Face Detection Computer Vision Model
How to use the Face Detection Detection API
Try This Model
Drop an image here or click to upload
Code Snippets
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="face-detection-mik1i-timqv/1")Or, Use Free Face and I Detection API
Powered by general detection model
pip install inference-sdk# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workspace
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="<YOUR_WORKSPACE>",
workflow_id="<YOUR_WORKFLOW_ID>",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"classes": "face, i"
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Run on custom image
Drop an image here or click to upload
About Face Detection Model
Background Information
This dataset was curated and annotated by Mohamed Traore and Justin Brady after forking the raw images from the Roboflow Universe Mask Wearing dataset and remapping the mask and no-mask classes to face.
The main objective is to identify human faces in images or video. However, this model could be used for privacy purposes with changing the output of the bounding boxes to blur the detected face or fill it with a black box.
The original custom dataset (v1) is composed of 867 unaugmented (raw) images of people in various environments. 55 of the images are marked as Null to help with feature extraction and reducing false detections.
Version 2 (v2) includes the augmented and trained version of the model. This version is trained from the COCO model checkpoint to take advantage of transfer learning and improve initial model training results.
Model Updates:
After a few trainings, and running tests with Roboflow's webcam model and Roboflow's video inference repo, it was clear that edge cases like hands sometimes recognized as faces was an issue. I grabbed some images from Alex Wong's Hand Signs dataset (96 images from the dataset) and added them to the project. I uploaded the images, without the annotation files, labeled all the faces, and retrained the model (version 5).
The dataset is available under the CC BY 4.0 license.
Includes images from:
@misc{ person-hgivm_dataset,
title = { person Dataset },
type = { Open Source Dataset },
author = { Abner },
howpublished = { \url{ https://universe.roboflow.com/abner/person-hgivm } },
url = { https://universe.roboflow.com/abner/person-hgivm },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2021 },
month = { aug },
note = { visited on 2022-10-14 },
}
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ face-detection-mik1i-timqv_dataset,
title = { Face Detection Dataset },
type = { Open Source Dataset },
author = { nhap },
howpublished = { \url{ https://universe.roboflow.com/nhap/face-detection-mik1i-timqv } },
url = { https://universe.roboflow.com/nhap/face-detection-mik1i-timqv },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2025 },
month = { sep },
note = { visited on 2026-07-29 },
}