pha123661/NTU-2022Fall-DLCV

Deep Learning for Computer Vision 深度學習於電腦視覺 by Frank Wang 王鈺強

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Experimental

This collection demonstrates advanced techniques in deep learning for computer vision, showcasing how to build systems for tasks like identifying objects in images, generating realistic faces, creating textual descriptions for images, and synthesizing 3D scenes. It takes various forms of image data, ranging from photographs to 3D point clouds, and produces classifications, generated images, descriptive text, or reconstructed 3D views. This resource is ideal for students, researchers, and practitioners exploring state-of-the-art computer vision models and their practical applications.

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Use this if you are studying or working on computer vision problems and want to see practical examples and reports on applying deep learning models for image classification, segmentation, generation, captioning, or 3D scene synthesis.

Not ideal if you are looking for a plug-and-play solution or a beginner's introduction to the absolute basics of deep learning, as this focuses on advanced model implementations and evaluations.

image-classification image-segmentation generative-models image-captioning 3D-reconstruction
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 8 / 25
Community 7 / 25

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Python

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Last pushed

Jun 30, 2024

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