YOLO-Face-Person-Detector

YOLOv8x Face & Person Detector

<div align="center"> <a href="https://huggingface.co/spaces/iitolstykh/MiVOLO-Demo"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in Spaces"> </a> <img src="images/image.png" width="500" alt="YOLO Detection Example"/> <!-- <video controls autoplay loop muted playsinline width="80%"> <source src="images/output.mp4" type="video/mp4"> </video> --> </div>

Model Description

This model is a fine-tuned version of YOLOv8x specialized in detecting two specific classes: Face and Person.

It has been trained on a large-scale proprietary dataset consisting of approximately 150,000 images. The high capacity of the YOLOv8x architecture combined with a diverse proprietary dataset ensures high accuracy and robustness in various scenarios.

How to Use

Installation

pip install ultralytics==8.1.0 torch==2.5.1 transformers huggingface_hub

1. Use with transformers

You can load the model using the Hugging Face transformers library by enabling custom code execution.

from transformers import AutoModel
from PIL import Image
import torch

# 1. Load model with trust_remote_code=True
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = AutoModel.from_pretrained(
    "iitolstykh/YOLO-Face-Person-Detector", 
    trust_remote_code=True,
    dtype=torch_dtype,
).to(device)

# 2. Load image (You can use URL, PIL.Image or np.ndarray)
image = Image.open("path/to/your/image.jpg")
# image = cv2.imread("path/to/your/image.jpg")

# 3. Perform inference
results = model(image, conf=0.4, iou=0.7)[0]

# 4. Process results
print("Found objects:", [results.names[int(det.cls)] for det in results.boxes])
print("Boxes:", results.boxes)
# render_result(model=model.yolo, image=image, result=results).show()

2. Use with ultralytics

If you prefer the standard Ultralytics API, you can download the weights from the Hub and load them directly.

from ultralytics import YOLO
from huggingface_hub import hf_hub_download
import torch

# 1. Download model weights
model_path = hf_hub_download(
    repo_id="iitolstykh/YOLO-Face-Person-Detector",
    filename="yolov8x_person_face.pt",
    repo_type="model"
)

# 2. Load model
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = YOLO(model_path)
model.fuse()
if torch_dtype is torch.float16:
    model.model = model.model.half()
model.to(device)

# 3. Perform inference
image = 'https://variety.com/wp-content/uploads/2023/04/MCDNOHA_SP001.jpg' 
results = model.predict(image, conf=0.4, iou=0.7, half=torch_dtype is torch.float16)

# 4. Show results
for result in results:
    boxes = result.boxes
    print("Found objects:", [result.names[int(c)] for c in boxes.cls])

3. Use with ultralyticsplus

This method automatically handles model downloading for ultralytics YOLO model.

pip install ultralyticsplus==0.1.0

from ultralyticsplus import YOLO, render_result

# 1. Load model
model = YOLO('iitolstykh/YOLO-Face-Person-Detector')

# 2. Set model parameters
model.overrides['conf'] = 0.4
model.overrides['iou'] = 0.7
model.overrides['max_det'] = 100

# 3. Set image (You can use URL, PIL.Image or np.ndarray)
image = 'https://variety.com/wp-content/uploads/2023/04/MCDNOHA_SP001.jpg'

# 4. Perform inference
results = model.predict(image)

# 5. Show results
print("Found objects:", [results[0].names[int(det.cls)] for det in results[0].boxes])
render = render_result(model=model, image=image, result=results[0])
render.show()

License

This model is based on the Ultralytics YOLOv8 architecture and inherits the AGPL-3.0 License.

Please refer to the official Ultralytics Licensing details for more information regarding commercial usage and restrictions.

Citation

🌟 If you find our work helpful, please consider citing our papers and leaving valuable stars

@article{mivolo2023,
   Author = {Maksim Kuprashevich and Irina Tolstykh},
   Title = {MiVOLO: Multi-input Transformer for Age and Gender Estimation},
   Year = {2023},
   Eprint = {arXiv:2307.04616},
}

@article{mivolo2024,
   Author = {Maksim Kuprashevich and Grigorii Alekseenko and Irina Tolstykh},
   Title = {Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation},
   Year = {2024},
   Eprint = {arXiv:2403.02302},
}

@article{cerberusdet,
   Author = {Irina Tolstykh,Michael Chernyshov,Maksim Kuprashevich},
   Title = {CerberusDet: Unified Multi-Dataset Object Detection},
   Year = {2024},
   Eprint = {arXiv:2407.12632},
}