Omkar2118/datasets huggingface dataset:
NELL-995

NELL-995 KG Completion Dataset

Introducing paper: DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
Food-101N
Full name: Food-101N

The Food-101N dataset is introduced in "CleanNet: Transfer Learning for Scalable 
Image Training with Label Noise (CVPR'18). It is an image dataset containing about 310,009 images of food recipes classified in 101 classes (categories). Food-101N and the Food-101 dataset share the same 101 classes, whereas Food-101N has much more images and is more noisy.

Food-101N is designed for the following two tasks:
1)Learning image classification with label noise
2)Label noise detection
KolektorSDD
Full name: Kolektor Surface-Defect Dataset

The dataset is constructed from images of defective production items that were provided and annotated by [Kolektor Group d.o.o.](https://www.kolektordigital.com/en/advanced-visual-tecnologies). The images were captured in a controlled industrial environment in a real-world case.

The dataset consists of 399 images at 500 x ~1250 px in size.

Please cite our paper published in the Journal of Intelligent Manufacturing when using this dataset:

```
@article{Tabernik2019JIM,
  author = {Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and 
  Sko{\v{c}}aj, Danijel},
  journal = {Journal of Intelligent Manufacturing},
  title = {{Segmentation-Based Deep-Learning Approach for Surface-Defect Detection}},
  year = {2019},
  month = {May},
  day = {15},
  issn={1572-8145},
  doi={10.1007/s10845-019-01476-x}
}
```

Introducing paper: Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
CIFAR10-DVS
Full name: CIFAR10-DVS

**CIFAR10-DVS** is an event-stream dataset for object classification. 10,000 frame-based images that come from CIFAR-10 dataset are converted into 10,000 event streams with an event-based sensor, whose resolution is 128×128 pixels. The dataset has an intermediate difficulty with 10 different classes. The repeated closed-loop smooth (RCLS) movement of frame-based images is adopted to implement the conversion. Due to the transformation, they produce rich local intensity changes in continuous time which are quantized by each pixel of the event-based camera.

Source: [Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor](https://arxiv.org/abs/2008.06204)
Image Source: [https://www.frontiersin.org/articles/10.3389/fnins.2017.00309/full](https://www.frontiersin.org/articles/10.3389/fnins.2017.00309/full)
In-Shop
Full name: In-shop Clothes Retrieval Benchmark

In-shop Clothes Retrieval Benchmark evaluates the performance of in-shop Clothes Retrieval. This is a large subset of DeepFashion, containing large pose and scale variations. It also has large diversities, large quantities, and rich annotations, including:

- 7,982 number of clothing items;
- 52,712 number of in-shop clothes images, and ~200,000 cross-pose/scale pairs;

Each image is annotated by bounding box, clothing type and pose type.

Introducing paper: DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations
Ecoli

The **Ecoli** dataset is a dataset for protein localization. It contains 336 E.coli proteins split into 8 different classes.

Introducing paper: A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Yeast

Yeast dataset consists of a protein-protein interaction network. Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large-scale data sets is important to present-day biology.

Source: [http://vlado.fmf.uni-lj.si/pub/networks/data/bio/Yeast/Yeast.htm](http://vlado.fmf.uni-lj.si/pub/networks/data/bio/Yeast/Yeast.htm)

Introducing paper: Topological structure analysis of the protein-protein interaction network in budding yeast
MOT20
Full name: MOT20

**MOT20** is a dataset for multiple object tracking. The dataset contains 8 challenging video sequences (4 train, 4 test) in unconstrained environments, from crowded places such as train stations, town squares and a sports stadium.
Image Source: [https://motchallenge.net/vis/MOT20-04](https://motchallenge.net/vis/MOT20-04)
EGTEA
Full name: EGTEA Gaze+

Extended GTEA Gaze+
EGTEA Gaze+ is a large-scale dataset for FPV actions and gaze. It subsumes GTEA Gaze+ and comes with HD videos (1280x960), audios, gaze tracking data, frame-level action annotations, and pixel-level hand masks at sampled frames.
Specifically, EGTEA Gaze+ contains 28 hours (de-identified) of cooking activities from 86 unique sessions of 32 subjects. These videos come with audios and gaze tracking (30Hz). We have further provided human annotations of actions (human-object interactions) and hand masks.

The action annotations include 10325 instances of fine-grained actions, such as "Cut bell pepper" or "Pour condiment (from) condiment container into salad".

The hand annotations consist of 15,176 hand masks from 13,847 frames from the videos.

Source: [http://cbs.ic.gatech.edu/fpv/](http://cbs.ic.gatech.edu/fpv/)
Image Source: [http://cbs.ic.gatech.edu/fpv/](http://cbs.ic.gatech.edu/fpv/)

Introducing paper: In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video
BIPED
Full name: Barcelona Images for Perceptual Edge Detection

#Details
It contains 250 outdoor images of 1280$\times$720 pixels each. These images have been carefully annotated by experts on the computer vision field, hence no redundancy has been considered. In spite of that, all results have been cross-checked several times in order to correct possible mistakes or wrong edges by just one subject. This dataset is publicly available as a benchmark for evaluating edge detection algorithms. The generation of this dataset is motivated by the lack of edge detection datasets, actually, there is just one dataset publicly available for the edge detection task published in 2016 (MDBD: Multicue Dataset for Boundary Detection—the subset for edge detection). The level of details of the edge level annotations in the BIPED’s images can be appreciated looking at the GT, see Figs above.

BIPED dataset has 250 images in high definition. Thoses images are already split up for training and testing. 200 for training and 50 for testing.
#Version
The current version is the second one.

Introducing paper: Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection