| license: agpl-3.0 | |
| library: ultralytics | |
| tags: | |
| - object-detection | |
| - pytorch | |
| - roboflow-universe | |
| - pickle | |
| - face-detection | |
| # 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 | |
| ```python | |
| # 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](https://universe.roboflow.com/large-benchmark-datasets/wider-face-ndtcz/dataset/1) | |
| - __Weights & Biases__: [Run Details](https://wandb.ai/2wb2ndur/Face-Detection/overview?workspace=user-2wb2ndur) | |
| - __Training Artifacts__: [training-artifacts](./fine-tune-artifacts/) |