Update README.md

README.md

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-title: Crosswalk Detection Model
+title: Crosswalk Detection App
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@@ -7,6 +7,23 @@ sdk: gradio
 sdk_version: 5.7.1
 app_file: app.py
 pinned: false
+thumbnail: >-
+  https://cdn-uploads.huggingface.co/production/uploads/674d3ccd6b4dbbd9a8561126/Ewwg1TM1nsc3pkqtHI2Cb.png
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+Check out my first app on Hugging Face Spaces - https://lnkd.in/gyHSTTN6.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+What does it do? Detects crosswalks in images of streets/intersections. Simply upload an image and get segmentation masks for crosswalks vs. rest of image, along with % pixels covered by crosswalks in the image. 
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+Why does it matter? Object detection models often struggle with out-of-distribution objects such as crosswalks. Whereas image segmentation makes it possible to create masks for any object. 
+Using the % pixels covered in the image, one can (theoretically) detect crosswalks if they cover > X% of the image. Ref recent research from U.S. Department of Transportation here - https://lnkd.in/gX7XTU3v. 
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+Model training and app development? I trained a custom segmentation model on LandingAI, and used Gradio to write the program - it was easy to publish it in Spaces thereafter. 
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+I hope you try it out and share your feedback with me! 
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+Sample outputs ->
+https://cdn-uploads.huggingface.co/production/uploads/674d3ccd6b4dbbd9a8561126/Ewwg1TM1nsc3pkqtHI2Cb.png
+https://cdn-uploads.huggingface.co/production/uploads/674d3ccd6b4dbbd9a8561126/feS_f7WzB8PPotu7zLFd_.png
+
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference