Omkar2118/datasets huggingface dataset:
Swedish Leaf Dataset

A dataset of images containing leaves from 15 tree classes. 

Image source: [https://www.cvl.isy.liu.se/en/research/datasets/swedish-leaf/](https://www.cvl.isy.liu.se/en/research/datasets/swedish-leaf/)
XA Bin-Picking

**XA Bin-Picking** is a point-cloud dataset comprising both simulated and real-world scenes with three industrial parts.  Synthesized scenes consists of 1000 training samples. The test samples are real scenes and the ground
truth instance labels are made manually. There are 20 to
30 identical types of parts randomly piled up in a scene.
Each scene contains about 60,000 boundary points. Each
point in the scene has instance annotations. The parts are
texture-less and have no discernible color. Both of training samples and test sam-
ples only contain the boundary points of parts.

Source: [A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color](/paper/a-convolutional-neural-network-for-point)
Image Source: [A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color](/paper/a-convolutional-neural-network-for-point)

Introducing paper: A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color
German Credit Dataset

Two datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical/symbolic attributes and is in the file "german.data". 

For algorithms that need numerical attributes, Strathclyde University produced the file "german.data-numeric". This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables. Several attributes that are ordered categorical (such as attribute 17) have been coded as integer. This was the form used by StatLog. 

This dataset requires use of a cost matrix:

|   | Good | Bad |
|---|---|---|
| Good | 0 | 1 |
| Bad | 5 | 0 |

The rows represent the actual classification and the columns the predicted classification. 

It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1).
Monkey V1 dataset

This dataset is used for neural co-training. 
mtl_monkey_dataset: was used for our MTL-Monkey model and involves neural responses that were predicted by a single-task trained model on real monkey V1.
mtl_oracle_dataset: was used for our MTL-Oracle model and involves neural responses that were predicted by our image classification oracle.
mtl_shuffled_dataset: was used for our MTL-Shuffled model and is the result of shuffling the mtl_monkey_dataset across images.

Introducing paper: Towards robust vision by multi-task learning on monkey visual cortex
S&P 500 Intraday Data
Full name: S&P 500 Index Intraday Data with 1min Interval

##### Technical Information
Dates range from 2017-09-11 to 2018-02-16 and the time interval is 1 minute.
This is a MultiIndex CSV file, to load in pandas use:

`dataset = pd.read_csv('dataset.csv', index_col=0, header=[0, 1]).sort_index(axis=1)`

Stocks that entered or exited the Index during the dataset time range are omitted.

##### Collection & Processing
These are the scripts used for collecting the data, and also utilities to clean & scale the dataset & convert it to a numpy array:
https://github.com/nickdl/alpha

Introducing paper: Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders