Metadata-Version: 2.1
Name: ultralyticsplus
Version: 0.0.6
Summary: HuggingFace utilities for Ultralytics/YOLOv8.
Author: 
License: MIT
Keywords: machine-learning,deep-learning,ml,vision,yolov8,object-detection,huggingface,datasets
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: dev
License-File: LICENSE

# ultralytics+

Extra features for [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics).

## installation

```bash
pip install ultralyticsplus
```

## push to 🤗 hub

```bash
ultralyticsplus --exp_dir runs/detect/train --hf_model_id HF_USERNAME/MODELNAME
```

## load from 🤗 hub

```python
from ultralyticsplus import YOLO, render_predictions

# load model
model = YOLO('HF_USERNAME/MODELNAME')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
for result in model.predict(img, imgsz=640, return_outputs=True):
    print(result["det"]) # [[x1, y1, x2, y2, conf, class]]
    render = render_predictions(model, img=img, det=result["det"])
    render.show()
```
