YouTube video by mariusz c.

Code for "How far can we go with ImageNet for Text-to-Image generation?" paper - lucasdegeorge/T2I-ImageNet

YouTube video by mariusz c.

YouTube video by Johnny Game Over

Solution to the SETI Breakthrough Listen Competition hosted on Kaggle - tanishqgautam/SETI-Breakthrough-Listen

데이터 증강 완벽 가이드! 과적합 방지와 모델 성능 향상의 핵심 기법. 이미지 증강: Mixup vs CutMix vs AugMix 비교, AutoAugment vs RandAugment 성능/비용. NLP 증강: Back Translation, EDA 4가지 연산, LLM 활용 최신 기법. GAN 기반 증강으로 민감도 10% 향상!

On this tutorial, we discover superior laptop imaginative and prescient methods utilizing TorchVision’s v2 transforms, trendy augmentation methods, and highly effective coaching enhancements. We stroll by the method of constructing an augmentation pipeline, making use of MixUp and CutMix, designing a contemporary CNN with consideration, and implementing a sturdy coaching loop. By working all the pieces seamlessly in Google Colab, we place ourselves to know and apply state-of-the-art practices in deep studying with readability and effectivity. Try the FULL CODES right here. !pip set up torch torchvision torchaudio --quiet !pip set up matplotlib pillow numpy --quiet import torch import torchvision from torchvision import transforms as T from torchvision.transforms import v2 import torch.nn as nn import torch.optim as optim from torch.utils.information import DataLoader import matplotlib.pyplot as plt import numpy as np from PIL import Picture import requests from io import BytesIO print(f"PyTorch model: {torch.__version__}") print(f"TorchVision model: {torchvision.__version__}")

In this tutorial, we explore advanced computer vision techniques using TorchVision’s v2 transforms, modern augmentation strategies, and powerful training enhancements. We walk through the process of building an augmentation pipeline, applying MixUp and CutMix, designing a modern CNN with attention, and implementing a robust training loop. By running everything seamlessly in Google Colab, we position ourselves to understand and apply state-of-the-art practices in deep learning with clarity and efficiency.

In this tutorial, we explore advanced computer vision techniques using TorchVision’s v2 transforms, modern augmentation strategies, and powerful training enhancements. We walk through the process of building an augmentation pipeline, applying MixUp and CutMix, designing a modern CNN with attention, and implementing a robust training loop. By running everything seamlessly in Google Colab, we position ourselves to understand and apply state-of-the-art practices in deep learning with clarity and efficiency.

I have spent years building deep learning models, and if there is one thing I’ve learned, it is that your model is only as good as your data. When I first started with Keras, I relied on simple flips and rotations, but my models often struggled with complex, real-world images. That all changed when I ... Read more >>

On this tutorial, we discover superior laptop imaginative and prescient methods utilizing TorchVision’s v2 transforms, trendy augmentation methods, and highly effective coaching enhancements. We stroll by the method of constructing an augmentation pipeline, making use of MixUp and CutMix, designing a contemporary CNN with consideration, and implementing a sturdy coaching loop. By working all the pieces seamlessly in Google Colab, we place ourselves to know and apply state-of-the-art practices in deep studying with readability and effectivity. Try the FULL CODES right here. !pip set up torch torchvision torchaudio --quiet !pip set up matplotlib pillow numpy --quiet import torch import torchvision from torchvision import transforms as T from torchvision.transforms import v2 import torch.nn as nn import torch.optim as optim from torch.utils.information import DataLoader import matplotlib.pyplot as plt import numpy as np from PIL import Picture import requests from io import BytesIO print(f"PyTorch model: {torch.__version__}") print(f"TorchVision model: {torchvision.__version__}")