Readme

README.md

@@ -1 +1,70 @@
-# Meta-MAE
+# BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
+
+## 🦢 Dataset Released: Cross-View iNAT Birds 2021
+This cross-view birds species dataset consists
+of paired ground-level bird images and satellite images, along with meta-information associated with the iNaturalist-2021 dataset.
+
+![CiNAT-Birds-2021](imgs/data5%20(2).png)
+
+#### Computer Vision Tasks
+1. Fine-Grained image classification
+2. Satellite-to-bird image retrieval
+3. Bird-to-satellite image retrieval
+4. Geolocalization of Bird Images
+
+An example of task 3 is shown below:
+
+![Retrieval](imgs/ret_ex.png)
+
+## 👨‍💻 Getting Started 
+
+#### Installing Required Packages
+There are two options to setup your environment to be able to run all the functions in the repository:
+1. Using Dockerfile provided in the repository to create a docker image with all required packages:
+    ```bash
+    docker build -t <your-docker-hub-id>/birdsat .
+    ```
+2. Creating conda Environment with all required packages:
+    ```bash
+    conda create -n birdsat python=3.10 && \
+    conda activate birdsat && \
+    pip install requirements.txt
+    ```
+
+Additionally, we have hosted a pre-built docker image on docker hub with tag `srikumar26/birdsat:latest` for use.
+
+#### Data Preparation
+Please refer to `./data/README.md` on instructions for downloading and preparing data.
+
+## 🔥 Training Models
+1. Setup all the parameters of interest inside `config.py` before launching the training script.
+2. Run pre-training by calling:
+    ```bash
+    python pretrain.py
+    ```
+3. Run fine-tuning by calling:
+    ```bash
+    python finetune.py
+    ```
+
+## ❄️ Pretrained Models
+Download pretrained models from the given links below:
+
+|Model Type|Download Url|
+|----------|--------|
+|CVE-MAE|[Link]()|
+|CVE-MAE-Meta| [Link]()|
+|CVM-MAE| [Link]()|
+|CVM-MAE-Meta| [Link]()|
+
+
+## 📑 Citation
+
+```bibtex
+@inproceedings{sastry2024birdsat,
+  title={BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping},
+  author={Srikumar, Sastry and Subash, Khanal and Huang, Di and Aayush, Dhakal and Nathan, Jacobs},
+  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
+  year={2024}
+}
+```