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add specific results (and parameters used) that were achieved after the CS 229 project deadline.

Thanks to the Stanford CS 229 autumn 2016-2017 teaching staff for a great class!.

Push to the branch: git push origin my-new-feature.

Commit your changes: git commit -m 'Add some feature'.

Create your feature branch: git checkout -b my-new-feature.

These royalty-free Trash Can Icon Images are available in PNG, SVG, AI, EPS, base64, and other formats & dimensions.

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Preprocessing the data involves deleting the data/dataset-resized folder and then calling python resize.py from trashnet/data. 3,792 Trash Can Icons Find & Download 3,792 Trash Can Icons in Line, Flat, Glyph, Colored Outline, and many more design styles for web, mobile, and graphic design projects. If adding more data, then the new files must be enumerated properly and put into the appropriate folder in data/dataset-original and then preprocessed.

Trash can download install#

Pip install numpy scipy Usage Step 1: Prepare the data You'll need the following Lua packages:ДЄfter installing Torch, you can install these packages by running the following:

Trash can download code#

We wrote code in Lua using Torch you can find installation instructions If you are using the dataset, please give a citation of this repository. If you are planning on using the Python code to preprocess the original dataset, then download dataset-original.zip from the link below and place the unzipped folder inside of the data folder.

The size of the original dataset, ~3.5GB, exceeds the git-lfs maximum size so it has been uploaded to Google Drive. The devices used were Apple iPhone 7 Plus, Apple iPhone 5S, and Apple iPhone SE. The pictures have been resized down to 512 x 384, which can be changed in data/constants.py (resizing them involves going through step 1 in usage). The pictures were taken by placing the object on a white posterboard and using sunlight and/or room lighting. Currently, the dataset consists of 2527 images: The dataset spans six classes: glass, paper, cardboard, plastic, metal, and trash. This repository contains the dataset that we collected. The convolutional neural network results on the poster are dated since we continued working after the end of the quarter and were able to achieve around 75% test accuracy (with 70/13/17 train/val/test split) after changing the weight initialization to the Kaiming method. Code (only for the convolutional neural network) and dataset for mine and Mindy Yang's final project for Stanford's CS 229: Machine Learning class.