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https://github.com/ServiceNow/seasonal-contrast
seasonal-contrast is a ServiceNow Research project that was started at Element AI.
https://github.com/ServiceNow/seasonal-contrast
Last synced: 17 days ago
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seasonal-contrast is a ServiceNow Research project that was started at Element AI.
- Host: GitHub
- URL: https://github.com/ServiceNow/seasonal-contrast
- Owner: ServiceNow
- License: apache-2.0
- Created: 2020-11-05T14:31:52.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-07-05T19:27:20.000Z (over 1 year ago)
- Last Synced: 2024-04-14T12:12:57.310Z (7 months ago)
- Language: Python
- Homepage:
- Size: 2.09 MB
- Stars: 149
- Watchers: 10
- Forks: 26
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
*ServiceNow completed its acquisition of Element AI on January 8, 2021. All references to Element AI in the materials that are part of this project should refer to ServiceNow.*
## Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
This is the official PyTorch implementation of the [SeCo paper](https://arxiv.org/abs/2103.16607):
```
@article{manas2021seasonal,
title={Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data},
author={Ma{\~n}as, Oscar and Lacoste, Alexandre and Giro-i-Nieto, Xavier and Vazquez, David and Rodriguez, Pau},
journal={arXiv preprint arXiv:2103.16607},
year={2021}
}
```
### Preparation
Install Python dependencies by running:
```shell
pip install -r requirements.txt
```
### Data Collection
First, obtain Earth Engine authentication credentials by following the [installation instructions](https://developers.google.com/earth-engine/guides/python_install).
Then, to collect and download a new SeCo dataset from a random set of Earth locations, run:
```shell
python datasets/seco_downloader.py \
--save_path [folder where data will be downloaded] \
--num_locations 200000
```
### Unsupervised Pre-training
To do unsupervised pre-training of a ResNet-18 model on the SeCo dataset, run:
```shell
python main_pretrain.py \
--data_dir datasets/seco_1m --data_mode seco \
--base_encoder resnet18
```
### Transferring to Downstream Tasks
With a pre-trained SeCo model, to train a supervised linear classifier on 10% of the [BigEarthNet](http://bigearth.net/) training set in a 4-GPU machine, run:
```shell
python main_bigearthnet.py \
--gpus 4 --accelerator dp --batch_size 1024 \
--data_dir datasets/bigearthnet --train_frac 0.1 \
--backbone_type pretrain --ckpt_path checkpoints/seco_resnet18_1m.ckpt \
--freeze_backbone --learning_rate 1e-3
```
To train a supervised linear classifier on [EuroSAT](https://github.com/phelber/eurosat) from a pre-trained SeCo model, run:
```shell
python main_eurosat.py \
--data_dir datasets/eurosat \
--backbone_type pretrain --ckpt_path checkpoints/seco_resnet18_1m.ckpt
```
To train a supervised change detection model on [OSCD](https://rcdaudt.github.io/oscd/) from a pre-trained SeCo model, run:
```shell
python main_oscd.py \
--data_dir datasets/oscd \
--backbone_type pretrain --ckpt_path checkpoints/seco_resnet18_1m.ckpt
```
### Datasets
Our collected SeCo datasets can be downloaded as following:
| #images | RGB preview | size | link | md5 |
| ------- | :---------: | ------- | ---- | --- |
| 100K | ✓ | 7.3 GB | [download](https://zenodo.org/record/4728033/files/seco_100k.zip?download=1) | `ebf2d5e03adc6e657f9a69a20ad863e0` |
| ~1M | | 36.3 GB | [download](https://zenodo.org/record/4728033/files/seco_1m.zip?download=1) | `187963d852d4d3ce6637743ec3a4bd9e` |
### Pre-trained Models
Our pre-trained SeCo models can be downloaded as following:
| dataset | architecture | link | md5 |
| --------- | ------------ | ---- | --- |
| SeCo-100K | ResNet-18 | [download](https://zenodo.org/record/4728033/files/seco_resnet18_100k.ckpt?download=1) | `dcf336be31f6c6b0e77dcb6cc958fca8` |
| SeCo-1M | ResNet-18 | [download](https://zenodo.org/record/4728033/files/seco_resnet18_1m.ckpt?download=1) | `53d5c41d0f479bdfd31d6746ad4126db` |
| SeCo-100K | ResNet-50 | [download](https://zenodo.org/record/4728033/files/seco_resnet50_100k.ckpt?download=1) | `9672c303f6334ef816494c13b9d05753` |
| SeCo-1M | ResNet-50 | [download](https://zenodo.org/record/4728033/files/seco_resnet50_1m.ckpt?download=1) | `7b09c54aed33c0c988b425c54f4ef948` |