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Malicious npm Packages Inject SSH Backdoors via Typosquatted Libraries
Socket’s threat research team has detected six malicious npm packages typosquatting popular libraries to insert SSH backdoors.
pip install yolov5
pip install "numpy>=1.18.5,<1.20" "matplotlib>=3.2.2,<4"
pip install yolov5
import yolov5
# load model
model = yolov5.load('yolov5s')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.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
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, 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/')
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/')
from yolov5 import train, val, detect, export
train.run(imgsz=640, data='coco128.yaml')
val.run(imgsz=640, data='coco128.yaml', weights='yolov5s.pt')
detect.run(imgsz=640)
export.run(imgsz=640, weights='yolov5s.pt')
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)
You can call yolov5 train
, yolov5 detect
, yolov5 val
and yolov5 export
commands after installing the package via pip
:
data.yaml
:$ yolov5 train --data data.yaml --weights yolov5s.pt --batch-size 16 --img 640
yolov5m.pt 8
yolov5l.pt 4
yolov5x.pt 2
# data.yml
train_json_path: "train.json"
train_image_dir: "train_image_dir/"
val_json_path: "val.json"
val_image_dir: "val_image_dir/"
$ yolov5 train --data data.yaml --weights yolov5s.pt
$ yolov5 train --data data.yaml --weights yolov5s.pt --neptune_project NAMESPACE/PROJECT_NAME --neptune_token YOUR_NEPTUNE_TOKEN
export AWS_ACCESS_KEY_ID=YOUR_KEY
export AWS_SECRET_ACCESS_KEY=YOUR_KEY
$ yolov5 train --data data.yaml --weights yolov5s.pt --s3_upload_dir YOUR_S3_FOLDER_DIRECTORY --upload_dataset
yolo_s3_data_dir
into data.yaml
to match Neptune dataset with a present dataset in S3.# data.yml
train_json_path: "train.json"
train_image_dir: "train_image_dir/"
val_json_path: "val.json"
val_image_dir: "val_image_dir/"
yolo_s3_data_dir: s3://bucket_name/data_dir/
yolov5 detect command runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect
.
$ yolov5 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
You can export your fine-tuned YOLOv5 weights to any format such as torchscript
, onnx
, coreml
, pb
, tflite
, tfjs
:
$ yolov5 export --weights yolov5s.pt --include 'torchscript,onnx,coreml,pb,tfjs'
FAQs
Packaged version of the Yolov5 object detector
We found that yolov5-thin demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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