Face Detection using YOLOv8
This model was fine tuned on a dataset of over 10k images containing human faces. The model was fine tuned for 100 epochs with a batch size of 16 on a single NVIDIA V100 16GB GPU, it took around 140 minutes for the fine tuning to complete.
Downstream Tasks
- Face Detection: This model can directly use this model for face detection or it can be further fine tuned own a custom dataset to improve the prediction capabilities.
- Face Recognition: This model can be fine tuned to for face recognition tasks as well, create a dataset with the images of faces and label them accordingly using name or any ID and then use this model as a base model for fine tuning.
Example Usage
# load libraries
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
from supervision import Detections
from PIL import Image
# download model
model_path = hf_hub_download(repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt")
# load model
model = YOLO(model_path)
# inference
image_path = "/path/to/image"
output = model(Image.open(image_path))
results = Detections.from_ultralytics(output[0])
Links
- Dataset Source: Roboflow Universe
- Weights & Biases: Run Details
- Training Artifacts: training-artifacts