add: dataset card (partial).

README.md

@@ -1,5 +1,19 @@
 ---
 license: apache-2.0
+language:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- depth-estimation
+task_ids:
+- depth-estimation
+pretty_name: NYU Depth V2
+tags: 
+- depth-estimation
+paperswithcode_id: nyuv2
 dataset_info:
   features:
   - name: image
@@ -15,4 +29,60 @@ dataset_info:
     num_examples: 654
   download_size: 35151124480
   dataset_size: 20452883313
----
+---
+
+# Dataset Card for MIT Scene Parsing Benchmark
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+
+## Dataset Description
+
+- **Homepage:** [NYU Depth Dataset V2 homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
+- **Repository:** Fast Depth [repository](https://github.com/dwofk/fast-depth) which was used to source the dataset in this repository. It is a preprocessed version of the original NYU Depth V2 dataset linked above. It is also used in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/nyu_depth_v2).
+- **Paper:** [Indoor Segmentation and Support Inference from RGBD Images](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) and [FastDepth: Fast Monocular Depth Estimation on Embedded Systems](https://arxiv.org/abs/1903.03273)
+- **Point of Contact:** [Nathan Silberman](mailto:silberman@@cs.nyu.edu) and [Diana Wofk](mailto:[email protected])
+
+### Dataset Summary
+
+As per the [dataset homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html):
+
+The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft [Kinect](http://www.xbox.com/kinect). It features:
+
+* 1449 densely labeled pairs of aligned RGB and depth images
+* 464 new scenes taken from 3 cities
+* 407,024 new unlabeled frames
+* Each object is labeled with a class and an instance number (cup1, cup2, cup3, etc)
+
+The dataset has several components:
+
+* Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels.
+* Raw: The raw rgb, depth and accelerometer data as provided by the Kinect.
+* Toolbox: Useful functions for manipulating the data and labels.
+
+### Supported Tasks
+
+- `depth-estimation`: Depth estimation is the task of approximating the perceived depth of a given image. In other words, it's about measuring the distance of each image pixel from the camera.