resnet 50

提供商microsoft
分类image-classification
许可证apache-2.0
下载量969
星标4

简介

ResNet-50 是计算机视觉领域的经典里程碑，由微软提出。它通过引入“残差连接”解决了深层网络难以训练的梯度消失问题，使得模型在增加深度提升精度时依然能保持高效收敛。对于中国开发者而言，它不仅是学习深度学习的必经之路，更是工业界最常用的图像分类基准模型。无论是作为端到端分类器，还是作为目标检测（如 Faster R-CNN）和语义分割的特征提取骨干网络（Backbone），ResNet-50 都在性能与计算开销之间取得了极佳的平衡，上手门槛低且生态极其成熟。

核心亮点

  • 经典残差结构，有效解决深层网络梯度消失
  • 工业级图像分类基准，模型鲁棒性极强
  • 广泛作为下游视觉任务的特征提取骨干网络
  • 开源生态丰富，主流深度学习框架均原生支持

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("microsoft/resnet-50")
tokenizer = AutoTokenizer.from_pretrained("microsoft/resnet-50")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download microsoft/resnet-50

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download microsoft/resnet-50 config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('microsoft/resnet-50')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/microsoft/resnet-50

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/microsoft/resnet-50

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('microsoft/resnet-50')
tokenizer = AutoTokenizer.from_pretrained('microsoft/resnet-50')

模型下载

我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 ModelScope：

操作指引 
pip install modelscope

命令行下载

下载完整模型库

下载完整模型库 
modelscope download --model microsoft/resnet-50

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例） 
modelscope download --model microsoft/resnet-50 README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

SDK 下载 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('microsoft/resnet-50')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://www.modelscope.cn/microsoft/resnet-50.git

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/microsoft/resnet-50.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook 快速开发

下载并安装 ModelScope library

下载并安装 ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

模型加载和推理

模型加载和推理 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'microsoft/resnet-50')

完整文档

来源: HuggingFace

---
license: apache-2.0
tags:

  • vision

  • image-classification

datasets:
  • imagenet-1k

---

ResNet-50 v1.5

ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al.

Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.

This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to Nvidia.

!model image

Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.

How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

python
from transformers import AutoImageProcessor, ResNetForImageClassification
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]


processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")


inputs = processor(image, return_tensors="pt")


with torch.no_grad():
    logits = model(**inputs).logits


model predicts one of the 1000 ImageNet classes

predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])

For more code examples, we refer to the documentation.

BibTeX entry and citation info

bibtex
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
查看官方来源