Detecting masked faces in the pandemic world
Packaging your Machine Learinng message
Vladimir Iglovikov
Sr. Software Engineer at Lyft, Level5
Ph.D. in Physics
Kaggle Grandmaster
Career ladder
Fresh grad without PhD
Fresh grad with PhD
(could have 100500 papers at NeurIPS, CVPR, ECCV, etc)
Glass ceiling
- You work harder than everyone else.
- You train State Of The Art models.
- You publish papers at top conferences.
Most likely (90% chance) you will never get to L6!
Data Scientist skill tree
Ownership
Technical
Communication
Your level
Not your level
Models are not everything!
You need to train
ownership
and
communication!
What SMALL improvements can you add to your modeling process to train ownership and communication?
Example:
Face Mask Detector
Target audience:
- Kagglers
- Academics
- Junior Data Scientists
Three ways
Detector with 2 classes:
mask / no mask
Detector with two heads:
face and face attributes
Detector + classifier
Slowest but the most accurate.
Our choice.
Face Detector
Face classifier
0.99
0.99
0.01
Stage I
Stage II
Face mask detection in two stages
Face detector: simplified RetinaFace
class + bbox + landmarks
class + bbox + landmarks
class + bbox + landmarks
RetinaFace: Single-stage Dense Face Localisation in the wild arXiv:1905.00641
Face Detection: Widerfaces dataset
- http://shuoyang1213.me/WIDERFACE/
- 32,203 images
- 393,703 faces
Training
Predictions: boxes
You have a model. What is next?
- +5 min: Publish your code at GitHub as is.
-
+10 min: Add code formatters and style checkers.
- +20 min: Create a clear readme.
- +20 min: Create a collab notebook with an example.
- +20 min: Make a library and upload it to PyPI
- +20 min: Build a web app.
- +4 hours: Write a blog post.
- +2 hours: Create video with a demo.
pip install retinaface-pytorch
GitHub
PyPi
Collab
import numpy as np
import streamlit as st
from PIL import Image
from retinaface.pre_trained_models import get_model
from retinaface.utils import vis_annotations
import torch
st.set_option("deprecation.showfileUploaderEncoding", False)
@st.cache
def cached_model():
m = get_model("resnet50_2020-07-20", max_size=1024, device="cpu")
m.eval()
return m
model = cached_model()
st.title("Detect faces and key points")
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
image = np.array(Image.open(uploaded_file))
st.image(image, caption="Before", use_column_width=True)
st.write("")
st.write("Detecting faces...")
with torch.no_grad():
annotations = model.predict_jsons(image)
if not annotations[0]["bbox"]:
st.write("No faces detected")
else:
visualized_image = vis_annotations(image, annotations)
st.image(visualized_image, caption="After", use_column_width=True)
Web app. 35 lines of code.
Mask classifier
Network
0.9999
0.0001
Task
Training mask classifier
-
Mask classification collab: https://colab.research.google.com/drive/1VkSK5MKIuGPIA31KJpGiFe_FafYC4xfD
-
Detection + classification collab: https://colab.research.google.com/drive/13Ktsrx164eQHfDmYLyMCoI-Kq0gC5Kg1
-
WebApp: https://facemaskd.herokuapp.com/
The same deliverables
Summary
There are small incremental steps that make your work:
- make your work better
- more visible
Will boost your ownership and comminication.
- Code to Github
- Add readme
- Clean code
- Make a demo notebook
- Build a library
- Build WebApp
- Give a talk
- Write a blogpost
Thank you!
Blog: http://ternaus.blog
Twitter: @viglovikov
Kaggle: https://www.kaggle.com/iglovikov
LinkedIn: https://www.linkedin.com/in/iglovikov/
Packaging your message
By Vladimir Iglovikov
Packaging your message
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