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https://github.com/gsurma/image_classifier
CNN image classifier implemented in Keras Notebook 🖼️.
https://github.com/gsurma/image_classifier
ai artificial-intelligence cnn cnn-classification cnn-keras colaboratory convolutional-neural-networks efficientnet efficientnet-keras google-colab image-classification image-recognition keras machine-learning ml notebook python transfer-learning
Last synced: 7 days ago
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CNN image classifier implemented in Keras Notebook 🖼️.
- Host: GitHub
- URL: https://github.com/gsurma/image_classifier
- Owner: gsurma
- License: mit
- Created: 2018-11-07T16:40:27.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-08-18T07:31:29.000Z (over 3 years ago)
- Last Synced: 2025-01-02T21:16:28.042Z (21 days ago)
- Topics: ai, artificial-intelligence, cnn, cnn-classification, cnn-keras, colaboratory, convolutional-neural-networks, efficientnet, efficientnet-keras, google-colab, image-classification, image-recognition, keras, machine-learning, ml, notebook, python, transfer-learning
- Language: Jupyter Notebook
- Homepage: https://gsurma.github.io
- Size: 6.4 MB
- Stars: 182
- Watchers: 13
- Forks: 98
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# Image Classifier
Python Jupyter Notebook with **Convolutional Neural Network** image classifier implemented in **Keras** 🖼️. It's **[Google Colab](https://colab.research.google.com/)** ready.
Check out corresponding Medium article:
[Image Classifier - Cats🐱 vs Dogs🐶 with Convolutional Neural Networks (CNNs) and Google Colab’s Free GPU](https://towardsdatascience.com/image-classifier-cats-vs-dogs-with-convolutional-neural-networks-cnns-and-google-colabs-4e9af21ae7a8)
## Usage
Structure your data as follows:
data/
training/
class_a/
class_a01.jpg
class_a02.jpg
...
class_b/
class_b01.jpg
class_b02.jpg
...
validation/
class_a/
class_a01.jpg
class_a02.jpg
...
class_b/
class_b01.jpg
class_b02.jpg
...
For binary classifications you are good to go!
For non-binary classifications:
* add other classes to training and validation directories
* change class_mode from "binary" to "categorical"
* change loss function from "binary\_crossentropy" to "categorical\_crossentropy"
## Performance
**Dataset:** [Dogs vs Cats](https://www.kaggle.com/c/dogs-vs-cats)
**Description:** Binary classification. Two classes two distinguish - **dogs** and **cats**.
**Training:** 10 000 images per class
**Validation:** 2 500 images per class
### model_1
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_5 (Conv2D) (None, 198, 198, 32) 896
_________________________________________________________________
activation_9 (Activation) (None, 198, 198, 32) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 99, 99, 32) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 97, 97, 32) 9248
_________________________________________________________________
activation_10 (Activation) (None, 97, 97, 32) 0
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 48, 48, 32) 0
_________________________________________________________________
flatten_3 (Flatten) (None, 73728) 0
_________________________________________________________________
dense_5 (Dense) (None, 16) 1179664
_________________________________________________________________
activation_11 (Activation) (None, 16) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 16) 0
_________________________________________________________________
dense_6 (Dense) (None, 1) 17
_________________________________________________________________
activation_12 (Activation) (None, 1) 0
=================================================================
Total params: 1,189,825
Trainable params: 1,189,825
Non-trainable params: 0
_________________________________________________________________
### model_2
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 198, 198, 32) 896
_________________________________________________________________
activation_6 (Activation) (None, 198, 198, 32) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 99, 99, 32) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 97, 97, 32) 9248
_________________________________________________________________
activation_7 (Activation) (None, 97, 97, 32) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 48, 48, 32) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 46, 46, 64) 18496
_________________________________________________________________
activation_8 (Activation) (None, 46, 46, 64) 0
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 23, 23, 64) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 33856) 0
_________________________________________________________________
dense_3 (Dense) (None, 64) 2166848
_________________________________________________________________
activation_9 (Activation) (None, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 64) 0
_________________________________________________________________
dense_4 (Dense) (None, 1) 65
_________________________________________________________________
activation_10 (Activation) (None, 1) 0
=================================================================
Total params: 2,195,553
Trainable params: 2,195,553
Non-trainable params: 0
_________________________________________________________________
### model_3
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 198, 198, 32) 896
_________________________________________________________________
activation_6 (Activation) (None, 198, 198, 32) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 99, 99, 32) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 97, 97, 64) 18496
_________________________________________________________________
activation_7 (Activation) (None, 97, 97, 64) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 48, 48, 64) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 46, 46, 128) 73856
_________________________________________________________________
activation_8 (Activation) (None, 46, 46, 128) 0
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 23, 23, 128) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 67712) 0
_________________________________________________________________
dense_3 (Dense) (None, 64) 4333632
_________________________________________________________________
activation_9 (Activation) (None, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 64) 0
_________________________________________________________________
dense_4 (Dense) (None, 1) 65
_________________________________________________________________
activation_10 (Activation) (None, 1) 0
=================================================================
Total params: 4,426,945
Trainable params: 4,426,945
Non-trainable params: 0
_________________________________________________________________
### model_4
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_7 (Conv2D) (None, 198, 198, 32) 896
_________________________________________________________________
activation_11 (Activation) (None, 198, 198, 32) 0
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 99, 99, 32) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 97, 97, 64) 18496
_________________________________________________________________
activation_12 (Activation) (None, 97, 97, 64) 0
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 48, 48, 64) 0
_________________________________________________________________
conv2d_9 (Conv2D) (None, 46, 46, 128) 73856
_________________________________________________________________
activation_13 (Activation) (None, 46, 46, 128) 0
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 23, 23, 128) 0
_________________________________________________________________
flatten_3 (Flatten) (None, 67712) 0
_________________________________________________________________
dense_5 (Dense) (None, 128) 8667264
_________________________________________________________________
activation_14 (Activation) (None, 128) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 128) 0
_________________________________________________________________
dense_6 (Dense) (None, 1) 129
_________________________________________________________________
activation_15 (Activation) (None, 1) 0
=================================================================
Total params: 8,760,641
Trainable params: 8,760,641
Non-trainable params: 0
_________________________________________________________________
### model_5
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 200, 200, 32) 896
_________________________________________________________________
conv2d_2 (Conv2D) (None, 200, 200, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 100, 100, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 100, 100, 64) 18496
_________________________________________________________________
conv2d_4 (Conv2D) (None, 100, 100, 64) 36928
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 50, 50, 64) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 50, 50, 128) 73856
_________________________________________________________________
conv2d_6 (Conv2D) (None, 50, 50, 128) 147584
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 25, 25, 128) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 25, 25, 256) 295168
_________________________________________________________________
conv2d_8 (Conv2D) (None, 25, 25, 256) 590080
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 12, 12, 256) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 36864) 0
_________________________________________________________________
dense_1 (Dense) (None, 256) 9437440
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_2 (Dense) (None, 256) 65792
_________________________________________________________________
dropout_2 (Dropout) (None, 256) 0
_________________________________________________________________
dense_3 (Dense) (None, 1) 257
_________________________________________________________________
activation_1 (Activation) (None, 1) 0
=================================================================
Total params: 10,675,745
Trainable params: 10,675,745
Non-trainable params: 0
_________________________________________________________________
## Author
**Greg (Grzegorz) Surma**
[**PORTFOLIO**](https://gsurma.github.io)
[**GITHUB**](https://github.com/gsurma)
[**BLOG**](https://medium.com/@gsurma)
