Omkar2118/datasets huggingface dataset:
Drinking Waste Classification

## About the Dataset:
4 classes of drinking waste: Aluminium Cans, Glass bottles, PET (plastic) bottles and HDPE (plastic) Milk bottles.
rawimgs - images of 4 classes of waste
YOLO_imgs - images of 4 classes of waste with corresponding txt file (annotations for YOLO framework)
labels.txt - labels of the classes

## Story
This dataset was manually labelled and collected as a part of final year Individual Project at University College London. Pictures were taken with 12 MP phone camera. I created a real-time waste detection and identification system powered by YOLO framework. Use it as you like, if you could cite me in your work, would be much appreciated. Please reach out to me if this dataset actually helped you with your project.
Arkadiy Serezhkin - arkadiyhacks@gmail.com

## Acknowledgements
The dataset used parts of manually collected dataset of Gary Thung and Mindy Yang. I would like to thank them for collecting their dataset as this is not a fun thing to do (from my own experience). You can find their repository [here](https://github.com/garythung/trashnet).
ionosphere

The original ionosphere dataset from UCI machine learning repository is a binary classification dataset with dimensionality 34. There is one attribute having values all zeros, which is discarded. So the total number of dimensions are 33. The ‘bad’ class is considered as outliers class and the ‘good’ class as inliers.

Introducing paper: Isolation forest
GeoLifeCLEF 2020

The GeoLifeCLEF 2020 dataset is a large-scale remote sensing dataset. More specifically, it consists of 1.9 million species observations from the community science platform iNaturalist, each of which is paired with high-resolution covariates (RGB-IR imagery, land cover, and altitude). The dataset is roughly evenly split between the US and France, and covers over 31k plant and animal species.

Introducing paper: The GeoLifeCLEF 2020 Dataset
ARC-100

The **ARC-100** dataset was collected as part of a prototype retail checkout system titled ARC (Automatic Retail Checkout). It consists of 31,000 $640\times480$ RGB images of 100 commonly found retail items in Lahore, Pakistan. Each retail item has 310 images captured at various *logical* orientations (on a black, matte finish conveyor belt) by a Logitech C310 webcam, under a wooden hood frame illuminated by LED strips (luminance set to approximately $70lx$). In the proposed setup, images were pre-processed and standardized before feeding into a Convolutional Neural Network for identification.

Links:

- [ARC paper](https://arxiv.org/abs/2104.02832)
- [ARC-100 dataset](https://drive.google.com/drive/folders/1joDBa30_k_TegLDXZ2g5J11iLzNS3Py6)

Introducing paper: ARC: A Vision-based Automatic Retail Checkout System