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update readme #198

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Dec 31, 2022
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update readme #198

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fcakyon Dec 31, 2022
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fcakyon Dec 31, 2022
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68 changes: 8 additions & 60 deletions README.md
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Expand Up @@ -27,7 +27,7 @@ You can finally install <a href="https://github.com/ultralytics/yolov5">YOLOv5 o
<br>
This yolov5 package contains everything from ultralytics/yolov5 <a href="https://github.com/ultralytics/yolov5/tree/357cde9ee7da13ba3095995488c5a23631467f1a">at this commit</a> plus:
<br>
1. Easy installation via pip: `pip install yolov5`
1. Easy installation via pip: <b>pip install yolov5</b>
<br>
2. Full CLI integration with <a href="https://github.com/google/python-fire">fire</a> package
<br>
Expand All @@ -45,30 +45,24 @@ This yolov5 package contains everything from ultralytics/yolov5 <a href="https:/

## <div align="center">Install</div>

<details open>
<summary>Install yolov5 using pip (for Python >=3.7)</summary>
Install yolov5 using pip (for Python >=3.7)

```console
pip install yolov5
```

</details>
## <div align="center">Model Zoo</div>

<details closed>
<summary>Install yolov5 using pip `(for Python 3.6)`</summary>

```console
pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"
pip install yolov5
```

</details>
<div align="center">

## <div align="center">Use from Python</div>
Effortlessly explore and use finetuned YOLOv5 models with one line of code: <a href="https://github.com/keremberke/awesome-yolov5-models">awesome-yolov5-models</a>

<a href="https://github.com/keremberke/awesome-yolov5-models"><img src="https://user-images.githubusercontent.com/34196005/210134158-108b24f4-2b8e-43ea-95c8-44731625cde2.gif" width="640"></a>
</div>

<details open>
<summary>Basic</summary>
## <div align="center">Use from Python</div>

```python
import yolov5
Expand Down Expand Up @@ -112,52 +106,6 @@ results.save(save_dir='results/')

```

</details>

<details closed>
<summary>Alternative</summary>

```python
from yolov5 import YOLOv5

# set model params
model_path = "yolov5/weights/yolov5s.pt"
device = "cuda:0" # or "cpu"

# init yolov5 model
yolov5 = YOLOv5(model_path, device)

# load images
image1 = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
image2 = 'https://github.com/ultralytics/yolov5/blob/master/data/images/bus.jpg'

# perform inference
results = yolov5.predict(image1)

# perform inference with larger input size
results = yolov5.predict(image1, size=1280)

# perform inference with test time augmentation
results = yolov5.predict(image1, augment=True)

# perform inference on multiple images
results = yolov5.predict([image1, image2], size=1280, augment=True)

# 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()

# save results into "results/" folder
results.save(save_dir='results/')
```

</details>

<details closed>
<summary>Train/Detect/Test/Export</summary>

Expand Down