Flops counter for convolutional networks in pytorch framework
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Updated
Sep 27, 2024 - Python
Flops counter for convolutional networks in pytorch framework
PyTorch 官方中文教程包含 60 分钟快速入门教程,强化教程,计算机视觉,自然语言处理,生成对抗网络,强化学习。欢迎 Star,Fork!
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.
✨ 首个CJK(中日韩)字体识别以及样式提取模型 YuzuMarker的字体识别模型与实现 / First-ever CJK (Chinese Japanese Korean) Font Recognition and Style Extractor, side project of YuzuMarker
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).
CNN | Keras/PyTorch | CAPTCHA recognition(卷积神经网络、Keras/PyTorch框架、验证码识别)
Training and evaluating state-of-the-art deep learning CNN architectures for plant disease classification task.
Manage your machine learning experiments with trixi - modular, reproducible, high fashion. An experiment infrastructure optimized for PyTorch, but flexible enough to work for your framework and your tastes.
A Complete and Simple Implementation of MobileNet-V2 in PyTorch
PyTorch Implementation Of WS-DAN(See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification)
Pytorch implementation of "An intriguing failing of convolutional neural networks and the CoordConv solution" - https://arxiv.org/abs/1807.03247
PyTorch tutorial for learners
Metrics for model evaluation in pytorch
Coarse-to-Fine CNN for Image Super-Resolution (IEEE Transactions on Multimedia,2021)
[EXPERIMENTAL] Demo of using PyTorch 1.0 inside an Android app. Test with your own deep neural network such as ResNet18/SqueezeNet/MobileNet v2 and a phone camera.
Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.
CNN-based model to realize aspect extraction of restaurant reviews based on pre-trained word embeddings and part-of-speech tagging
使用pytorch搭建cnn识别验证码
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