The document outlines a step-by-step process for using Keras CNN pre-trained deep learning models to recognize flower species through the Flower17 dataset. It includes steps for data acquisition, training configuration, and testing, emphasizing the creation of a structured dataset and the use of a configuration file in JSON format for various model parameters. The results highlight the model's performance, achieving 98.53% accuracy in rank-1 and 100% in rank-5 classifications.

1. Keras CNN Pre-
trained Deep
Learning models for
Flower Recognition

2. “
2

3. Steps
3
Step 1
Dataset
Acquisition
Step 3
Testing
Step 2
Training
Deep Learning

4. 1.
Data Acquisition

5. Data
Acquisition
5

6. Data Acquisition
o Download the
FLOWER17 dataset
from here:
http://www.robots.ox.ac.
uk/~vgg/data/flowers/17/
o Unzip the file and you
will see all the 1360
images listed in one
single folder
named *.jpg.
o The FLOWERS17
dataset has 680
images of 17 flower
species classes with
40 images per class.
6
o To build our
training dataset, we
need to create a
master folder
named dataset,
and create Train
and Test folder
inside it.
o Inside train folder,
create 17 folders
corresponding to
the flower species
labels.

7. 7
Folder Structure

8. Organize
Dataset
Code

9. 9

10. 2.
Data Training

11. Configuration
File
◦ This is the configuration file or
the settings file we will be
using to provide inputs to our
system.
◦ This is just a .json file which is a
key-value pair file format to
store data effectively.
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12. 12
1. Model:
The model key
takes the
parameters –
’mobilenet’.
2. Weights:
The weights key takes the
value ’imagenet’ as we are
using weights from
imagenet. You can also set
this to ’None’.
3. Include-top:
This key takes the
value false specifying that we
are going to take the
features from any
intermediate layer of the
network.
4. Test_size:
The test_size key takes the
value in the range (0.10 - 0.90).
This is to make a split between
your overall data into training
and testing.
5. Seed:
The seed key takes any
value to reproduce same
results everytime you run
the code.
6. Num_classes:
The num_classes specifies the
number of classes or labels
considered for the image
classification problem.

13. “
Feature Extraction
13

14. “
14

15. “
15
Output

16. Output
Files
16

17. 17

18. 18

19. Output
19

20. Confusion Matrix
20

21. Trained
Model
21
Model File

22. “
Results File
22

23. Accuracy
Rank-1
Accuracy
Rank-5
Accuracy
Mobilenet 98.53% 100.00%
23

24. 3.
Test Un-seen Data

25. 25

26. 26

27. References
27
1. https://gogul09.github.io/software/flower-recognition-deep-learning

28. 28
THANKS!
Any questions?