Package Health Score

49 / 100

security
No known security issues
popularity
Limited
maintenance
Inactive
community
Sustainable

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Popularity

Limited

GitHub Stars: 290
Forks: 70
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package musong, we found that it has been starred 290 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Jan 23, 2023
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 1
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

GPL-3.0

Non-Permissive License

We noticed that this project uses a license which requires less permissive conditions such as disclosing the source code, stating changes or redistributing the source under the same license. It is advised to further consult the license terms before use.

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 0
Open PR: 2
Last Release: 2 years ago
Last Commit: 3 months ago

Further analysis of the maintenance status of musong based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for musong is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 20
Funding: Yes

With more than 10 contributors for the musong repository, this is possibly a sign for a growing and inviting community.

We found a way for you to contribute to the project! Looks like musong is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.7

Age: 2 years
Latest Release: 2 years ago
Dependencies: N/A
Versions: 3
Maintainers: 1
Wheels: OS Independent

Readme

packaged ultralytics/yolov5

pip install yolov5

Overview

You can finally install YOLOv5 object detector using pip and integrate into your project easily.

This yolov5 package contains everything from ultralytics/yolov5 at this commit plus:
1. Easy installation via pip: pip install yolov5
2. Full CLI integration with fire package
3. COCO dataset format support (for training)
4. Full 🤗 Hub integration
5. S3 support (model and dataset upload)
6. NeptuneAI logger support (metric, model and dataset logging)
7. Classwise AP logging during experiments

Install

Install yolov5 using pip (for Python >=3.7)

pip install yolov5

Model Zoo

Effortlessly explore and use finetuned YOLOv5 models with one line of code: awesome-yolov5-models

Use from Python

import yolov5

# load pretrained model
model = yolov5.load('yolov5s.pt')

# or load custom model
model = yolov5.load('train/best.pt')
  
# 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/')

Train/Detect/Test/Export

You can directly use these functions by importing them:

from yolov5 import train, val, detect, export
# from yolov5.classify import train, val, predict
# from yolov5.segment import train, val, predict

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')

You can pass any argument as input:

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)

Use from CLI

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

Training

Finetune one of the pretrained YOLOv5 models using your custom data.yaml:

$ yolov5 train --data data.yaml --weights yolov5s.pt --batch-size 16 --img 640
                                          yolov5m.pt              8
                                          yolov5l.pt              4
                                          yolov5x.pt              2

Start a training using a COCO formatted dataset:

# 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

Visualize your experiments via Neptune.AI (neptune-client>=0.10.10 required):

$ yolov5 train --data data.yaml --weights yolov5s.pt --neptune_project NAMESPACE/PROJECT_NAME --neptune_token YOUR_NEPTUNE_TOKEN

Automatically upload weights to Huggingface Hub:

$ yolov5 train --data data.yaml --weights yolov5s.pt --hf_model_id username/modelname --hf_token YOUR-HF-WRITE-TOKEN

Automatically upload weights and datasets to AWS S3 (with Neptune.AI artifact tracking integration):

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

Add 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/

Inference

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

Export

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

Classify

Train/Val/Predict with YOLOv5 image classifier:

$ yolov5 classify train --img 640 --data mnist2560 --weights yolov5s-cls.pt --epochs 1

$ yolov5 classify predict --img 640 --weights yolov5s-cls.pt --source images/

Segment

Train/Val/Predict with YOLOv5 instance segmentation model:

$ yolov5 segment train --img 640 --weights yolov5s-seg.pt --epochs 1

$ yolov5 segment predict --img 640 --weights yolov5s-seg.pt --source images/

FAQs

What is musong?

Packaged version of the Yolov5 object detector. Visit Snyk Advisor to see a full health score report for musong, including popularity, security, maintenance & community analysis.

Is musong popular?

The python package musong receives a total of 45 weekly downloads. As such, musong popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is musong well maintained?

We found indications that musong is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is musong safe to use?

The python package musong was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 1 November-2024, at 07:06 (UTC).

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musong

Packaged version of the Yolov5 object detector For more information about how to use this package see README

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Package Health Score

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Popularity

Direct Usage Popularity

Security

Security and license risk for latest version

Non-Permissive License

Maintenance

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Community

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Package

Readme

packaged ultralytics/yolov5

pip install yolov5

Overview

Install

Model Zoo

Use from Python

Use from CLI

FAQs

What is musong?

Is musong popular?

Is musong well maintained?

Is musong safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Explore Similar Packages

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

Embed Package Health Score Badge

Keep your project healthy

Check your requirements.txt

Snyk Vulnerability Scanner

Readme

packaged ultralytics/yolov5

pip install yolov5

Overview

Install

Model Zoo

Use from Python

Use from CLI

FAQs

What is musong?

Is musong popular?

Is musong well maintained?

Is musong safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues