Add short description and remove redundancy in acknowledgements

README.md

@@ -17,6 +17,7 @@ tags:
 - UAV
 - drone
 - video
+model_description: "Behavior recognition model for in situ drone videos of zebras and giraffes, built using X3D model initialized on Kinetics weights. It is trained on the KABR dataset, which is comprised of 10 hours of aerial video footage of reticulated giraffes (Giraffa reticulata), Plains zebras (Equus quagga), and Grevy’s zebras (Equus grevyi) captured using a DJI Mavic 2S drone. It includes both spatiotemporal (i.e., mini-scenes) and behavior annotations provided by an expert behavioral ecologist."
 ---
 
 # Model Card for X3D-KABR-Kinetics
@@ -33,15 +34,15 @@ behavioral ecologist.
 
 ### Model Description
 
-- **Developed by:** [Maksim Kholiavchenko, Maksim Kukushkin, Otto Brookes, Jenna Kline, Sam Stevens, Isla Duporge, Alec Sheets,
+- **Developed by:** Maksim Kholiavchenko, Maksim Kukushkin, Otto Brookes, Jenna Kline, Sam Stevens, Isla Duporge, Alec Sheets,
 Reshma R. Babu, Namrata Banerji, Elizabeth Campolongo,
 Matthew Thompson, Nina Van Tiel, Jackson Miliko,
 Eduardo Bessa Mirmehdi, Thomas Schmid,
-Tanya Berger-Wolf, Daniel I. Rubenstein, Tilo Burghardt, Charles V. Stewart]
+Tanya Berger-Wolf, Daniel I. Rubenstein, Tilo Burghardt, Charles V. Stewart
 
-- **Model type:** [X3D]
-- **License:** [MIT]
-- **Fine-tuned from model:** [X3D-S, Kinetics]
+- **Model type:** X3D-L
+- **License:** MIT
+- **Fine-tuned from model:** [X3D-L, Kinetics](https://github.com/facebookresearch/SlowFast/blob/main/configs/Kinetics/X3D_L.yaml)
 
 This model was developed for the benefit of the community as an open-source product, thus we request that any derivative products are also open-source.
 
@@ -88,7 +89,7 @@ for more information on how this model can be used generate time-budgets from ae
 
 ### Training Data
 
-[KABR Dataset](https://huggingface.co/datasets/imageomics/KABR)
+This model was trained on the [KABR mini-scene dataset](https://huggingface.co/datasets/imageomics/KABR).
 
 ### Training Procedure 
 
@@ -102,7 +103,7 @@ For each tracklet, we create a separate video, called a mini-scene, by extractin
 detection in a video frame. 
 This allows us to compensate for the drone's movement and provides a stable, zoomed-in representation of the animal.
 
-See [project page](https://kabrdata.xyz/) and the [paper](https://openaccess.thecvf.com/content/WACV2024W/CV4Smalls/papers/Kholiavchenko_KABR_In-Situ_Dataset_for_Kenyan_Animal_Behavior_Recognition_From_Drone_WACVW_2024_paper.pdf) for data preprocessing details.
+See the [KBAR mini-scene project page](https://kabrdata.xyz/) and the [paper](https://openaccess.thecvf.com/content/WACV2024W/CV4Smalls/papers/Kholiavchenko_KABR_In-Situ_Dataset_for_Kenyan_Animal_Behavior_Recognition_From_Drone_WACVW_2024_paper.pdf) for data preprocessing details.
 
 We applied data augmentation techniques during training, including horizontal flipping to randomly
 mirror the input frames horizontally and color augmentations to randomly modify the
@@ -114,104 +115,38 @@ The model was trained for 120 epochs, using a batch size of 5.
 We used the EQL loss function to address the long-tailed class distribution and SGD optimizer with a learning rate of 1e5.
 We used a sample rate of 16x5, and random weight initialization.
 
-<!-- ADD RESULTS ONCE NEW PAPER PUBLISHED
-
-<!-- 
-#### Speeds, Sizes, Times 
-
-<!-- [optional] This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-<!-- 
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-<!-- 
-[More Information Needed]
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible, otherwise link to the original source with more info.
-Provide a basic overview of the test data and documentation related to any data pre-processing or additional filtering. -->
-<!-- 
-[More Information Needed]
+The dataset was evaluated on the X3D-L model utilizing the [SlowFast](https://github.com/facebookresearch/SlowFast) framework, specifically utilizing teh [test_net script](https://github.com/facebookresearch/SlowFast/blob/main/tools/test_net.py).
 
-#### Factors
+### Testing Data
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-<!-- 
-[More Information Needed]
+We provide a train-test split of the mini-scenes from the [KABR Dataset](https://huggingface.co/datasets/imageomics/KABR) for evaluation purposes (test set indicated in [annotations/val.csv](https://huggingface.co/datasets/imageomics/KABR/blob/main/KABR/annotation/val.csv), with 75% for train and 25% for testing. No mini-scene was divided by the split. The splits ensured a stratified representation of giraffes, Plains zebras, and Grevy’s zebras.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-<!-- 
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-[More Information Needed]
+We report precision, recall, and F1 score on the KABR mini-scene test set, along with the mean Average Precision (mAP) for overall, head-class, and tail-class performance.
 
-## Model Examination
+**Results**
 
-<!-- [optional] Relevant interpretability work for the model goes here -->
-<!-- 
-[More Information Needed]
+| WI       | BS | mAP Overall | mAP Head | mAP Tail | P      | R      | F1     |
+|----------|----|-------------|----------|----------|--------|--------|--------|
+| K-400    | 64 | **66.36**   | **96.96**| **56.16**| 66.44  | 63.65  | 64.70  |
 
-## Environmental Impact
-
-<!-- 
-It would be great to try to include this.
-
-Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-<!-- 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) 
-presented in [Lacoste et al. (2019)](https://doi.org/10.48550/arXiv.1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications 
-[More Information Needed--optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
+Please see the [Base Model Description](https://arxiv.org/pdf/2004.04730).
 
 #### Hardware
 
-[More Information Needed: hardware requirements]
-
-#### Software
-
-[More Information Needed]
+Running the X3D model requires a modern NVIDIA GPU with CUDA support. X3D-L is designed to be computationally efficient, and requires 10–16 GB of GPU memory during training.
 
 ## Citation
 
-<!-- If there is a paper introducing the model, the Bibtex information for that should go in this section. 
-
-See notes at top of file about selecting a license. 
-If you choose CC0: This model is dedicated to the public domain for the benefit of scientific pursuits. 
-We ask that you cite the model and journal paper using the below citations if you make use of it in your research.
-
--->
-
 **BibTeX:**
 
-
 If you use our model in your work, please cite the model and associated paper.
 
 **Model**
@@ -257,13 +192,9 @@ Tanya Berger-Wolf, Daniel I. Rubenstein, Tilo Burghardt, Charles V. Stewart},
 
 ## Acknowledgements
 
-This work was supported by the [Imageomics Institute](https://imageomics.org), 
-which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under 
-[Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) 
-(Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). 
-Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) 
-and do not necessarily reflect the views of the National Science Foundation.
+This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Additional support was also provided by the [AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE)](https://icicle.osu.edu/), which is funded by the US National Science Foundation under [Award #2112606](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112606). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 
+The data was gathered at the [Mpala Research Centre](https://mpala.org/) in Kenya, in accordance with Research License No. NACOSTI/P/22/18214. The data collection protocol adhered strictly to the guidelines set forth by the Institutional Animal Care and Use Committee under permission No. IACUC 1835F.
 
 
 ## Model Card Authors
@@ -272,11 +203,4 @@ Jenna Kline and Maksim Kholiavchenko
 
 ## Model Card Contact
 
-Maksim Kholiavchenko 
-<!-- Could include who to contact with questions, but this is also what the "Discussions" tab is for. -->
-
-### Contributions
-
-This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Additional support was also provided by the [AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE)](https://icicle.osu.edu/), which is funded by the US National Science Foundation under [Award #2112606](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112606). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
-
-The data was gathered at the [Mpala Research Centre](https://mpala.org/) in Kenya, in accordance with Research License No. NACOSTI/P/22/18214. The data collection protocol adhered strictly to the guidelines set forth by the Institutional Animal Care and Use Committee under permission No. IACUC 1835F.
+For questions on this model, please open a [discussion](https://huggingface.co/imageomics/x3d-kabr-kinetics/discussions) on this repo.