6DRepNet
Machine-Learning-Collection
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6DRepNet | Machine-Learning-Collection | |
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4 | 9 | |
499 | 6,936 | |
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3.7 | 3.5 | |
about 1 month ago | 3 months ago | |
Python | Python | |
MIT License | MIT License |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
6DRepNet
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How to do Human Head Segmentation from images?
Well so it sounds like you want to do something like DeepFaceLab? I don't have a lot of experience with that but I believe they collect facial images based on their angles (pitch, roll and yaw) and match and merge them together. So something like this might help you there: https://github.com/thohemp/6DRepNet
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Head Pose Estimation
I found this was the easiest one to get installed and up and running: https://github.com/thohemp/6DRepNet
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[R] 6D Rotation Representation For Unconstrained Head Pose
Code for https://arxiv.org/abs/2202.12555 found: https://github.com/thohemp/6DRepNet
Machine-Learning-Collection
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Building an AI Game Bot 🤖Using Imitation Learning and 3D Convolution ResNet
def compute_mean_std(dataloader): ''' We assume that the images of the dataloader have the same height and width source: https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_std_mean.py ''' # var[X] = E[X**2] - E[X]**2 channels_sum, channels_sqrd_sum, num_batches = 0, 0, 0 for batch_images, labels in tqdm(dataloader): # (B,H,W,C) batch_images = batch_images.permute(0,3,4,2,1) channels_sum += torch.mean(batch_images, dim=[0, 1, 2, 3]) channels_sqrd_sum += torch.mean(batch_images ** 2, dim=[0, 1, 2,3]) num_batches += 1 mean = channels_sum / num_batches std = (channels_sqrd_sum / num_batches - mean ** 2) ** 0.5 return mean, std compute_mean_std(dataloader)
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What can be the reasons of BatchNorm working and Dropout not working in YoloV1 Pytorch implementation?
I then found Aladdin Persson implementation (which he described in YouTube video). He said that original paper used Dropout, because BatchNorm was not invented at the time, and he wants to use BatchNorm instead. I thought there is no critical difference between these two, and decided to stick up with paper for the sake of learning to implement such things.
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How to create a custom parallel corpus for machine translation with recent versions of pytorch and torchtext?
I am trying to train a model for NMT on a custom dataset. I found this great tutorial on youtube along with the accompanying repo, but it uses an old version of PyTorch and torchtext. More recent versions of torchtext have removed the Field and BucketIterator classes. I looked for more recent tutorials. The closest thing I could find was this medium post (again with the accompanying code) which worked with a custom dataset for text classification. I tried to replicate the code with my problem and got this far:
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I need help knowing how to improve a CycleGan I am working on.
with the source code here: Machine-Learning-Collection/ML/Pytorch/GANs/CycleGAN at master · aladdinpersson/Machine-Learning-Collection · GitHub
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Pytorch: Custom Dataset for Machine Translation
seq2seq_attention
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Project to prettify music notes
I followed a tutorial to do a pix2pix GAN network here: https://www.youtube.com/watch?v=SuddDSqGRzg, and the github.
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Awesome Youtube
Aladdin Persson
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Help with initial set-up.
Hello everyone. I am all set up and ready to go. I downloaded the code below (btw, I don't fully understand what it does, lol) and ran it a few times.
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YOLOv3 from scratch in PyTorch
Code: https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/object_detection/YOLOv3
What are some alternatives?
pytorch-geodesic-loss - A PyTorch criterion for computing the distance between rotation matrices.
nodding-pigeon - Detection and classification of head gestures in videos
PyTorch-VAE - A Collection of Variational Autoencoders (VAE) in PyTorch.
a-PyTorch-Tutorial-to-Image-Captioning - Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
ALAE - [CVPR2020] Adversarial Latent Autoencoders
DeepPoseKit - a toolkit for pose estimation using deep learning
Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control - Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments
PoseNDF - Implementation of Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields
Gradient-Centralization-TensorFlow - Instantly improve your training performance of TensorFlow models with just 2 lines of code!
RSCalibration - Docs and scripts to estimate a camera's rolling shutter readout time
pytorch-accelerated - A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases, and capable of utilizing different hardware options with no code changes required. Docs: https://pytorch-accelerated.readthedocs.io/en/latest/