Metadata-Version: 2.4
Name: geoclip
Version: 1.2.1
Summary: CLIP-inspired image and GPS encoders for worldwide image geolocalization
Author-email: Vicente Vivanco <vicente.vivancocepeda@ucf.edu>
License-Expression: MIT
Project-URL: Homepage, https://github.com/VicenteVivan/geo-clip
Project-URL: Repository, https://github.com/VicenteVivan/geo-clip
Project-URL: Paper, https://arxiv.org/abs/2309.16020
Keywords: geolocation,computer-vision,clip,pytorch
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch<3,>=2.0
Requires-Dist: torchvision<1,>=0.15
Requires-Dist: Pillow<13,>=9.0
Requires-Dist: transformers<6,>=4.28
Requires-Dist: pandas<4,>=1.5
Requires-Dist: numpy<3,>=1.23
Requires-Dist: geopy<3,>=2.3
Requires-Dist: tqdm<5,>=4.65
Provides-Extra: demo
Requires-Dist: folium<1,>=0.14; extra == "demo"
Requires-Dist: matplotlib<4,>=3.7; extra == "demo"
Provides-Extra: test
Requires-Dist: pytest<10,>=8; extra == "test"
Dynamic: license-file

<div align="center">    
 
# 🌎 GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization

[![Paper](http://img.shields.io/badge/paper-arxiv.2309.16020-B31B1B.svg)](https://arxiv.org/abs/2309.16020v2)
[![Conference](https://img.shields.io/badge/NeurIPS-2023-blue)]()

![ALT TEXT](/figures/GeoCLIP.png)

</div>

### 📍 Try out our demo! [![Colab Demo](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1p3f5F3fIw9CD7H4RvfnHO9g-J45qUPHp?usp=sharing)

## Description

GeoCLIP addresses the challenges of worldwide image geo-localization by introducing a novel CLIP-inspired approach that aligns images with geographical locations, achieving state-of-the-art results on geo-localization and GPS to vector representation on benchmark datasets (Im2GPS3k, YFCC26k, GWS15k, and the Geo-Tagged NUS-Wide Dataset). Our location encoder models the Earth as a continuous function, learning semantically rich, CLIP-aligned features that are suitable for geo-localization. Additionally, our location encoder architecture generalizes, making it suitable for use as a pre-trained GPS encoder to aid geo-aware neural architectures.

![ALT TEXT](/figures/method.png)

## Method

Similarly to OpenAI's CLIP, GeoCLIP is trained contrastively by matching Image-GPS pairs. By using the MP-16 dataset, composed of 4.7M Images taken across the globe, GeoCLIP learns distinctive visual features associated with different locations on earth.

_🚧 Repo Under Construction 🔨_

## 📎 Getting Started: API

You can install GeoCLIP's module using pip:

```
pip install geoclip
```

or directly from source:

```
git clone https://github.com/VicenteVivan/geo-clip
cd geo-clip
python -m pip install .
```

## 🗺️📍 Worldwide Image Geolocalization

![ALT TEXT](/figures/inference.png)

### Usage: GeoCLIP Inference

```python
import torch
from geoclip import GeoCLIP

model = GeoCLIP()

image_path = "image.png"

top_pred_gps, top_pred_prob = model.predict(image_path, top_k=5)

print("Top 5 GPS Predictions")
print("=====================")
for i in range(5):
    lat, lon = top_pred_gps[i]
    print(f"Prediction {i+1}: ({lat:.6f}, {lon:.6f})")
    print(f"Probability: {top_pred_prob[i]:.6f}")
    print("")
```

## 🌐 Worldwide GPS Embeddings

In our paper, we show that once trained, our location encoder can assist other geo-aware neural architectures. Specifically, we explore our location encoder's ability to improve multi-class classification accuracy. We achieved state-of-the-art results on the Geo-Tagged NUS-Wide Dataset by concatenating GPS features from our pre-trained location encoder with an image's visual features. Additionally, we found that the GPS features learned by our location encoder, even without extra information, are effective for geo-aware image classification, achieving state-of-the-art performance in the GPS-only multi-class classification task on the same dataset.

![ALT TEXT](/figures/downstream-task.png)

### Usage: Pre-Trained Location Encoder

```python
import torch
from geoclip import LocationEncoder

gps_encoder = LocationEncoder()

gps_data = torch.Tensor([[40.7128, -74.0060], [34.0522, -118.2437]])  # NYC and LA in lat, lon
gps_embeddings = gps_encoder(gps_data)
print(gps_embeddings.shape) # (2, 512)
```

## Acknowledgments

This project incorporates code from Joshua M. Long's Random Fourier Features Pytorch. For the original source, visit [here](https://github.com/jmclong/random-fourier-features-pytorch).

## Citation

If you find GeoCLIP beneficial for your research, please consider citing us with the following BibTeX entry:

```
@inproceedings{geoclip,
  title={GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization},
  author={Vivanco, Vicente and Nayak, Gaurav Kumar and Shah, Mubarak},
  booktitle={Advances in Neural Information Processing Systems},
  year={2023}
}
```
