semvec/pypi-packages huggingface dataset:
simAIRR 0.1
A tool for simulation of antigen-experienced adaptive immune receptor repertoire (AIRR) datasets for benchmarking of machine learning (ML) methods.
License: MIT

# simAIRR

![unit_tests](https://github.com/KanduriC/simAIRR/actions/workflows/run_unit_tests.yml/badge.svg)
![docker](https://github.com/KanduriC/simAIRR/actions/workflows/push_docker.yml/badge.svg)

simAIRR provides a simulation approach to generate synthetic AIRR datasets that are suitable for benchmarking machine learning (ML) methods, where undesirable access to ground truth signals in training datasets for ML methods is mitigated. Unlike state-of-the-art approaches, simAIRR constructs antigen-experienced-like baseline repertoires and introduces signals by following the empirical relationship between generation probability and sharing pattern of public sequences calibrated from real-world experimental datasets.

For installation instructions and user guide, see documentation: https://kanduric.github.io/simAIRR/
yolov8tohf 0.0.2
Huggingface utilities for Ultralytics/YOLOv8.
License: MIT
Requires Python: >=3.7
Keywords: machine-learning,deep-learning,ml,vision,yolov8,object-detection,huggingface,datasets

# YOLOv8 to 🤗
HuggingFace utilities for Ultralytics/YOLOv8

## installation

```bash
pip install yolov8tohf
```

## push to hub

```bash
yolov8tohf --exp_dir runs/detect/train --hf_model_id HF_USERNAME/MODELNAME
```

## load from hub

```python
from yolov8tohf import YOLO

# load model
model = YOLO('fcakyon/yolov8s-test')

# 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
model.predict(img, imgsz=640)
```