Update README.md

README.md

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----
-license: cc-by-nc-nd-4.0
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+---
+license: cc-by-nc-nd-4.0
+task_categories:
+- object-detection
+- depth-estimation
+- image-classification
+language:
+- en
+size_categories:
+- 1K<n<10K
+---
+NITCAD (National Institute of Technology Calicut Autonomous Driving) is an effort to collect and label dataset to develop and contribute to autonomous driving efforts in India.
+
+India has some unique road scenarios such as
+- No lanes
+- New common objects on roads (like cows, autos, rickshaws)
+- No clear traffic signs for pedestrains, zebra crossings etc;
+
+So, that calls for specialized datasets to train and develop ML models for Indian roads. As a contribution, we have collected stereo and image datasets on Indian roads and labeled different classes that can be used to train Deep Learning models.
+
+To access the dataset, please fill out the Google Form - https://docs.google.com/forms/d/e/1FAIpQLScv2QXTYy3jwiAlqC9ro-lR_4UhUaWtBDNYQ4jWLZ3eUv3nSA/viewform?usp=sf_link
+
+For any questions regarding dataset/paper, feel free to email - [email protected], [email protected] with mail subject as “NITCAD Dataset — Doubts”
+
+ **Additional Links:** 
+ 1. Medium blog post explaining more details on dataset collection, statistics - https://namburisrinath.medium.com/nitcad-an-object-detection-classification-and-stereo-vision-dataset-for-autonomous-navigation-f28d3fe5b7d9
+ 2. Github repo - https://github.com/NamburiSrinath/NITCAD-dataset
+
+If this work helps in your research, please consider to cite as
+
+    @article{srinath2020nitcad,
+      title={NITCAD-Developing an object detection, classification and stereo vision dataset for autonomous navigation in Indian roads},
+      author={Srinath, Namburi GNVV Satya Sai and Joseph, Athul Zac and Umamaheswaran, S and Priyanka, Ch Lakshmi and Nair, Malavika and Sankaran, Praveen},
+      journal={Procedia Computer Science},
+      volume={171},
+      pages={207--216},
+      year={2020},
+      publisher={Elsevier}
+    }
+
+Also you can refer to [Speed estimation using Stereo Vision images](https://ieeexplore.ieee.org/abstract/document/9031876?casa_token=qeCiQNa9m50AAAAA:lOe4ogBfc866e3gPs2s6yesqeHqJ22WElxCQxdl_luLtbeTrgb_eluUFsmMrr8040A_S8U1Lof4y) for the speed estimation by using SIFT (Scale Invariant Feature Transform), YOLO and MC-CNN.
+
+If this work helps in your research, please consider to cite as
+
+    @inproceedings{umamaheswaran2019stereo,
+      title={Stereo Vision Based Speed Estimation for Autonomous Driving},
+      author={Umamaheswaran, S and Nair, Malavika and Joseph, Athul Zac and Srinath, Namburi GNVV Satya Sai and Priyanka, Ch Lakshmi and Sankaran, Praveen},
+      booktitle={2019 International Conference on Information Technology (ICIT)},
+      pages={201--205},
+      year={2019},
+      organization={IEEE}
+    }
+
+
+**Note:** This project was done in 2019. This dataset card is mainly created to improve visibility, searchability for the datasets via the Huggingface community.