- paper-implementations VS climategan
- paper-implementations VS Deep-Learning-Computer-Vision
- paper-implementations VS blended-latent-diffusion
- paper-implementations VS blended-diffusion
- paper-implementations VS min-3-flow
- paper-implementations VS pytorch-implementations
- paper-implementations VS Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
- paper-implementations VS mmagic
- paper-implementations VS diffusers-interpret
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climategan
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Deep-Learning-Computer-Vision
My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
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blended-diffusion
Official implementation for "Blended Diffusion for Text-driven Editing of Natural Images" [CVPR 2022]
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min-3-flow
A multistage text to image framework. Built from a inference-reduced set of min-dalle, glid-3-xl, and SwinIR.
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Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
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mmagic
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diffusers-interpret
Diffusers-Interpret 🤗🧨🕵️♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.
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Feedback on pytorch vision transformer paper implementation
Project link, https://github.com/quickgrid/paper-implementations/tree/main/pytorch/vision_transformer.
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over 3 years ago
quickgrid/paper-implementations is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of paper-implementations is Jupyter Notebook.
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