Python package with latest versions of YOLO architecture for training and inference
Installing is quite simple, just use pip:
pip3 install det_executorTraining support is still in progress!
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,
),
}from det_executor import DetExecutor
# loading model
name = 'yolov7'
ex = DetExecutor(name)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()- Training pipeline for all models
- Load from custom weights
- More models
@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}
}
