LaSOT
Large-scale Single Object Tracking (LaSOT) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance.
http://vision.cs.stonybrook.edu/~lasot/
Github: https://github.com/HengLan/LaSOT_Evaluation_Toolkit
Dataset: http://vision.cs.stonybrook.edu/~lasot/download.html
Paper: https://arxiv.org/abs/2009.03465
@Machine_learn
Large-scale Single Object Tracking (LaSOT) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance.
http://vision.cs.stonybrook.edu/~lasot/
Github: https://github.com/HengLan/LaSOT_Evaluation_Toolkit
Dataset: http://vision.cs.stonybrook.edu/~lasot/download.html
Paper: https://arxiv.org/abs/2009.03465
@Machine_learn
GitHub
GitHub - HengLan/LaSOT_Evaluation_Toolkit: [CVPR 2019 & IJCV 2021] LaSOT: A High-quality Benchmark for Large-scale Single Object…
[CVPR 2019 & IJCV 2021] LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking - HengLan/LaSOT_Evaluation_Toolkit
Improving Sparse Training with RigL
https://ai.googleblog.com/2020/09/improving-sparse-training-with-rigl.html
Github: https://github.com/google-research/rigl
Paper: https://arxiv.org/abs/1911.11134
@Machine_learn
https://ai.googleblog.com/2020/09/improving-sparse-training-with-rigl.html
Github: https://github.com/google-research/rigl
Paper: https://arxiv.org/abs/1911.11134
@Machine_learn
research.google
Improving Sparse Training with RigL
Posted by Utku Evci and Pablo Samuel Castro, Research Engineers, Google Research, Montreal Modern deep neural network architectures are often highl...
This media is not supported in your browser
VIEW IN TELEGRAM
Pixelopolis, a self-driving car demo from Google I/O built with TF-Lite
@Machine_learn
https://blog.tensorflow.org/2020/07/pixelopolis-self-driving-car-demo-tensorflow-lite.html
@Machine_learn
https://blog.tensorflow.org/2020/07/pixelopolis-self-driving-car-demo-tensorflow-lite.html
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Releases the pre-trained first-stage pytorch models of MobileNet-V1 structure, the pre-processed training&testing dataset and codebase.
Github: https://github.com/cleardusk/3DDFA
Paper: https://arxiv.org/abs/2009.09960v1
@Machine_learn
Releases the pre-trained first-stage pytorch models of MobileNet-V1 structure, the pre-processed training&testing dataset and codebase.
Github: https://github.com/cleardusk/3DDFA
Paper: https://arxiv.org/abs/2009.09960v1
@Machine_learn
Measuring dataset similarity using optimal transport
https://www.microsoft.com/en-us/research/blog/measuring-dataset-similarity-using-optimal-transport/
@Machine_learn
https://www.microsoft.com/en-us/research/blog/measuring-dataset-similarity-using-optimal-transport/
@Machine_learn
Microsoft Research
Measuring dataset similarity using optimal transport - Microsoft Research
Is FashionMNIST, a dataset of images of clothing items labeled by category, more similar to MNIST or to USPS, both of which are classification datasets of handwritten digits? This is a pretty hard question to answer, but the solution could have an impact…
Seeing Theory
🎲 A visual introduction to probability and statistics
https://seeing-theory.brown.edu/index.html#4thPage
📗 Free book: https://seeing-theory.brown.edu/doc/seeing-theory.pdf
@Machine_learn
🎲 A visual introduction to probability and statistics
https://seeing-theory.brown.edu/index.html#4thPage
📗 Free book: https://seeing-theory.brown.edu/doc/seeing-theory.pdf
@Machine_learn
seeing-theory.brown.edu
Seeing Theory
A visual introduction to probability and statistics.
Boosting quantum computer hardware performance with TensorFlow
https://blog.tensorflow.org/2020/10/boosting-quantum-computer-hardware.html
@Machine_learn
https://blog.tensorflow.org/2020/10/boosting-quantum-computer-hardware.html
@Machine_learn
blog.tensorflow.org
Boosting quantum computer hardware performance with TensorFlow
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.
Real-time semantic segmentation in the browser - Made With TensorFlow.js
https://www.youtube.com/watch?v=3XzQQlh_p1c
🆔@Machine_learn
https://www.youtube.com/watch?v=3XzQQlh_p1c
🆔@Machine_learn
YouTube
Real-time semantic segmentation in the browser - Made with TensorFlow.js
Our 2nd episode of Made with TensorFlow.js heads to Brazil to join Hugo Zanini, a Python developer who was looking to use the latest cutting edge research from the TensorFlow community in the browser using JavaScript. Join us as Hugo takes us through his…
Cool New Features in Python 3.9
https://realpython.com/courses/cool-new-features-python-39/
@Machine_learn
https://realpython.com/courses/cool-new-features-python-39/
@Machine_learn
Realpython
Cool New Features in Python 3.9 – Real Python
In this course, you'll explore some of the coolest and most useful features in the newly released Python 3.9. You'll learn how Python 3.9 makes it easier to work with time zones, dictionaries, decorators, and several other techniques that will make your code…
❤1
This is a list of awesome articles about object detection. If you want to read the paper according to time
https://github.com/amusi/awesome-object-detection
👉@Machine_learn
https://github.com/amusi/awesome-object-detection
👉@Machine_learn
GitHub
GitHub - amusi/awesome-object-detection: Awesome Object Detection based on handong1587 github: https://handong1587.github.io/d…
Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html - amusi/awesome-object-detection
Transforming sounds into musical instruments used in a variety of styles, from Baroque to jazz using machine learning, created by the Magenta and AIUX team within Google Research.
https://sites.research.google/tonetransfer
Intro Video:
https://youtu.be/bXBliLjImio
Blog post:
https://magenta.tensorflow.org/ddsp
Colab:
https://colab.research.google.com/github/magenta/ddsp/blob/master/ddsp/colab/demos/timbre_transfer.ipynb
https://github.com/magenta/ddsp/tree/master/ddsp/colab/tutorials
Github:
https://github.com/magenta/ddsp
@Machine_learn
https://sites.research.google/tonetransfer
Intro Video:
https://youtu.be/bXBliLjImio
Blog post:
https://magenta.tensorflow.org/ddsp
Colab:
https://colab.research.google.com/github/magenta/ddsp/blob/master/ddsp/colab/demos/timbre_transfer.ipynb
https://github.com/magenta/ddsp/tree/master/ddsp/colab/tutorials
Github:
https://github.com/magenta/ddsp
@Machine_learn
sites.research.google
Tone Transfer — Magenta DDSP
Play around with different inputs and outputs to see how machine learning transforms sounds.
Partial FC
Distributed deep learning training framework for face recognition.
Github: https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc
Paper: https://arxiv.org/abs/2010.05222v1
Largest Face Recognition Dataset: https://www.dropbox.com/sh/gdix4jabzlwtk72/AAAXEItN1zwdo_tzOx5-QqHWa?dl=0
@Machine_learn
Distributed deep learning training framework for face recognition.
Github: https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc
Paper: https://arxiv.org/abs/2010.05222v1
Largest Face Recognition Dataset: https://www.dropbox.com/sh/gdix4jabzlwtk72/AAAXEItN1zwdo_tzOx5-QqHWa?dl=0
@Machine_learn
Recreating Historical Streetscapes Using Deep Learning and Crowdsourcing
http://ai.googleblog.com/2020/10/recreating-historical-streetscapes.html
@Machine_learn
http://ai.googleblog.com/2020/10/recreating-historical-streetscapes.html
@Machine_learn
research.google
Recreating Historical Streetscapes Using Deep Learning and Crowdsourcing
Posted by Raimondas Kiveris, Software Engineer, Google Research For many, gazing at an old photo of a city can evoke feelings of both nostalgia and...
An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes
Github: https://github.com/Guepardow/Visual-features
Paper: https://arxiv.org/abs/2010.07881
@Machine_learn
Github: https://github.com/Guepardow/Visual-features
Paper: https://arxiv.org/abs/2010.07881
@Machine_learn