Yolov5

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5.0.9

- minor neptune logger fix https://github.com/fcakyon/yolov5-pip/commit/571bd4ed78df5d7b0596977bab4157ff890b636a

5.0.8

- update to 24.08.21 ultralytics/yolov5

- cli api changes:


<div align="center">Use from CLI</div>

You can call `yolov5 train`, `yolov5 detect`, `yolov5 val` and `yolov5 export` commands after installing the package via `pip`:

<summary>Training</summary>

Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).

bash
$ yolov5 train --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 40
yolov5l 24
yolov5x 16


<summary>Inference</summary>

yolov5 detect command runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.

bash
$ yolov5 detect --img 1280 --source 0 webcam
file.jpg image
file.mp4 video
path/ directory
path/*.jpg glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa rtsp stream
rtmp://192.168.1.105/live/test rtmp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream


To run inference on example images in `yolov5/data/images`:

5.0.7

- remove check_requirements (37)

5.0.6

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

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

console
pip install yolov5


</details>

<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">Use from Python</div>


<details open>
<summary>Basic</summary>

python
import yolov5

load model
model = yolov5.load('yolov5s')

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

perform inference
results = model(img)

inference with larger input size
results = model(img, size=1280)

inference with test time augmentation
results = model(img, augment=True)

parse results
predictions = results.pred[0]
boxes = predictions[:, :4] x1, x2, y1, 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>Alternative</summary>

python
from yolov5 import YOLOv5

set model params
model_path = "yolov5/weights/yolov5s.pt" it automatically downloads yolov5s model to given path
device = "cuda" 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, x2, y1, 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 open>
<summary>Train/Detect/Test/Export</summary>

- You can directly use these functions by importing them:

python
from yolov5 import train, test, detect, export

train.run(imgsz=640, data='coco128.yaml')
test.run(imgsz=640, data='coco128.yaml', weights='yolov5s.pt')
detect.run(imgsz=640)
export.run(imgsz=640, weights='yolov5s.pt')


- You can pass any argument as input:

python
from yolov5 import detect

img_url = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

detect.run(source=img_url, weights="yolov5s6.pt", conf_thres=0.25, imgsz=640)



</details>

<div align="center">Use from CLI</div>

You can call `yolo_train`, `yolo_detect`, `yolo_test` and `yolo_export` commands after installing the package via `pip`:

<details closed>
<summary>Training</summary>

Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).

bash
$ yolo_train --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 40
yolov5l 24
yolov5x 16


</details>

<details closed>
<summary>Inference</summary>

yolo_detect command runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.

bash
$ yolo_detect --source 0 webcam
file.jpg image
file.mp4 video
path/ directory
path/*.jpg glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa rtsp stream
rtmp://192.168.1.105/live/test rtmp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream


To run inference on example images in `yolov5/data/images`:

</details>

5.0.5

- Synchronized with [11.05.21 ultralytics/yolov5 repo](https://github.com/ultralytics/yolov5/tree/abfcf9eb79877971acd238cafe6149711c5056ad).

PLUS:

- neptune ai support:
`yolo_train --data coco.yaml --weights yolov5s.pt --neptune_token YOUR_TOKEN --neptune_project YOUR/PROJECT`

- mmdet style metric logging support
`yolo_train --data coco.yaml --weights yolov5s.pt --mmdet_tags`

5.0.3

- Update to [ultralytics/yolov5 24.04.21](https://github.com/ultralytics/yolov5/tree/4200674a136a5589972f352790f76d3f37e98dd6)

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