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Multiclass Image Segmentation using UNETR in TensorFlow | Vision Transformer for Image Segmentation
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📺 Video Description: In this video, we are going to train the UNEt TRansformers (UNETR) architecture on the Landmark Guided Face Parsing dataset (LaPa) dataset for Multiclass Image Segmentation.
UNETR, or UNet Transformer, is a specialized architecture for medical image segmentation. It uses a pure transformer as the encoder, focusing on learning sequence representations for the input volume to capture global multi-scale information. The encoder connects directly to a decoder through skip connections, forming a U-Net-like structure and producing the ultimate semantic segmentation output.
🕒 Timeline:
00:00 - Introduction
00:59 - Landmark Guided Face Parsing dataset (LaPa) dataset.
02:46 - UNETR Architecture
04:35 - Training the UNETR
16:23 - Testing the UNETR
31:36 - Conclusion
💡Support:
🌐 Connect with Me:
Instagram: instagram/nikhilroxtomar
Immerse yourself in the world of UNETR and revolutionize your understanding of image segmentation. Subscribe, code along, and let's embark on this transformative journey together!
UNETR, or UNet Transformer, is a specialized architecture for medical image segmentation. It uses a pure transformer as the encoder, focusing on learning sequence representations for the input volume to capture global multi-scale information. The encoder connects directly to a decoder through skip connections, forming a U-Net-like structure and producing the ultimate semantic segmentation output.
🕒 Timeline:
00:00 - Introduction
00:59 - Landmark Guided Face Parsing dataset (LaPa) dataset.
02:46 - UNETR Architecture
04:35 - Training the UNETR
16:23 - Testing the UNETR
31:36 - Conclusion
💡Support:
🌐 Connect with Me:
Instagram: instagram/nikhilroxtomar
Immerse yourself in the world of UNETR and revolutionize your understanding of image segmentation. Subscribe, code along, and let's embark on this transformative journey together!
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