Metadata-Version: 2.4
Name: fomo-edge-ai
Version: 0.0.10
Summary: FOMO - Lightweight Point Localization models.
Author-email: Bence Danko <bencejdanko@gmail.com>
License: Apache-2.0
Project-URL: Homepage, https://github.com/fomo-edge-ai/fomo
Project-URL: Repository, https://github.com/fomo-edge-ai/fomo
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.19.0
Requires-Dist: Pillow>=8.0.0
Requires-Dist: torch>=1.13.0
Requires-Dist: torchvision>=0.11.0
Requires-Dist: PyYAML>=6.0
Requires-Dist: requests>=2.25.0
Requires-Dist: opencv-python>=4.11.0.86
Requires-Dist: scipy>=1.7.0
Requires-Dist: safetensors>=0.4.0
Requires-Dist: tqdm>=4.64.0
Requires-Dist: litert-torch==0.9.1
Requires-Dist: ai-edge-quantizer-nightly==0.8.0.dev20260602
Dynamic: license-file

# FOMO: Fast Object Localization

FOMO is a lightweight point localization model designed for edge AI applications. Instead of regressing bounding boxes, FOMO downsamples the input image (for example, mapping a 192x192 input to a 24x24 grid) and predicts class probabilities and coordinates on a per-cell basis.

## Installation

Install the package via PyPI:

```bash
pip install fomo-edge-ai
```

## Model Hosting

Models are currently available on Hugging Face: 

https://huggingface.co/fomo-edge-ai/FOMO

## Examples

Refer to `examples/` for detailed examples on training and inference.

## License

Code is licensed under the Apache License 2.0. Pre-trained weights are hosted externally and may inherit separate licensing terms. Check details in the specific weight repositories.
