 313
heigh : 313
Objects :
Object #1

24. CAUTION : Some training libraries prefers different coordinates system in labeling
PREPARE DATASET / OBJECT DETECTION
X, Y, Width , Height midX, midY, Width , Height
minX, minY, maxX , maxY
It’s recommended to check the library needs you want to use for training before start labeling

25. How many photos do you think we need
for each label ?
PREPARE DATASET / OBJECT DETECTION
10 ?
20 ?
50 ?
100 ?
200 ?
300 ?

26. Assuming 300 photos
is good for our model
let’s calculate time required
PREPARE DATASET / OBJECT DETECTION
300 image x 14 Label x (5 sec) taking photo x (30 sec) labeling
175 hours !!! 7 Days !

27. We made a timesaving app
PREPARE DATASET / OBJECT DETECTION
Only 1 hour
300 image x 14 Label x (5 sec) taking photo x (60 sec) labeling
50 image x 14 Label x (5 sec) taking photo x (0 sec) labeling

28. PREPARE DATASET / OBJECT DETECTION
1 2 3Create labels Capture Generate

29. PREPARE DATASET / OBJECT DETECTION
3 Transfer
Easy Dataset

30. PREPARE DATASET / OBJECT DETECTION
Demo time
Easy Dataset

31. PREPARE DATASET / OBJECT DETECTION
Money Reader - ‫العملة‬ ‫قارئ‬
Final live app
Final dataset
kaggle.com/husamaamer/iraqi-currency-
~1 GB
Iraqi Money ‫العراقية‬ ‫العملة‬
Object detection dataset for Iraqi currency

32. MODEL TRAINING
import turicreate as tc
import os
# Define all images annotations with bounding box details (I am showing only 1)
annotations = tc.SArray([
[{
“label”:”5000ar",
“type":"rectangle",
“coordinates”:{“y":188.5,"x":207,"width":304,"height":152}
}],
… , … …
])

33. MODEL TRAINING
import turicreate as tc
import os
# Define all images annotations with bounding box details (I am showing only 1)
annotations = tc.SArray([
[{
“label”:”5000ar",
“type":"rectangle",
“coordinates”:{“y":188.5,"x":207,"width":304,"height":152}
}],
… , … …
])
# 1. Load images (Note: you can ignore 'Not a JPEG file' errors)
data = tc.image_analysis.load_images('mr_turi_ic', with_path=True)
data['label'] = data['path'].apply(lambda path: os.path.basename(os.path.dirname(path)))
data['annotations'] = tc.SArray(data=annotations, dtype=list)

34. MODEL TRAINING
import turicreate as tc
import os
# Define all images annotations with bounding box details (I am showing only 1)
annotations = tc.SArray([
[{
“label”:”5000ar",
“type":"rectangle",
“coordinates”:{“y":188.5,"x":207,"width":304,"height":152}
}],
… , … …
])
# 1. Load images (Note: you can ignore 'Not a JPEG file' errors)
data = tc.image_analysis.load_images('mr_turi_ic', with_path=True)
data['label'] = data['path'].apply(lambda path: os.path.basename(os.path.dirname(path)))
data['annotations'] = tc.SArray(data=annotations, dtype=list)
# Make a train-test split
train_data, test_data = data.random_split(0.8)

35. MODEL TRAINING
import turicreate as tc
import os
# Define all images annotations with bounding box details (I am showing only 1)
annotations = tc.SArray([
[{
“label”:”5000ar",
“type":"rectangle",
“coordinates”:{“y":188.5,"x":207,"width":304,"height":152}
}],
… , … …
])
# 1. Load images (Note: you can ignore 'Not a JPEG file' errors)
data = tc.image_analysis.load_images('mr_turi_ic', with_path=True)
data['label'] = data['path'].apply(lambda path: os.path.basename(os.path.dirname(path)))
data['annotations'] = tc.SArray(data=annotations, dtype=list)
# Make a train-test split
train_data, test_data = data.random_split(0.8)
# Create a model using Turi Create's object detector API
model = tc.object_detector.create(train_data, max_iterations=1000)
# Save the predictions to an SArray
predictions = model.predict(test_data)
# Evaluate the model and save the results into a dictionary
metrics = model.evaluate(test_data)
print('Precision' , metrics['mean_average_precision'])

36. MODEL TRAINING
import turicreate as tc
import os
# Define all images annotations with bounding box details (I am showing only 1)
annotations = tc.SArray([
[{
“label”:”5000ar",
“type":"rectangle",
“coordinates”:{“y":188.5,"x":207,"width":304,"height":152}
}],
… , … …
])
# 1. Load images (Note: you can ignore 'Not a JPEG file' errors)
data = tc.image_analysis.load_images('mr_turi_ic', with_path=True)
data['label'] = data['path'].apply(lambda path: os.path.basename(os.path.dirname(path)))
data['annotations'] = tc.SArray(data=annotations, dtype=list)
# Make a train-test split
train_data, test_data = data.random_split(0.8)
# Create a model using Turi Create's object detector API
model = tc.object_detector.create(train_data, max_iterations=1000)
# Save the predictions to an SArray
predictions = model.predict(test_data)
# Evaluate the model and save the results into a dictionary
metrics = model.evaluate(test_data)
print('Precision' , metrics['mean_average_precision'])
# Save the model for later use in Turi Create
model.save(‘turi_ic.model')
# Export for use in Core ML file to the current directory
model.export_coreml('turi_ic.mlmodel')

37. Thanks for listening,