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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
🚀 Lightning-fast computer vision models. Fine-tune SOTA models with just a few lines of code. Ready for cloud ☁️ and edge 📱 deployment.
| Date | Stars |
|---|---|
| 2026-07-24 | 352 |
| 2026-07-25 | 352 |
| 2026-07-28 | 352 |
| 2026-07-30 | 352 |
| 2026-07-31 | 352 |
| 2026-08-06 | 352 |
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<a href="https://www.focoos.ai" target="_blank">
<img src="https://public.focoos.ai/library/focoos_banner.png" alt="FocoosAI" style="max-width:100%;">
</a>
[](https://colab.research.google.com/github/FocoosAI/focoos/blob/main/tutorials/training.ipynb)
[](https://focoosai.github.io/focoos/)
# Welcome to Focoos AI
Focoos AI provides an advanced development platform designed to empower developers and businesses with efficient, customizable computer vision solutions. Whether you're working with data from cloud infrastructures or deploying on edge devices, Focoos AI enables you to select, fine-tune, and deploy state-of-the-art models optimized for your unique needs.
## Overview
<!-- Unlock the full potential of Focoos AI with the Focoos library! 🚀 -->
The Focoos library is your gateway to easily access cutting-edge computer vision models and development tools. With just a few lines of code, you can **fine tune** pre-trained models tailored to your specific needs.
Whether you're working in the cloud or on edge devices, the Focoos library seamlessly integrates into your workflow, accelerating development and simplifying the implementation of computer vision solutions.
### Key Features 🔑
1. **Frugal Pretrained Models** 🌿
Get started quickly by selecting one of our efficient, [pre-trained models](https://focoosai.github.io/focoos/models/) that best suits your data and application needs.
Focoos Model Registry give access to 11 pretrained models of different size from different families: RTDetr, Maskformer, BisenetFormer
2. **Fine Tune Your Model** ✨ Adapt the model to your specific use case by customize its config and training it on your own dataset.
4. **Optimized Inference** 🖥️ Export Models and run inference efficiently, Leverage hardware acceleration through Torchscript, TensorRT and ONNX for maximum performance.
5. **FocoosHub Integration** 🔄 Seamlessly integrate with Focoos Cloud to access your models and data, you can also run cloud inference on managed models.
# Quickstart 🚀
Ready to dive in? Get started with the setup in just a few simple steps!
## Installation
**Install** the Focoos library (for more options, see [setup](https://focoosai.github.io/focoos/setup))
```bash linenums="0"
uv pip install 'focoos @ git+https://github.com/FocoosAI/focoos.git'
```
## Inference
```python
from focoos import ModelManager
im = "https://public.focoos.ai/samples/motogp.jpg" # can be local/remote path, np.array, PIL image
model = ModelManager.get("fai-detr-l-obj365") # any models from ModelRegistry, FocoosHub or local folder
detections = model.infer(im,annotate=True)
```
## Training
```python
from focoos import ModelManager,DatasetSplitType, DatasetLayout, RuntimeType, TrainerArgs, Task
from focoos.data import get_default_by_task
from focoos.data import AutoDataset
ds_name = "my_dataset.zip"
task = Task.DETECTION
layout = DatasetLayout.ROBOFLOW_COCO
auto_dataset = AutoDataset(dataset_name=ds_name, task=task, layout=layout)
train_augs, val_augs = get_default_by_task(task, 640, advanced=False)
train_dataset = auto_dataset.get_split(augs=train_augs, split=DatasetSplitType.TRAIN)
valid_dataset = auto_dataset.get_split(augs=val_augs, split=DatasetSplitType.VAL)
model = ModelManager.get("fai-detr-l-obj365")
args = TrainerArgs(
run_name=f"{model.name}_{train_dataset.name}",
batch_size=16,
max_iters=50,
eval_period=50,
learning_rate=0.0008,
sync_to_hub=False, # use this to sync model info, weights and metrics on the hub
)
model.train(args, train_dataset, valid_dataset)
# if you want to do only evaluation
model.eval(args, valid_dataset)
```
## Export, Benchmark and optimized Inference
```python
from focoos import RuntimeType, ModelManager
model = MoExcerpt of 5,364 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7b735f1af9d921e0, topic:computer-vision, topic:object-detection, topic:image-segmentation