Blog:
https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/
Github:
https://github.com/facebookresearch/detectron2
Documentation:
https://detectron2.readthedocs.io/en/latest/tutorials/getting_started.html
Colab tutorial:
https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5#scrollTo=QHnVupBBn9eR
https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/
Github:
https://github.com/facebookresearch/detectron2
Documentation:
https://detectron2.readthedocs.io/en/latest/tutorials/getting_started.html
Colab tutorial:
https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5#scrollTo=QHnVupBBn9eR
Meta
Detectron2: A PyTorch-based modular object detection library
We are open-sourcing Detectron2, the second-generation of our widely used object-recognition platform. Detectron2 has been rewritten from the ground up in PyTorch to enable faster model iteration and deployment.
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CVPR 2021 paper
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion (MiVOS)
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion (MiVOS)
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Paper:
https://arxiv.org/pdf/2103.07941v1.pdf
Github:
https://github.com/hkchengrex/MiVOS
Project page:
https://hkchengrex.github.io/MiVOS/
https://arxiv.org/pdf/2103.07941v1.pdf
Github:
https://github.com/hkchengrex/MiVOS
Project page:
https://hkchengrex.github.io/MiVOS/
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YolactEdge Real time Instance Segmentation on the Edge
https://www.youtube.com/watch?v=pMDwXkIerw8
https://www.youtube.com/watch?v=pMDwXkIerw8
YouTube
YolactEdge Real time Instance Segmentation on the Edge / python tutorial / google colaboratory
@AIcoordinator
python tutorial.
YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti)โฆ
python tutorial.
YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti)โฆ
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Github:
https://github.com/ai-coodinator/yolact_edge
YolactEdge, competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti) with a ResNet-101 backbone on 550x550 resolution images.
https://github.com/ai-coodinator/yolact_edge
YolactEdge, competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti) with a ResNet-101 backbone on 550x550 resolution images.
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Paper:
https://arxiv.org/pdf/2104.01604v1.pdf
Github:
https://github.com/speechbrain/speechbrain/tree/develop/recipes/timers-and-such
Dataset:
https://zenodo.org/record/4623772#.YHFLkxKRWJk
https://arxiv.org/pdf/2104.01604v1.pdf
Github:
https://github.com/speechbrain/speechbrain/tree/develop/recipes/timers-and-such
Dataset:
https://zenodo.org/record/4623772#.YHFLkxKRWJk
GitHub
speechbrain/recipes/timers-and-such at develop ยท speechbrain/speechbrain
A PyTorch-based Speech Toolkit. Contribute to speechbrain/speechbrain development by creating an account on GitHub.
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a2v-demo.gif
16.1 MB
Transferable Interactiveness Knowledge forHuman-Object Interaction Detection
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CS224W: Machine Learning with Graphs - Stanford / Winter 2021
https://www.youtube.com/playlist?list=PLuv1FSpHurUemjLiP4L1x9k6Z9D8rNbYW
Full Stack Deep Learning - Spring 2021 - UC Berkeley
https://www.youtube.com/playlist?list=PLuv1FSpHurUc2nlabZjCLLe8EQa9fOoa9
Introduction to Deep Learning (I2DL) - Technical University of Munich
https://www.youtube.com/playlist?list=PLuv1FSpHurUdmk7v06MDyIx0SDxTrIoqk
3D Computer Vision - National University of Singapore - 2021
https://www.youtube.com/playlist?list=PLuv1FSpHurUflLnJF6hgi0FkeNG1zSFCZ
CV3DST - Computer Vision 3: Detection, Segmentation and Tracking
https://www.youtube.com/playlist?list=PLuv1FSpHurUd08wNo1FMd3eCUZXm8qexe
ADL4CV - Advanced Deep Learning for Computer Vision
https://www.youtube.com/playlist?list=PLuv1FSpHurUcQi2CwFIVQelSFCzxphJqz
https://www.youtube.com/playlist?list=PLuv1FSpHurUemjLiP4L1x9k6Z9D8rNbYW
Full Stack Deep Learning - Spring 2021 - UC Berkeley
https://www.youtube.com/playlist?list=PLuv1FSpHurUc2nlabZjCLLe8EQa9fOoa9
Introduction to Deep Learning (I2DL) - Technical University of Munich
https://www.youtube.com/playlist?list=PLuv1FSpHurUdmk7v06MDyIx0SDxTrIoqk
3D Computer Vision - National University of Singapore - 2021
https://www.youtube.com/playlist?list=PLuv1FSpHurUflLnJF6hgi0FkeNG1zSFCZ
CV3DST - Computer Vision 3: Detection, Segmentation and Tracking
https://www.youtube.com/playlist?list=PLuv1FSpHurUd08wNo1FMd3eCUZXm8qexe
ADL4CV - Advanced Deep Learning for Computer Vision
https://www.youtube.com/playlist?list=PLuv1FSpHurUcQi2CwFIVQelSFCzxphJqz
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arch.gif
20.5 MB
Advancing the state of the art in computer vision with self-supervised Transformers and 10x more efficient training
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๐ฅ Self-Supervised Vision Transformers with DINO
Blogpost:
https://ai.facebook.com/blog/dino-paws-computer-vision-with-self-supervised-transformers-and-10x-more-efficient-training/
Paper:
https://arxiv.org/pdf/2104.14294.pdf
Code:
https://github.com/facebookresearch/dino
Blogpost:
https://ai.facebook.com/blog/dino-paws-computer-vision-with-self-supervised-transformers-and-10x-more-efficient-training/
Paper:
https://arxiv.org/pdf/2104.14294.pdf
Code:
https://github.com/facebookresearch/dino
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Github:
https://github.com/OlafenwaMoses/DeepStack_ExDark
Documentation:
https://docs.deepstack.cc/index.html#installation
Custom dataset preparation:
https://docs.deepstack.cc/custom-models/datasetprep/index.html
Training custom model:
https://docs.deepstack.cc/custom-models/training/index.html
https://github.com/OlafenwaMoses/DeepStack_ExDark
Documentation:
https://docs.deepstack.cc/index.html#installation
Custom dataset preparation:
https://docs.deepstack.cc/custom-models/datasetprep/index.html
Training custom model:
https://docs.deepstack.cc/custom-models/training/index.html
GitHub
GitHub - OlafenwaMoses/DeepStack_ExDark: A DeepStack custom model for detecting common objects in dark/night images and videos.
A DeepStack custom model for detecting common objects in dark/night images and videos. - OlafenwaMoses/DeepStack_ExDark
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500 + ๐๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ถ๐ฎ๐น ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ถ๐๐ ๐๐ถ๐๐ต ๐ฐ๐ผ๐ฑ๐ฒ
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
GitHub
GitHub - ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code: 500 AI Machine learning Deepโฆ
500 AI Machine learning Deep learning Computer vision NLP Projects with code - ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
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Synthesizing Light Field From a Single Image with Variable MPI and Two Network Fusion
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