PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results. The work...
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more - taylor1355/pytorch-image-models
⽬前pytorch-image-models整合的模型⼤类如下：ResNet/ResNeXt DenseNet Squeeze-and-Excitation ResNet/ResNeXt Inception-ResNet-V2 and Inception-V4 Xeption PNasNet and NASNet-A DPN Generic EfficientNet 具体模型列表可以参考：https://github.com/rwightman/pytorch-image-models#models ⽬前pytorch-image-...
链接: https://github.com/rwightman/pytorch-image-models
github 图像训练 pytorch GoogLeNet是在2014年由Google团队提出的,获得了当年ImageNet比赛中分类任务的第一名,也就是和VGG是同一年提出的,在ImageNet比赛中都获得了很好的成绩。GoogLeNet的网络结构比较复杂,具体的结构可以参考原论文,论文名字是:Going Deeper with Convolutions。
models import resnet50from vit_pytorch.distill import DistillableViT, DistillWrapper teacher = resnet50(pretrained = True) v = DistillableViT( image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 8, mlp_dim = 2048, dropout = 0.1, emb_dropout =...
import torchfrom torchvision.models import resnet50from vit_pytorch.distill import DistillableViT, DistillWrapperteacher = resnet50(pretrained = True)v = DistillableViT( image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 8, mlp_...
model=models.resnet18(pretrained=True)model.eval() 1. 2. 3. 4. 5. 如果你希望将模型集成到 Java 中,可以参考以下代码: // 使用 Java 调用 PyTorch 模型的伪代码importorg.pytorch.*;classModelRunner{publicstaticvoidmain(String[]args){Modulemodel=Module.load("model.pt");// 加载输入数据Tensorinpu...
首先来看 Vision Transformer-PyTorch 的安装、使用、参数、蒸馏等步骤。 第一步是安装: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 $ pip install vit-pytorch 第二步是使用: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import torch from vit_pytorch import ViT v = ViT( image_size = ...
项目地址：https://github.com/pytorch/text 2.pytorch-seq2seq：在 PyTorch 中实现序列到序列（seq2seq）模型的框架。项目地址：https://github.com/IBM/pytorch-seq2seq 3.anuvada：PyTorch 中实现的 NLP 的可解释模型。项目地址：https://github.com/Sandeep42/anuvada 4.audio：PyTorch 的简单音频 I/O。...