examples
pytorch-image-models
examples | pytorch-image-models | |
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23 | 35 | |
21,727 | 29,828 | |
0.6% | 1.2% | |
7.7 | 9.4 | |
11 days ago | 2 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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examples
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A Distributed File System in Go Cut Average Metadata Memory Usage to 100 Bytes
For “cloud-native” apps, JuiceFS is not needed.
S3 is not designed for intensive metadata operations, like listing, renaming etc. For these operations, you will need a somewhat POSIX-complaint system. For example, if you want to train on ImageNet dataset, the “canonical” way [1] is to extract the images and organize them into folders, class by class. The whole dataset is discovered by directory listing. This where JuiceFS shines.
Of course, if the dataset is really massive, you will mostly end-up with in-house solutions.
[1]: https://github.com/pytorch/examples/blob/main/imagenet/extra...
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Logistic Regression for Image Classification Using OpenCV
Pytorch includes a simple neural network example for the MNIST data: https://github.com/pytorch/examples/blob/main/mnist/main.py
It only takes a few minutes to train with default parameters and will have >99% accuracy on the MNIST test set.
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[R] Nvidia RTX 4090 ML benchmarks. Under QEMU/KVM. Image + Transformers. FP16/FP32.
pytorch-examples
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I work at a non-tech company and have been asked to make software that is impossible. How do I explain it to my boss?
Pretty much just grab one of these, swap in your own database, go home early: https://pytorch.org/examples/
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MIT Course: Generative AI for Constructive Communication
[5] https://github.com/pytorch/examples/tree/main/word_language_...
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From a Dumb Student to a PyTorch Contributor: The Impact of Teachers on My Life⚡
The cherry on top of the cake I've added my father's name at the top of the code in the comments. I hope that for the next upcoming 200-300 years, someone will read modify and improve or perform experiments with my code.(Vivek V patel), My code can be found at official PyTorch's Website https://pytorch.org/examples/(Image Classification Using Forward-Forward Algorithm)
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What modifications can maximize the efficacy of the REINFORCE algorithm for a policy gradient task?
I am straying out of my domain knowledge to attempt a basic reinforcement learning task in a toy environment and have become fairly familiar with the REINFORCE algorithm for policy gradient agents, especially PyTorch’s implementation (found here). It is clear to me now that there are superior methods to train RL agents (PPO for instance), but as I read, these feel beyond my current intellectual or time resources. As such, I’d like to eek out as much power through modifications of REINFORCE as possible before determining how I might move on.
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How does Taichi differ from PyTorch? They are different in every sense!
import torch import torch.nn as nn import torch.nn.functional as F # Simplified version of https://github.com/pytorch/examples/blob/main/mnist/main.py#L21 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) def forward(self, x): x = self.conv1(x) output = F.relu(x) return output
- Noob PyTorch Question
- Syntax Error, attempting to train neural network.
pytorch-image-models
- FLaNK AI Weekly 18 March 2024
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[D] Hugging face and Timm
I am a PyTorch user I work in CV, I usually use the PyTorch models. However, I see people use timm in research papers to train their models I don't understand what it is timm is it a new framework like PyTorch? Further, when I click https://pypi.org/project/timm/ homepage it takes me to hugging face GitHub https://github.com/huggingface/pytorch-image-models is there any connection between timm and hugging face many of my friends use hugging face but I also don't know about hugging face I use simple PyTorch and torchvision.models.
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FLaNK Stack Weekly for 07August2023
https://github.com/huggingface/pytorch-image-models https://huggingface.co/docs/timm/index
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[R] Nvidia RTX 4090 ML benchmarks. Under QEMU/KVM. Image + Transformers. FP16/FP32.
pytorch-image-models
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Inference on resent, cant work out the problem?
additionally, you might find the timm library handy for this sort of work.
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Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows
This is still being pursued. Ross Wightmann's timm[0,1] package (now on Hugging Face) has done a lot of this. There's also a V2 of ConvNext[2]. Ross does write about this a lot on Twitter fwiw. I should also mention that there are still many transformer based networks that still beat convs. So there probably won't be a resurgence in convs until someone can show that there's a really strong reason for them. They have some advantages but they also might not be flexible enough for the long range tasks in segmentation and detection. But maybe they are.
FAIR definitely did great work with ConvNext, and I do hope to see more. There always needs to be people pushing unpopular paradigms.
[0] https://github.com/huggingface/pytorch-image-models
[1] https://arxiv.org/abs/2110.00476
[2] https://arxiv.org/abs/2301.00808
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Problems with Learning Rate Finder in Pytorch Lightning
I am doing Binary classification with a pre-trained EfficientNet tf_efficientnet_l2. I froze all weights during training and replaced the classifier with a custom trainable one that looks like:
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PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter
In this post, I’m going to show you how you can pick from over 900+ SOTA models on TIMM, train them using best practices with Fastai, and deploy them on Android using Flutter.
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ImageNet Advise
The other thing is, try to find tricks to speed up your experiments (if not having done so already). The most obvious are mixed precision training, have your model train on a lower resolution input first and then increase the resolution later in the training, stochastic depth, and a bunch more stuffs. Look for implementations in https://github.com/rwightman/pytorch-image-models .
- Doubt about transformers
What are some alternatives?
self-driving-car - The Udacity open source self-driving car project
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
aws-graviton-getting-started - Helping developers to use AWS Graviton2 and Graviton3 processors which power the 6th and 7th generation of Amazon EC2 instances (C6g[d], M6g[d], R6g[d], T4g, X2gd, C6gn, I4g, Im4gn, Is4gen, G5g, C7g[d][n], M7g[d], R7g[d]).
mmdetection - OpenMMLab Detection Toolbox and Benchmark
fast-style-transfer - TensorFlow CNN for fast style transfer ⚡🖥🎨🖼
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
pytea - PyTea: PyTorch Tensor shape error analyzer
mmcv - OpenMMLab Computer Vision Foundation
PyTorchProjectFramework - A basic framework for your PyTorch projects
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
raccoon_dataset - The dataset is used to train my own raccoon detector and I blogged about it on Medium
yolact - A simple, fully convolutional model for real-time instance segmentation.