kadirnar / Yolov10

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Model's Last Updated: May 25 2024
object-detection

Introduction of Yolov10

Model Details of Yolov10

Model Description

YOLOv10: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Paper Repo: Implementation of paper - YOLOv10

Installation
pip install supervision git+https://github.com/THU-MIG/yolov10.git
Yolov10 Inference
from ultralytics import YOLOv10
import supervision as sv
import cv2

MODEL_PATH = 'yolov10n.pt'
IMAGE_PATH = 'dog.jpeg'

model = YOLOv10(MODEL_PATH)
image  = cv2.imread(IMAGE_PATH)
results = model(source=image, conf=0.25, verbose=False)[0]
detections = sv.Detections.from_ultralytics(results)
box_annotator = sv.BoxAnnotator()

category_dict = {
    0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
    6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
    11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
    16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
    22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
    27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
    32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
    36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
    40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
    46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
    51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
    56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
    61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
    67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
    72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
    77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
}

labels = [
    f"{category_dict[class_id]} {confidence:.2f}"
    for class_id, confidence in zip(detections.class_id, detections.confidence)
]
annotated_image = box_annotator.annotate(
    image.copy(), detections=detections, labels=labels
)

cv2.imwrite('annotated_dog.jpeg', annotated_image)
BibTeX Entry and Citation Info
@misc{wang2024yolov10,
     title={YOLOv10: Real-Time End-to-End Object Detection}, 
     author={Ao Wang and Hui Chen and Lihao Liu and Kai Chen and Zijia Lin and Jungong Han and Guiguang Ding},
     year={2024},
     eprint={2405.14458},
     archivePrefix={arXiv},
     primaryClass={cs.CV}
}

Runs of kadirnar Yolov10 on huggingface.co

215
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More Information About Yolov10 huggingface.co Model

More Yolov10 license Visit here:

https://choosealicense.com/licenses/agpl-3.0

Yolov10 huggingface.co

Yolov10 huggingface.co is an AI model on huggingface.co that provides Yolov10's model effect (), which can be used instantly with this kadirnar Yolov10 model. huggingface.co supports a free trial of the Yolov10 model, and also provides paid use of the Yolov10. Support call Yolov10 model through api, including Node.js, Python, http.

kadirnar Yolov10 online free

Yolov10 huggingface.co is an online trial and call api platform, which integrates Yolov10's modeling effects, including api services, and provides a free online trial of Yolov10, you can try Yolov10 online for free by clicking the link below.

kadirnar Yolov10 online free url in huggingface.co:

https://huggingface.co/kadirnar/Yolov10

Yolov10 install

Yolov10 is an open source model from GitHub that offers a free installation service, and any user can find Yolov10 on GitHub to install. At the same time, huggingface.co provides the effect of Yolov10 install, users can directly use Yolov10 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Yolov10 install url in huggingface.co:

https://huggingface.co/kadirnar/Yolov10

Url of Yolov10

Provider of Yolov10 huggingface.co

kadirnar
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