xclip base patch32

提供商microsoft
分类video-classification
许可证mit
下载量363
星标0

简介

xclip base patch32 是由微软推出的轻量级视频-语言预训练模型，旨在解决视频内容理解中的跨模态对齐问题。与传统的图像 CLIP 不同，它能捕捉视频中的时空动态特征，使机器能够通过文本描述检索视频片段或对视频内容进行分类。对于开发者而言，该模型在保持较低计算开销的同时，提供了不错的零样本（Zero-shot）迁移能力，非常适合用于短视频标签自动生成、视频内容审核等实际场景。它与 OpenAI 的 CLIP 逻辑相似，但将感知维度从静态图像扩展到了动态视频。

核心亮点

  • 支持视频与文本的跨模态对齐与语义检索
  • 具备强大的 Zero-shot 视频分类能力
  • 轻量化设计，推理速度快且部署成本低
  • 适用于短视频分析、自动打标等工业场景

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/xclip-base-patch32")
tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch32")

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/xclip-base-patch32

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/xclip-base-patch32 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/xclip-base-patch32')

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/xclip-base-patch32

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/xclip-base-patch32

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/xclip-base-patch32')
tokenizer = AutoTokenizer.from_pretrained('microsoft/xclip-base-patch32')

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/xclip-base-patch32

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/xclip-base-patch32 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/xclip-base-patch32')

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/xclip-base-patch32.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/xclip-base-patch32.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/xclip-base-patch32')

完整文档

来源: HuggingFace

---
language: en
license: mit
tags:

  • vision

  • video-classification

model-index:
  • name: nielsr/xclip-base-patch32

results:
- task:
type: video-classification
dataset:
name: Kinetics 400
type: kinetics-400
metrics:
- type: top-1 accuracy
value: 80.4
- type: top-5 accuracy
value: 95.0
---

X-CLIP (base-sized model)

X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on Kinetics-400. It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository.

This model was trained using 8 frames per video, at a resolution of 224x224.

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

Model description

X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.

!X-CLIP architecture

This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.

Intended uses & limitations

You can use the raw model for determining how well text goes with a given video. See the model hub to look for
fine-tuned versions on a task that interests you.

How to use

For code examples, we refer to the documentation.

Training data

This model was trained on Kinetics-400.

Preprocessing

The exact details of preprocessing during training can be found here.

The exact details of preprocessing during validation can be found here.

During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.

Evaluation results

This model achieves a top-1 accuracy of 80.4% and a top-5 accuracy of 95.0%.

查看官方来源