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musong

Packaged version of ultralytics/yolov5 + many extra features

https://github.com/fcakyon/yolov5-pip

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    DOI references
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    Committers with academic emails
    2 of 15 committers (13.3%) from academic institutions
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  • ○
    Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary

Keywords

aws cli coco computer-vision deep-learning machine-learning neptune neptune-ai object-detection pip pypi python pytorch s3 ultralytics yolo yolov3 yolov4 yolov5

Keywords from Contributors

transformer agents fine-tuning instance-segmentation yolo11
Last synced: 6 months ago · JSON representation

Repository

Packaged version of ultralytics/yolov5 + many extra features

Basic Info
Statistics
  • Stars: 294
  • Watchers: 4
  • Forks: 70
  • Open Issues: 3
  • Releases: 54
Topics
aws cli coco computer-vision deep-learning machine-learning neptune neptune-ai object-detection pip pypi python pytorch s3 ultralytics yolo yolov3 yolov4 yolov5
Created about 5 years ago · Last pushed 12 months ago
Metadata Files
Readme Funding License

README.md

packaged ultralytics/yolov5

pip install yolov5

total downloads monthly downloads fcakyon twitter
pypi version ci testing package testing

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)

console pip install yolov5

Model Zoo

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

Use from Python

```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.multilabel = False # NMS multiple labels per box model.maxdet = 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: ```python 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: ```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) ```

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`: ```bash $ 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: ```yaml # data.yml train_json_path: "train.json" train_image_dir: "train_image_dir/" val_json_path: "val.json" val_image_dir: "val_image_dir/" ``` ```bash $ yolov5 train --data data.yaml --weights yolov5s.pt ``` - Train your model using [Roboflow Universe](https://universe.roboflow.com/) datasets (roboflow>=0.2.29 required): ```bash $ yolov5 train --data DATASET_UNIVERSE_URL --weights yolov5s.pt --roboflow_token YOUR_ROBOFLOW_TOKEN ``` Where `DATASET_UNIVERSE_URL` must be in `https://universe.roboflow.com/workspace_name/project_name/project_version` format. - Visualize your experiments via [Neptune.AI](https://neptune.ai/) (neptune-client>=0.10.10 required): ```bash $ yolov5 train --data data.yaml --weights yolov5s.pt --neptune_project NAMESPACE/PROJECT_NAME --neptune_token YOUR_NEPTUNE_TOKEN ``` - Automatically upload weights to [Huggingface Hub](https://huggingface.co/models?other=yolov5): ```bash $ 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): ```bash export AWS_ACCESS_KEY_ID=YOUR_KEY export AWS_SECRET_ACCESS_KEY=YOUR_KEY ``` ```bash $ 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. ```yaml # 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](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash $ 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`: ```bash $ yolov5 export --weights yolov5s.pt --include torchscript,onnx,coreml,pb,tfjs ```
Classify Train/Val/Predict with YOLOv5 image classifier: ```bash $ yolov5 classify train --img 640 --data mnist2560 --weights yolov5s-cls.pt --epochs 1 ``` ```bash $ yolov5 classify predict --img 640 --weights yolov5s-cls.pt --source images/ ```
Segment Train/Val/Predict with YOLOv5 instance segmentation model: ```bash $ yolov5 segment train --img 640 --weights yolov5s-seg.pt --epochs 1 ``` ```bash $ yolov5 segment predict --img 640 --weights yolov5s-seg.pt --source images/ ```

Owner

  • Name: fatih akyon
  • Login: fcakyon
  • Kind: user
  • Location: Ankara, Turkiye
  • Company: @viddexa @ultralytics

helping AI's to understand videos at @ultralytics & @viddexa

GitHub Events

Total
  • Watch event: 6
  • Delete event: 1
  • Issue comment event: 4
  • Push event: 2
  • Pull request review comment event: 5
  • Pull request review event: 8
  • Pull request event: 5
  • Fork event: 3
  • Create event: 1
Last Year
  • Watch event: 6
  • Delete event: 1
  • Issue comment event: 4
  • Push event: 2
  • Pull request review comment event: 5
  • Pull request review event: 8
  • Pull request event: 5
  • Fork event: 3
  • Create event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 250
  • Total Committers: 15
  • Avg Commits per committer: 16.667
  • Development Distribution Score (DDS): 0.064
Past Year
  • Commits: 3
  • Committers: 3
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.667
Top Committers
Name Email Commits
fatih 3****n 234
Piotr Skalski p****2@g****m 2
Kadir Nar k****r@h****m 2
ngxingyu n****u@u****u 1
merdini m****n@g****m 1
Zacchaeus Scheffer S****t 1
Petros626 6****6 1
Muhammad Salman Kabir 5****n 1
Muhammad Arif Faizin 4****n 1
Lai Quang Huy 6****h 1
Kazybek Askarbek k****k@i****y 1
Juan Carlos Roman j****r@g****m 1
Ihsan Soydemir s****n@g****m 1
Hasan Emir Akın 9****n 1
ABYZOU 8****e 1
Committer Domains (Top 20 + Academic)
innopolis.university: 1 u.nus.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 0
  • Total pull requests: 116
  • Average time to close issues: N/A
  • Average time to close pull requests: 6 days
  • Total issue authors: 0
  • Total pull request authors: 19
  • Average comments per issue: 0
  • Average comments per pull request: 0.45
  • Merged pull requests: 98
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 6
  • Average time to close issues: N/A
  • Average time to close pull requests: 29 days
  • Issue authors: 0
  • Pull request authors: 3
  • Average comments per issue: 0
  • Average comments per pull request: 1.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • fcakyon (86)
  • SIR-unit (3)
  • SkalskiP (3)
  • kadirnar (3)
  • lachiewalker (2)
  • muhammadariffaizin (2)
  • topherbuckley (2)
  • jc-roman (2)
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  • 1qh (2)
  • Isydmr (1)
  • keremberke (1)
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  • severin-lemaignan (1)
  • 5a7man (1)
Top Labels
Issue Labels
Pull Request Labels
enhancement (22) documentation (6) bug (6)

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 71,496 last-month
  • Total docker downloads: 819
  • Total dependent packages: 17
    (may contain duplicates)
  • Total dependent repositories: 72
    (may contain duplicates)
  • Total versions: 59
  • Total maintainers: 2
pypi.org: yolov5

Packaged version of the Yolov5 object detector

  • Versions: 55
  • Dependent Packages: 17
  • Dependent Repositories: 72
  • Downloads: 71,479 Last month
  • Docker Downloads: 819
Rankings
Dependent packages count: 0.7%
Downloads: 0.9%
Dependent repos count: 1.8%
Docker downloads count: 2.1%
Average: 2.4%
Stargazers count: 3.8%
Forks count: 5.3%
Maintainers (1)
fcakyon
Last synced: 6 months ago
pypi.org: musong

Packaged version of the Yolov5 object detector

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 17 Last month
Rankings
Stargazers count: 4.0%
Forks count: 5.5%
Dependent packages count: 6.6%
Average: 13.0%
Downloads: 18.1%
Dependent repos count: 30.6%
Maintainers (1)
musong
Last synced: 7 months ago

Dependencies

requirements.txt pypi
  • Pillow >=7.1.2
  • PyYAML >=5.3.1
  • boto3 >=1.19.1
  • fire *
  • matplotlib >=3.2.2
  • numpy >=1.18.5
  • opencv-python >=4.1.2
  • pandas >=1.1.4
  • protobuf <=3.20.1
  • requests >=2.23.0
  • sahi >=0.9.1
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • tensorboard >=2.4.1
  • thop *
  • torch >=1.7.0
  • torchvision >=0.8.1
  • tqdm >=4.41.0
.github/workflows/ci.yml actions
  • actions/cache v3 composite
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
.github/workflows/package_testing.yml actions
  • actions/cache v3 composite
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
.github/workflows/publish_pypi.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
setup.py pypi