Flux Model Zoo
This repository contains various demonstrations of the Flux machine learning library. Any of these may freely be used as a starting point for your own models.
The models are broadly categorised into the folders vision (e.g. large convolutional neural networks (CNNs)), text (e.g. various recurrent neural networks (RNNs) and natural language processing (NLP) models), games (Reinforcement Learning / RL). See the READMEs of respective models for more information.
Usage
The zoo comes with its own Julia project, which lists the packages you need to run the models. You can run the models by opening Julia in the project folder and running
using Pkg; Pkg.activate("."); Pkg.instantiate()
to install all needed packages. Then you can run the model code with include("<model-to-run>.jl") or by running the model script line-by-line.
Models may also be run with NVIDIA GPU support, if you have a CUDA installed. Most models will have this capability by default, pointed at by calls to gpu in the model code.
Gitpod Online IDE
Each model can be used in Gitpod, just open the repository by gitpod
- Based on Gitpod's policies, free access is limited.
- All of your work will place in the Gitpod's cloud.
- It isn't an officially maintained feature.
Contributing
We welcome contributions of new models. They should be in a folder with a project and manifest file, to pin all relevant packages, as well as a README to explain what the model is about, how to run it, and what results it achieves (if applicable). If possible models should not depend directly on GPU functionality, but ideally should be CPU/GPU agnostic. Please keep the code short, clean and self-explanatory, with as little boilerplate code as possible.
Examples Listing
- Vision
- Text
- Other & contributed models
- Tutorials
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平时加载预训练模型的时候，使用pytorch自带的model_zoo方法加载url地址中的模型，具体的api如下： torch.utils.model_zoo.load_url(url, model_dir=None) 作用：在给定URL上加载torch序列化对象。 具体来讲就是通过提供的.pth文件的url地址来下载指定的.pth文件。 参数： url (string) - 要下载对象的URL
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参考 torch.utils.model_zoo - 云+社区 - 腾讯云 torch.utils.model_zoo.load_url(url, model_dir=None) 在给定URL上加载Torch序列化对象。 如果对象已经存在于 model_dir 中，则将被反序列化并返回。URL的文件名部分应遵循命名约定filename-<sha256>.ext，其中<sha256>是文件内
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作者|open-mmlab 编译|Flin 来源|Github 基准测试 和 Model Zoo 环境 硬件 8 个 NVIDIA Tesla V100 GPUs Intel Xeon 4114 CPU @ 2.20GHz 软件环境 Python 3.6 / 3.7 PyTorch 1.1 CUDA 9.0.176 CUDNN 7.0.4 NCCL 2.1.15 镜像站点 我们使用AWS作为托管m
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dgl.model_zoo Chemistry 实用工具 chem.load_pretrained（* args，** kwargs） 性能预测 当前支持的模型架构： GCN分类器 GAT分类器 神经网络 网络 MGCN AttentiveFP classdgl.model_zoo.chem.GCNClassifier(** kwargs) 基于GCN的分子图多任务预测的预测器，我们假设每个
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博客新址: http://blog.xuezhisd.top 邮箱：xuezhisd@126.com MXNet的模型园地 MXNet 突出了学术论文中报告的最先进模型的快速实现。我们的模型园地（Modle Zoo）包含了完整的模型，Python脚本，预训练的权重和如何进行微调的说明文档。 如何贡献一个预训练的模型 (应包含什么) 提交一个包含下列内容的 Pull 请求： Gist 日志 .jso
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描述 (Description) 要指定模型类，我们需要覆盖集合类的model属性。 语法 (Syntax) Backbone.Collection.model 例子 (Example) <!DOCTYPE html> <html> <head> <title>Collection Example</title> <script src = "https://cod
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模型包含动态数据及其逻辑。 转换，验证，计算属性和访问控制等逻辑属于“模型”类别。 由于它包含所有应用程序数据，因此模型也被称为heart of JavaScript application的heart of JavaScript application 。 下表列出了可用于操作BackboneJS模型的所有方法 - S.No. 方法和描述 1 extend 它扩展了backbone.Model类
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以下是模型的子类型 - iges vnd.gdl vnd.parasolid.transmit.binary mesh vnd.gs-gdl vnd.parasolid.transmit.text vnd.dwf vnd.gtw vnd.vtu vnd.flatland.3dml vnd.mts vrml
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Data Model Inference Criticism Compositionality Development Model A probabilistic model is a joint distribution (p(\mathbf{x}, \mathbf{z})) of data $(\mathbf{x})$ and latent variables $(\mathbf{z})$.
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基础使用 // 定义一个 Eloquent 模型 class User extends Model {} // 生成一个 Eloquent 模型 php artisan make:model User // 指定一个自定义的数据表名称 class User extends Model { protected $table = 'my_users'; } More Model
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设备类型 Fieldsid (ID!) 唯一 IDname (String!) 名字type (String!) 类型，如 灯skillId (String!) vendorName (String) vendorId (ID) url (String) imageUrl (String)
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本文向大家介绍chain model相关面试题，主要包含被问及chain model时的应答技巧和注意事项，需要的朋友参考一下 https://blog.csdn.net/u013569304/article/details/81290281 chain model是DNN-HMM模型的一种，使用nnet3结构 使神经网络的输出的帧率缩小三倍，明显的缩小了测试时的计算量，使实时解码更加容易 模型从
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The obj-model component loads a 3D model and material using a Wavefront(.OBJ) file and a .MTL file. Example We can load an .OBJ model by pointing to assets that specify the path to an .OBJ and .MTL fi