This media is not supported in your browser
VIEW IN TELEGRAM
✅ DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients
🖥 Github: https://github.com/cvg/deeplsd
⏩ Paprer: https://arxiv.org/abs/2212.07766v1
✔️ Dataset: https://paperswithcode.com/dataset/hpatches
@Machine_learn
git clone --recurse-submodules [email protected]:cvg/DeepLSD.git
cd DeepLSD
🖥 Github: https://github.com/cvg/deeplsd
⏩ Paprer: https://arxiv.org/abs/2212.07766v1
✔️ Dataset: https://paperswithcode.com/dataset/hpatches
@Machine_learn
⏩ GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
🖥 Github: https://github.com/chenhongyiyang/gpvit
➡️Paprer: https://arxiv.org/abs/2212.06795v1
✔️Data Preparation: https://paperswithcode.com/dataset/must-c
@Machine_learn
🖥 Github: https://github.com/chenhongyiyang/gpvit
➡️Paprer: https://arxiv.org/abs/2212.06795v1
✔️Data Preparation: https://paperswithcode.com/dataset/must-c
@Machine_learn
This media is not supported in your browser
VIEW IN TELEGRAM
✅️ JRBD: Egocentric Perception of Humans
⭐️ Dataset: https://jrdb.erc.monash.edu/
🖥 Github: https://github.com/JRDB-dataset/jrdb_toolkit/
⏩ JRDB-Pose: https://jrdb.erc.monash.edu/dataset/pose#toolkit
✅ Paper: arxiv.org/pdf/1910.11792.pdf
@Machine_learn
⭐️ Dataset: https://jrdb.erc.monash.edu/
🖥 Github: https://github.com/JRDB-dataset/jrdb_toolkit/
⏩ JRDB-Pose: https://jrdb.erc.monash.edu/dataset/pose#toolkit
✅ Paper: arxiv.org/pdf/1910.11792.pdf
@Machine_learn
⚡️ MVTN: Learning Multi-View Transformations for 3D Understanding
🖥Github: https://github.com/ajhamdi/mvtorch
⭐️ Paper: https://arxiv.org/abs/2212.13462v1
⏩ Dataset: https://paperswithcode.com/dataset/modelnet
⏩ Сlassification example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/classification.ipynb
➡️ Segmentation example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/segmentation.ipynb
@Machine_learn
🖥Github: https://github.com/ajhamdi/mvtorch
⭐️ Paper: https://arxiv.org/abs/2212.13462v1
⏩ Dataset: https://paperswithcode.com/dataset/modelnet
⏩ Сlassification example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/classification.ipynb
➡️ Segmentation example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/segmentation.ipynb
@Machine_learn
This media is not supported in your browser
VIEW IN TELEGRAM
When you are presenting a topic in the class and make eye contact with your friends😹😹😹
@Machine_learn
@Machine_learn
⭐️ The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
🖥 Github: https://paperswithcode.com/paper/the-cropandweed-dataset-a-multi-modal
⏩ Paper: https://openaccess.thecvf.com/content/WACV2023/html/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.html
➡️ Datasets: https://paperswithcode.com/dataset/cropandweed-dataset
@Machine_learn
🖥 Github: https://paperswithcode.com/paper/the-cropandweed-dataset-a-multi-modal
⏩ Paper: https://openaccess.thecvf.com/content/WACV2023/html/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.html
➡️ Datasets: https://paperswithcode.com/dataset/cropandweed-dataset
@Machine_learn
Math-for-Programmers.pdf
27.7 MB
MEAP Edition
Manning Early Access Program
Math for Programmers
3D graphics, machine learning, and simulations with Python
Version 11
#book @Machine_learn
Manning Early Access Program
Math for Programmers
3D graphics, machine learning, and simulations with Python
Version 11
#book @Machine_learn
Build_a_Career_in_Data_Science_by_Emily_Robinson,_Jacqueline_Nolis.pdf
12.3 MB
Build a Career in Data Science
EMILY ROBINSON AND JACQUELINE NOLIS
#Data_Science
#Book
#ML
@Machine_learn
EMILY ROBINSON AND JACQUELINE NOLIS
#Data_Science
#Book
#ML
@Machine_learn
💬 GLIGEN: Open-Set Grounded Text-to-Image Generation
GLIGEN’s zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.
⭐️ Project: https://gligen.github.io/
⭐️ Demo: https://aka.ms/gligen
✅️ Paper: https://arxiv.org/abs/2301.07093
🖥 Github: https://github.com/gligen/GLIGEN
@Machine_learn
GLIGEN’s zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.
⭐️ Project: https://gligen.github.io/
⭐️ Demo: https://aka.ms/gligen
✅️ Paper: https://arxiv.org/abs/2301.07093
🖥 Github: https://github.com/gligen/GLIGEN
@Machine_learn
Apress.PyTorch.pdf
5.1 MB
PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models, 2nd Edition (2022)
#Pythorch #book #python
@Machin_learn
#Pythorch #book #python
@Machin_learn
This media is not supported in your browser
VIEW IN TELEGRAM
AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling
Autoregressive approach for modeling dynamically deforming human bodies by Meta.
🖥 Github: github.com/facebookresearch/AutoAvatar
⭐️ Project: zqbai-jeremy.github.io/autoavatar
✅️ Paprer: arxiv.org/pdf/2203.13817.pdf
⏩ Dataset: https://amass.is.tue.mpg.de/index.html
⭐️ Video: https://zqbai-jeremy.github.io/autoavatar/static/images/video_arxiv.mp4
@Machine_learn
Autoregressive approach for modeling dynamically deforming human bodies by Meta.
🖥 Github: github.com/facebookresearch/AutoAvatar
⭐️ Project: zqbai-jeremy.github.io/autoavatar
✅️ Paprer: arxiv.org/pdf/2203.13817.pdf
⏩ Dataset: https://amass.is.tue.mpg.de/index.html
⭐️ Video: https://zqbai-jeremy.github.io/autoavatar/static/images/video_arxiv.mp4
@Machine_learn
🖥 Deep BCI SW ver. 1.0 is released.
🖥 Github: https://github.com/DeepBCI/Deep-BCI
⏩ Paper: https://arxiv.org/abs/2301.08448v1
➡️ Project: http://deepbci.korea.ac.kr/
@Machine_learn
🖥 Github: https://github.com/DeepBCI/Deep-BCI
⏩ Paper: https://arxiv.org/abs/2301.08448v1
➡️ Project: http://deepbci.korea.ac.kr/
@Machine_learn
This media is not supported in your browser
VIEW IN TELEGRAM
✅️ StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
🖥 Github: github.com/autonomousvision/stylegan-t
✅️ Paper: arxiv.org/pdf/2301.09515.pdf
⭐️ Project: sites.google.com/view/stylegan-t
✔️ Video: https://www.youtube.com/watch?v=MMj8OTOUIok&embeds_euri=https%3A%2F%2Fsites.google.com%2F&feature=emb_logo
🖥 Projected GAN: https://github.com/autonomousvision/projected-gan
@Machine_learn
🖥 Github: github.com/autonomousvision/stylegan-t
✅️ Paper: arxiv.org/pdf/2301.09515.pdf
⭐️ Project: sites.google.com/view/stylegan-t
✔️ Video: https://www.youtube.com/watch?v=MMj8OTOUIok&embeds_euri=https%3A%2F%2Fsites.google.com%2F&feature=emb_logo
🖥 Projected GAN: https://github.com/autonomousvision/projected-gan
@Machine_learn