Initializing README

README.md

@@ -1,3 +1,205 @@
 ---
 license: cc0-1.0
+task_categories:
+- video-classification
+tags:
+- zebra
+- giraffe
+- plains zebra
+- Grevy's zebra
+- video
+- animal behavior
+- behavior recognition
+- annotation
+- annotated video
+- conservation
+- drone
+- UAV
+- imbalanced
+- Kenya
+- Mpala Research Centre
+pretty_name: >-
+  KABR: High-Quality Dataset for Kenyan Animal Behavior Recognition from Drone
+  Videos
+size_categories:
+- 1M<n<10M
 ---
+# Dataset Card for KABR: High-Quality Dataset for Kenyan Animal Behavior Recognition from Drone Videos
+
+## Dataset Description
+
+- **Homepage:** https://dirtmaxim.github.io/kabr/
+- **Repository:** https://github.com/dirtmaxim/kabr-tools
+- **Paper:** [Coming Soon]
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+We present a novel high-quality dataset for animal behavior recognition from drone videos. 
+The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. 
+The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. 
+In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. 
+Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. 
+The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors.
+
+<!--This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).-->
+
+### Supported Tasks and Leaderboards
+
+[Include Benchmarks Here]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+The KABR dataset follows the Charades format:
+
+```
+KABR
+    /images
+        /video_1
+            /image_1.jpg
+            /image_2.jpg
+            ...
+            /image_n.jpg
+        /video_2
+            /image_1.jpg
+            /image_2.jpg
+            ...
+            /image_n.jpg
+        ...
+        /video_n
+            /image_1.jpg
+            /image_2.jpg
+            /image_3.jpg
+            ...
+            /image_n.jpg
+    /annotation
+        /classes.json
+        /train.csv
+        /val.csv
+```
+
+The dataset can be directly loaded and processed by the [SlowFast](https://github.com/facebookresearch/SlowFast) framework.
+
+**Informational Files**
+* `KABR/configs`: examples of SlowFast framework configs.
+* `KABR/annotation/distribution.xlsx`: distribution of classes for all videos.
+
+**Scripts:**
+* `image2video.py`: Encode image sequences into the original video.
+  * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `video/G0067.mp4`.
+* `image2visual.py`: Encode image sequences into the original video with corresponding annotations.
+  * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `visual/G0067.mp4`.
+
+### Data Instances
+
+[More Information Needed]
+**Naming:** Within the image folder, the `video_n` folders are named as follows (X indicates a number):
+* G0XXX.X - Giraffes
+* ZP0XXX.X - Plains Zebras
+* ZG0XXX.X - Grevy's Zebras
+* Within each of these folders the images are simply `X.jpg`.
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+Training and validation sets are indicated by their respecive CSV files, located within the `annotation` folder.
+
+## Dataset Creation
+
+### Curation Rationale
+
+We present a novel high-quality dataset for animal behavior recognition from drone videos. 
+The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. 
+The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. 
+In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. 
+Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. 
+The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors. 
+We believe that this dataset will be a valuable resource for researchers working on animal behavior recognition, as it provides a diverse and high-quality set of annotated videos that can be used for evaluating deep learning models. 
+Additionally, the dataset can be used to study the behavior patterns of Kenyan animals and can help to inform conservation efforts and wildlife management strategies. 
+
+[To be added:]
+We provide a detailed description of the dataset and its annotation process, along with some initial experiments on the dataset using conventional deep learning models. 
+The results demonstrate the effectiveness of the dataset for animal behavior recognition and highlight the potential for further research in this area.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+Data was collected from 6 January 2023 through 21 January 2023 at the [Mpala Research Centre](https://mpala.org/) in Kenya.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process.
+Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations.
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+<!---location for authors instead of under curators?--->
+* Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957
+* Jenna Kline (The Ohio State University)
+* Michelle Ramirez (The Ohio State University)
+* Sam Stevens (The Ohio State University)
+* Alec Sheets (The Ohio State University) - ORCID: 0000-0002-3737-1484
+* Reshma Ramesh Babu (The Ohio State University) - ORCID: 0000-0002-2517-5347
+* Namrata Banerji (The Ohio State University) - ORCID: 0000-0001-6813-0010
+* Elizabeth Campolongo (Imageomics Institute) - ORCID: 0000-0003-0846-2413
+* Nina Van Tiel (Eidgenössische Technische Hochschule Zürich) - ORCID: 0000-0001-6393-5629
+* Jackson Miliko (Mpala Research Centre)
+* Eduardo Bessa (Universidade de Brasília) - ORCID: 0000-0003-0606-5860
+* Tanya Berger-Wolf (The Ohio State University) - ORCID: 0000-0001-7610-1412
+* Daniel Rubenstein (Princeton University) - ORCID: 0000-0001-9049-5219
+* Charles Stewart (Rensselaer Polytechnic Institute)