Machine-Learning-Collection
Gradient-Centralization-TensorFlow
| Machine-Learning-Collection | Gradient-Centralization-TensorFlow | |
|---|---|---|
| 9 | 5 | |
| 6,991 | 106 | |
| - | - | |
| 3.5 | 0.0 | |
| 3 months ago | about 2 years ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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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
Gradient-Centralization-TensorFlow
- How many epochs of constant loss (e.g. 0.6933) will cause you to try something else?
- A Python package to boost your ML training performance
- A Python package to sizably boost your ML performance
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[P] I made gctf: a package to sizeably improve your performance (link in comments)
GitHub repo: https://github.com/Rishit-dagli/Gradient-Centralization-TensorFlow
- A Python package to sizably increase your ML Training performance
What are some alternatives?
6DRepNet - Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.
guesslang - Detect the programming language of a source code
nodding-pigeon - Detection and classification of head gestures in videos
DemonRangerOptimizer - Quasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay
a-PyTorch-Tutorial-to-Image-Captioning - Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
EasyMSE - A Python package for generating custom Magic The Gathering cards
ALAE - [CVPR2020] Adversarial Latent Autoencoders
Im2txt - Image captioning ready-to-go inference: show and tell model compatible with Tensorflow r1.9
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
neural-network-scratch - build a neural network to show as a demonstration on inner workings of a neural network
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/
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x