fcakyon/yolov5-pip
Fork: 68 Star: 275 (更新于 1970-01-01 00:00:00)
license: GPL-3.0
Language: Python .
Packaged version of ultralytics/yolov5 + many extra features
最后发布版本： 7.0.13 ( 2023-10-16 18:17:06)
packaged ultralytics/yolov5
pip install yolov5
Overview
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
- Train your model using Roboflow Universe datasets (roboflow>=0.2.29 required):
$ 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 (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_dirintodata.yamlto 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/
最近版本更新:(数据更新于 1970-01-01 00:00:00)
2023-10-16 18:17:06 7.0.13
2023-05-16 04:29:29 7.0.12
2023-03-21 19:57:27 7.0.11
2023-03-21 05:17:12 7.0.10
2023-02-13 03:38:28 7.0.9
2023-02-01 04:53:36 7.0.8
2023-01-05 15:45:23 7.0.7
2022-12-31 20:06:48 7.0.6
2022-12-27 07:06:02 7.0.5
2022-12-24 07:36:43 7.0.4
主题(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
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