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README.md

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 license: apache-2.0
+language:
+- en
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-
 # Satvision Pretraining Dataset - Small
 
-This dataset repository houses the pretraining data for the Satvision pretrained transformers.
+- **Developed by:** NASA GSFC CISTO Data Science Group
+- **Model type:** Pre-trained visual transformer model
+- **License:** Apache license 2.0
+
+This dataset repository houses the pretraining data for the Satvision pretrained transformers.
+This dataset was constructed using [webdatasets](https://github.com/webdataset/webdataset) to
+limit the number of inodes used in HPC systems with limited shared storage. Each file has 100000
+tiles, with pairs of image input and annotation. The data has been further compressed to ease
+the download from HuggingFace.
+
+SatelliteVision-Base (SatVis-B) is a pre-trained vision transformer based on the SwinV2 mode architecture.
+The model is pre-trained on global MODIS surface reflectance data from which 1.99 million image chips were used. SatVis-B is pre-trained using
+the masked-image-modeling (MIM) contrastive pre-training strategy. The MIM pre-training approach utilizes random
+masking of the input geospatial image chip, using a linear layer to regress the raw pixel values of the masked
+area with an l1 loss serving as the loss function.
+
+Resolution of the pre-training MODIS chips was `128x128` with a window size of `16x16`. SatViz-B was pre-trained
+for `800` epochs on 8x A100 GPUs and 12x V100 GPUs.
+
+
+### SatVision Transformer
+
+**Pre-trained models pre-trained on MODIS-Small dataset**
+
+| name | pre-train epochs | pre-train resolution | #params | pre-trained model |
+| :---: | :---: | :---: | :---: | :---: |
+| SatVision-Base | 800 | 128x128 | 84.5m | [checkpoint](https://huggingface.co/nasa-cisto-data-science-group/satvision-base/blob/main/ckpt_epoch_800.pth)/[config](https://github.com/nasa-nccs-hpda/pytorch-caney/blob/develop/examples/satvision/mim_pretrain_swinv2_satvision_base_192_window12_800ep.yaml) |
+
+## Getting Started with SatVision-Base
+
+- **Training repository:** https://github.com/nasa-nccs-hpda/pytorch-caney
+- **Pre-training dataset repository:** Coming soon!
+
+### Installation
+
+If you have singularity installed
+```bash
+$ git clone git@github.com:nasa-nccs-hpda/pytorch-caney.git
+$ singularity build --sandbox pytorch-caney.sif docker://nasanccs/pytorch-caney:latest
+# To shell into the container
+$ singularity shell --nv -B <mounts> pytorch-caney.sif
+```
+
+Anaconda installation
+```bash
+$ git clone git@github.com:nasa-nccs-hpda/pytorch-caney.git
+$ conda create -n satvision-env python==3.9
+```
+
+### Fine-tuning Satvision-Base
+- Create config file [example config](https://github.com/nasa-nccs-hpda/pytorch-caney/blob/finetuning/examples/satvision/finetune_satvision_base_landcover5class_192_window12_100ep.yaml)
+- Download checkpoint from this HF model repo
+- `$ git clone git@github.com:nasa-nccs-hpda/pytorch-caney.git`
+- Add a new pytorch dataset in pytorch-caney/pytorch_caney/data/datasets/
+- Add new pytorch dataset to dict in pytorch-caney/pytorch_caney/data/datamodules/finetune_datamodule.py
+```bash
+torchrun --nproc_per_node <NGPUS> pytorch-caney/pytorch_caney/pipelines/finetuning/finetune.py --cfg <config-file> --pretrained <path-to-pretrained> --dataset <dataset-name (key for new dataset)> --data-paths <path-to-data-dir> --batch-size <batch-size> --output <output-dir> --enable-amp
+```
+
+### Pre-training with pytorch-caney
+
+## Pre-training with SatVision-Base with Masked Image Modeling and pytorch-caney
+
+To pre-train the swinv2 base model with masked image modeling pre-training, run:
+```bash
+torchrun --nproc_per_node <NGPUS> pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg <config-file> --dataset <dataset-name> --data-paths <path-to-data-subfolder-1> --batch-size <batch-size> --output <output-dir> --enable-amp
+```
+
+For example to run on a compute node with 4 GPUs and a batch size of 128 on the MODIS SatVision pre-training dataset with a base swinv2 model, run:
+
+```bash
+singularity shell --nv -B <mounts> /path/to/container/pytorch-caney-container
+Singularity> export PYTHONPATH=$PWD:$PWD/pytorch-caney
+Singularity> torchrun --nproc_per_node 4 pytorch-caney/pytorch_caney/pipelines/pretraining/mim.py --cfg pytorch-caney/examples/satvision/mim_pretrain_swinv2_satvision_base_192_window12_800ep.yaml --dataset MODIS --data-paths /explore/nobackup/projects/ilab/data/satvision/pretraining/training_* --batch-size 128 --output . --enable-amp
+```
+
+## SatVision-Base Pre-Training Datasets
+
+| name | bands | resolution | #chips | meters-per-pixel |
+| :---: | :---: | :---: | :---: | :---: |
+| MODIS-Small | 7 | 128x128 | 1,994,131 | 500m |
+
+## Citing SatVision-Base
+
+If this model helped your research, please cite `satvision-base` in your publications.
+
+```
+@misc{satvision-base,
+	author       = {Carroll, Mark and Li, Jian and Spradlin, Caleb and Caraballo-Vega, Jordan},
+	doi          = {10.57967/hf/1017},
+    month        = aug,
+	title        = {{satvision-base}},
+	url          = {https://huggingface.co/nasa-cisto-data-science-group/satvision-base},
+    repository-code = {https://github.com/nasa-nccs-hpda/pytorch-caney}
+    year         = {2023}
+}
+```