amznbin

Project Description

amznbin is a deep learning image recognition project using Amazon Bin Image Dataset.

We solve two tasks,

Count: Predict the number of items in the image
Classify: Predict what item is contained in the image

To reduce the complexity of the tasks and training time,

We use images with only one kind of item. - this enables us to solve classify task as a single classification task
We specify the minimum number of repetition of an item - most items appear only once in all images, when untreated it will make testing and validating unconvincing.
We manually ruled out a number of invalid images. - check dataset/invalid_images.json to see which images are excluded.

How to Start

On root directory run pip install -r requirements.txt to install required libraries
Run make image_dataset and make load_metadata on command line to download the images and metadata (Using tmux would be a good idea)
After make load_metadata is finished, run make prepare_train and input the number of repetition you want (we recommend 20 or higher) and data augmentation mode.
When all above is finished you are ready to run our program!

Demo

Run make demo to checkout our training demo - To run the demo of our pretrained model, open jupyter notebook and run src/demo.ipynb
- our pretrained model was too big to upload on github, you can train the model and use your own trained model to use this file

Results

We accomplished 68.5714% accuracy on count task, and 41.4286% accuracy on classify task