license: agpl-3.0 library: ultralytics tags:

• 目标检测
• pytorch
• ultralytics
• roboflow-universe
• 人体检测
• yolov8

基于热成像摄像头的人体检测

应用场景

本模型专用于从热成像图像中检测人体，适用于伪彩色和灰度热成像图像。虽然模型已针对人体检测进行优化，但可通过微调用于检测热成像中的其他目标。

部署代码如下：

• 安装依赖：

$ python -m pip install ultralytics supervision huggingface_hub 

• Python代码

# 导入库
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
from supervision import Detections
import cv2

# 下载模型
model_path = hf_hub_download(
    repo_id = "pitangent-ds/YOLOv8-human-detection-thermal",
    filename = "model.pt"
)

# 加载模型
model = YOLO(model_path)

# 推理方法
def inference(image_path):
    cv_image = cv2.imread(image_path, cv2.IMREAD_ANYCOLOR)
    model_output = model(cv_image, conf=0.6, verbose=False)
    detections = Detections.from_ultralytics(model_output[0])
    return detections

训练代码

• 数据集链接：Roboflow Universe

from ultralytics import YOLO
import torch

# 加载模型
model = YOLO("yolov8n.pt")

# 超参数配置
hyperparams = {
    "batch": 32,
    "epochs": 30,
    "imgsz": [640, 480],
    "optimizer": "AdamW",
    "cos_lr": True,
    "lr0": 3e-5,
    "warmup_epochs": 10
}

# 开始训练
model.train(
    device = 'cuda' if torch.cuda.is_available() else 'cpu',
    data = "data.yaml",
    **hyperparams
)

• 训练参数详见：训练参数文档

依赖库

python: 3.10.13
ultralytics: 8.0.206
torch: "2.1.0+cu118"
roboflow: 1.1.9