Add bibtex.

README.md

@@ -8,17 +8,17 @@ tags:
 ---
 
 ### Model and Inputs
-Prithvi is a first-of-its-kind temporal Vision transformer pre-trained by the IBM and NASA team on contiguous US Harmonised Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder learning strategy with an L1 loss function. The model includes spatial attention across multiple patches and also temporal attention for each patch. 
+Prithvi is a first-of-its-kind temporal Vision transformer pre-trained by the IBM and NASA team on contiguous US Harmonised Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder learning strategy with an MSE loss function. The model includes spatial attention across multiple patches and also temporal attention for each patch. 
 
 ![](GFM.png)
 
-The model expects remote sensing data in a video format (B, C, T, H, W). Note that the temporal dimension is very important here and not present in most 
-other works around remote sensing modeling. Being able to handle a time series of remote sensing images can benefit a variety of downstream tasks. The model can also handle static images, which can be simply fed into the model with T=1.
+The model accepts remote sensing data in a video format (B, C, T, H, W). Note that the temporal dimension (T) is very important in this application and not present in most 
+other works around remote sensing modeling. The ability to handle a time series of remote sensing images can benefit a variety of downstream tasks (e.g. Burn Scars segmentation, Flood Segmentation, Land Cover Classification). The model can also handle static imagery which can be fed into the model with T=1.
 
 ### Pre-training
-The model was pre-trained with NASA's HLS2 L30 product (30m granularity) from the contiguous United States. The bands that were used are the following: 
+The model was pre-trained with NASA's HLS V2 L30 product (30m granularity) from the contiguous United States. The following bands were used: 
 
-1. Blue
+1.  Blue
 2.  Green
 3.  Red
 4.  Narrow NIR
@@ -26,12 +26,12 @@ The model was pre-trained with NASA's HLS2 L30 product (30m granularity) from th
 6.  SWIR 2
 
 ### Code
-The model follows the [original mae repo](https://github.com/facebookresearch/mae) with some modifications including:
+The model follows the [original MAE repo](https://github.com/facebookresearch/mae) with the following modifications:
 
-1. replace 2D patch embed with 3D patch embed;
-2. replace 2D positional embed with 3D positional embed;
-3. replace 2D patchify and unpatchify with 3D.
-4. adding infrared bands besides RGB
+1. 2D patch embed replaced with 3D patch embed;
+2. 2D positional embed replaced with 3D positional embed;
+3. 2D patchify and unpatchify replaced with 3D.
+4. added infrared bands besides RGB.
 
 ### Inference and demo
 There is an inference script (`Prithvi_run_inference.py`) that allows to run the image reconstruction on a set of three HLS images (see example below). These images have to be geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR 1, SWIR 2) in reflectance units. There is also a **demo** that leverages the same code [here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-demo).
@@ -40,5 +40,20 @@ There is an inference script (`Prithvi_run_inference.py`) that allows to run the
 python Prithvi_run_inference.py --data_files t1.tif t2.tif t3.tif --yaml_file_path /path/to/yaml/Prithvi_100.yaml --checkpoint /path/to/checkpoint/Prithvi_100.pth --output_dir /path/to/out/dir/ --mask_ratio 0.5
 ```
 
-### Finetuning examples
+### Finetuning Examples
 Examples of finetuning the model for image segmentation using the mmsegmentation library are available through Hugging Face (e.g. [burn scars detection](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar) and [multi temporal crop classification](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification)), with the code used for the experiments available on [github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main/fine-tuning-examples). This also contains instructions to finetune the model for flood detection on the popular open access [sen1floods11 dataset](https://github.com/cloudtostreet/Sen1Floods11).
+
+## Citation
+
+If this model helped your research, please cite `Prithvi-100M` in your publications. Here is an example BibTeX entry:
+
+```
+@misc{Prithvi-100M,
+    author = {Jakubik, Johannes and Chu, Linsong and Fraccaro, Paolo and Bangalore, Ranjini and Lambhate, Devyani and Das, Kamal and Oliveira Borges, Dario and Kimura, Daiki and Simumba, Naomi and Szwarcman, Daniela and Muszynski, Michal and Weldemariam, Kommy and Zadrozny, Bianca and Ganti, Raghu and Costa, Carlos and Watson, Campbell and Mukkavilli, Karthik and Roy, Sujit and Phillips, Christopher and Ankur, Kumar and Ramasubramanian, Muthukumaran and Gurung, Iksha and Ji, Wei and Avery, Ryan and Ramachandran, Rahul and Maskey, Manil and Olofossen, Pontus and Fancher, Elizabeth and Lee, Tsengdar and Murphy, Kevin and Duffy, Dan and Little, Mike and Alemohammad, Hamed and Cecil, Michael and Li, Steve and Khallaghi, Sam and Godwin, Denys and Ahmadi, Maryam and Kordi, Fatemeh and Saux, Bertrand and Pastick, Neal and Doucette, Peter and Fleckenstein, Rylie and Luanga, Dalton and Corvin, Alex and Granger, Erwan},
+    doi    = {https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M},
+    month  = aug,
+    title  = {{Prithvi-100M}},
+    url    = {https://github.com/nasa-impact/Prithvi-100M},
+    year   = {2023}
+}
+```