Yolov8 Human Detection Thermal

🚀 Human Detection using Thermal Camera

This model is designed to detect humans from thermal images, offering a reliable solution for human detection tasks.

🚀 Quick Start

This model can be used to detect humans in thermal images. It is applicable to both Pseudo - color and Grayscale thermal images. Although the model was initially fine - tuned for human detection, it can be further fine - tuned to detect other objects using thermal images.

To deploy this model, follow these steps:

📦 Installation

Install the required dependencies:

$ python -m pip install ultralytics supervision huggingface_hub 

💻 Usage Examples

Basic Usage

# import libraries
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
from supervision import Detections
import cv

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

# load model
model = YOLO(model_path)

# method for inference
def inference(image_path):
    cv_image = cv.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

📚 Documentation

Training Code

• Dataset Link: Roboflow Universe

from ultralytics import YOLO
import torch

# load model
model = YOLO("yolov8n.pt")

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

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

• Click here for: Training Arguments

Libraries

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

📄 License

This project is licensed under the AGPL - 3.0 license.