https://github.com/reachsumit/tensorflow-mobilenet-which-car-is-that

Android app containing an Image classifier based on transfer learning CNN using Tensorflow 1.4.1 on Stanford's Imagenet cars dataset
https://github.com/reachsumit/tensorflow-mobilenet-which-car-is-that

android-application deep-neural-networks imagenet-classifier imagenet-dataset java playstore-sample python-3 tensorflow tensorflow-android transfer-learning

Last synced: 22 days ago
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Android app containing an Image classifier based on transfer learning CNN using Tensorflow 1.4.1 on Stanford's Imagenet cars dataset

• Host: GitHub
• URL: https://github.com/reachsumit/tensorflow-mobilenet-which-car-is-that
• Owner: reachsumit
• Created: 2018-01-05T16:37:39.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-20T17:27:18.000Z (over 7 years ago)
• Last Synced: 2025-03-29T16:51:12.585Z (about 2 months ago)
• Topics: android-application, deep-neural-networks, imagenet-classifier, imagenet-dataset, java, playstore-sample, python-3, tensorflow, tensorflow-android, transfer-learning
• Language: Java
• Homepage: https://play.google.com/store/apps/details?id=com.reachsumit.whichcaristhat&hl=en
• Size: 55.8 MB
• Stars: 16
• Watchers: 0
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Which car is that ?

Android app containing an Image classifier based on transfer learning CNN using Tensorflow 1.4.1 on Stanford's Imagenet cars dataset.  

![Android app icon](https://i.imgur.com/xxomPOZ.png)

## Project details

* This application is a demonstration of an image classifier built using convolutional neural network.  

* The model is trained on Stanford's imagenet dataset of 196 cars. Dataset can be accessed here: http://ai.stanford.edu/~jkrause/cars/car_dataset.html  

* The Cars dataset contains 16,185 images of 196 classes of cars. Full list of cars is present here: https://paste.ubuntu.com/26311458/  

* This project is published as an Android app available on Play Store. [LINK](https://play.google.com/store/apps/details?id=com.reachsumit.whichcaristhat&hl=en)  

* For further details, please have a look at the Medium blogpost I wrote for this project. https://medium.com/@sumit.arora/training-a-neural-network-using-mobilenets-in-tensorflow-for-image-classification-on-android-14f2792f64c1

## Screenshots

App on Play store  

![App on Play store](https://i.imgur.com/0iCyds8.png)

### Test 1 

![Test 1](https://i.imgur.com/EhfFxCq.jpg)

### Test 2  

![Test 2](https://i.imgur.com/YuGf4s3.jpg)

### Test 3  

![Test 3](https://i.imgur.com/xX2veJ9.jpg)

### Test 4  

![Test 4](https://i.imgur.com/h7INKZg.jpg)

### Test 5  

![Test 5](https://i.imgur.com/yh29bd9.jpg)

## Other commands

IMAGE_SIZE=224

ARCHITECTURE="mobilenet_0.75_${IMAGE_SIZE}"

python -m scripts.retrain \

  --bottleneck_dir=tf_files/bottlenecks \

  --how_many_training_steps=5000 \

  --model_dir=tf_files/models/"${ARCHITECTURE}" \

  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \

  --output_graph=tf_files/retrained_graph.pb \

  --output_labels=tf_files/retrained_labels.txt \

  --architecture="${ARCHITECTURE}" \

  --image_dir=tf_files/dataset

python -m scripts.retrain \

    --image_dir=tf_files/dataset \

    --learning_rate=0.0001 \

    --testing_percentage=20 \

    --validation_percentage=20 \

    --train_batch_size=32 \

    --validation_batch_size=-1 \

    --flip_left_right True \

    --random_scale=30 \

    --random_brightness=30 \

    --eval_step_interval=100 \

    --how_many_training_steps=600 \

    --architecture mobilenet_1.0_224