det-executor 0.0.7

Python package with latest versions of YOLO architecture for training and inference

DetExecutor

Python package with latest versions of YOLO architecture for training and inference

Install

Installing is quite simple, just use pip:

pip3 install det_executor

Train

Training support is still in progress!

Inference

Get available models

from det_executor import DetExecutor
# print list of supported arches
DetExecutor.list_arch()

Output

{
    "yolov7": YoloArch(
        version="7",
        img_size=(640, 640),
        size="75.6MB",
        params="37.6M",
        flops="",
        module="yolov7_package",
        load_link="yolov7.pt",
        trainable=False,
        traced=False,
    ),
    "yolov7x": YoloArch(
        version="7",
        img_size=(640, 640),
        size="75.6MB",
        params="71.3M",
        flops="",
        module="yolov7_package",
        load_link="yolov7x.pt",
        trainable=False,
        traced=False,
    ),
    "yolov7-w6": YoloArch(
        version="7",
        img_size=(1280, 1280),
        size="141.3MB",
        params="70.4M",
        flops="",
        module="yolov7_package",
        load_link="yolov7-w6.pt",
        trainable=False,
        traced=False,
    ),
    "yolov7-e6": YoloArch(
        version="7",
        img_size=(1280, 1280),
        size="195.0MB",
        params="97.2M",
        flops="",
        module="yolov7_package",
        load_link="yolov7-e6.pt",
        trainable=False,
        traced=False,
    ),
    "yolov7-d6": YoloArch(
        version="7",
        img_size=(1280, 1280),
        size="286.3MB",
        params="133.8M",
        flops="",
        module="yolov7_package",
        load_link="yolov7-d6.pt",
        trainable=False,
        traced=False,
    ),
    "yolov7-e6e": YoloArch(
        version="7",
        img_size=(1280, 1280),
        size="304.4MB",
        params="151.8M",
        flops="",
        module="yolov7_package",
        load_link="yolov7-e6e.pt",
        trainable=False,
        traced=False,
    ),
    "yolov7-traced": YoloArch(
        version="7",
        img_size=(640, 640),
        size="74.3MB",
        params="36.9M",
        flops="",
        module="yolov7_package",
        load_link="1L8mPcUvabUscEk6Nr8ck5EFgopgPAMDW",
        trainable=False,
        traced=True,
    ),
    "yolov7-tiny": YoloArch(
        version="7",
        img_size=(640, 640),
        size="12.6MB",
        params="6.2M",
        flops="",
        module="yolov7_package",
        load_link="yolov7-tiny.pt",
        trainable=False,
        traced=False,
    ),
    "yolov7-tiny-traced": YoloArch(
        version="7",
        img_size=(640, 640),
        size="12.7MB",
        params="6.2M",
        flops="",
        module="yolov7_package",
        load_link="18zJyljtolPENDI_kFw3FlRFnQTnaLuDF",
        trainable=False,
        traced=True,
    ),
    "yolov8n": YoloArch(
        version="8",
        img_size=(640, 640),
        size="6.5MB",
        params="3.2M",
        flops="",
        module="yolov8",
        load_link="yolov8n.pt",
        trainable=False,
        traced=False,
    ),
    "yolov8s": YoloArch(
        version="8",
        img_size=(640, 640),
        size="22.6MB",
        params="11.2M",
        flops="",
        module="yolov8",
        load_link="yolov8s.pt",
        trainable=False,
        traced=False,
    ),
    "yolov8m": YoloArch(
        version="8",
        img_size=(640, 640),
        size="52.1MB",
        params="25.9M",
        flops="",
        module="yolov8",
        load_link="yolov8m.pt",
        trainable=False,
        traced=False,
    ),
    "yolov8l": YoloArch(
        version="8",
        img_size=(640, 640),
        size="87.8MB",
        params="43.7M",
        flops="",
        module="yolov8",
        load_link="yolov8l.pt",
        trainable=False,
        traced=False,
    ),
    "yolov8x": YoloArch(
        version="8",
        img_size=(640, 640),
        size="136.9MB",
        params="68.2M",
        flops="",
        module="yolov8",
        load_link="yolov8x.pt",
        trainable=False,
        traced=False,
    ),
    "yolos-tiny": YoloArch(
        version="s",
        img_size=None,
        size="136.9MB",
        params="6.5M",
        flops="512x*>18.8G|256x*>3.4G",
        module="yolos",
        load_link="hustvl/yolos-tiny",
        trainable=False,
        traced=False,
    ),
}

Loading model

from det_executor import DetExecutor

# loading model
name = 'yolov7'
ex = DetExecutor(name)

Predict and draw

from det_executor import DetExecutor, draw_on_image
import cv2

# loading model
name = 'yolov7'
ex = DetExecutor(name)

# loading image
img = ex.load_image('test/img.jpg')
# or img = cv2.imread('test/img.jpg')

# predict
classes, boxes, scores = ex.predict(img)

# draw
img = draw_on_image(img, boxes[0], scores[0], classes[0])
cv2.imshow("image", img)
cv2.waitKey()

Roadmap

• Training pipeline for all models
• Load from custom weights
• More models

Citation

@article{wang2022yolov7,
  title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2207.02696},
  year={2022}
}

@misc{fang2021look,
      title={You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection}, 
      author={Yuxin Fang and Bencheng Liao and Xinggang Wang and Jiemin Fang and Jiyang Qi and Rui Wu and Jianwei Niu and Wenyu Liu},
      year={2021},
      eprint={2106.00666},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}