Metadata-Version: 2.4
Name: yolov9-trunk
Version: 0.0.2.3
Summary: yolov9-trunk
Home-page: https://github.com/Trunk-Tools/yolov9
Author: cvan-tt
Author-email: collin.van@trunk.tools
License: Apache License 2.0
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: gitpython
Requires-Dist: ipython
Requires-Dist: matplotlib>=3.2.2
Requires-Dist: numpy>=1.18.5
Requires-Dist: opencv-python>=4.1.1
Requires-Dist: packaging>=20.0
Requires-Dist: Pillow>=7.1.2
Requires-Dist: psutil
Requires-Dist: PyYAML>=5.3.1
Requires-Dist: requests>=2.23.0
Requires-Dist: scipy>=1.4.1
Requires-Dist: thop>=0.1.1
Requires-Dist: torch>=1.7.0
Requires-Dist: torchvision>=0.8.1
Requires-Dist: tqdm>=4.64.0
Requires-Dist: click>=8.3.0
Requires-Dist: tensorboard>=2.4.1
Requires-Dist: mlflow>=2.0.0
Requires-Dist: boto3>=1.26.0
Requires-Dist: pandas>=1.1.4
Requires-Dist: seaborn>=0.11.0
Requires-Dist: albumentations>=1.0.3
Requires-Dist: pycocotools>=2.0
Dynamic: author
Dynamic: author-email
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
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## 🤗 Citation

This code was forked from [yolov9pip](https://github.com/kadirnar/yolov9-pip).

## Description

This code is lightly updated to allow use of Pytorch >2.6 as an imported library.

This repo is a packaged version of the [Yolov9](https://github.com/WongKinYiu/yolov9) model.

### ⭐ Installation

```
pip install yolov9pip
```

### 🌠 Yolov9 Inference

```python
import yolov9

# load pretrained or custom model
model = yolov9.load(
    "yolov9-c.pt",
    device="cpu",
)

# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.classes = None  # (optional list) filter by class

# set image
imgs = "data/zidane.jpg"

# perform inference
results = model(imgs)

# inference with larger input size and test time augmentation
results = model(img, size=640)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4]  # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()
```

## 😍 Contributing

```bash
pip install -r dev-requirements.txt
pre-commit install
pre-commit run --all-files
```
