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5.3 KiB
Action Classification with VideoSwinTransformer
Author: Jinling xu
Description
An action classification operator generates labels of human activities (with corresponding scores) and extracts features for the input video. It transforms the video into frames and loads pre-trained models by model names. This operator has implemented pre-trained models from VideoSwinTransformer and maps vectors with labels.
Code Example
Use the pretrained VideoSwinTransformer model ('swin_t_k400_1k') to classify and generate a vector for the given video path './archery.mp4' (download).
Write a pipeline with explicit inputs/outputs name specifications:
from towhee import pipe, ops, DataCollection
p = (
pipe.input('path')
.map('path', 'frames', ops.video_decode.ffmpeg())
.map('frames', ('labels', 'scores', 'features'),
ops.action_classification.video_swin_transformer(model_name='swin_t_k400_1k'))
.output('path', 'labels', 'scores', 'features')
)
DataCollection(p('./archery.mp4')).show()
Factory Constructor
Create the operator via the following factory method
action_classification.video_swin_transformer( model_name='swin_tiny_patch244_window877_kinetics400_1k', skip_preprocess=False, classmap=None, topk=5)
Parameters:
model_name: str
The name of pre-trained model. Supported model names:
- swin_t_k400_1k
- swin_s_k400_1k
- swin_b_k400_1k
- swin_b_k400_22k
skip_preprocess: bool
Flag to control whether to skip UniformTemporalSubsample in video transforms, defaults to False. If set to True, the step of UniformTemporalSubsample will be skipped. In this case, the user should guarantee that all the input video frames are already reprocessed properly, and thus can be fed to model directly.
classmap: Dict[str: int]:
Dictionary that maps class names to one hot vectors. If not given, the operator will load the default class map dictionary.
topk: int
The topk labels & scores to present in result. The default value is 5.
Interface
A video classification operator generates a list of class labels and a corresponding vector in numpy.ndarray given a video input data.
Parameters:
video: List[towhee.types.VideoFrame]
Input video data should be a list of towhee.types.VideoFrame representing video frames in order.
Returns:
labels, scores, features: Tuple(List[str], List[float], numpy.ndarray)
- labels: predicted class names.
- scores: possibility scores ranking from high to low corresponding to predicted labels.
- features: a video embedding in shape of (768,) representing features extracted by model.
More Resources
- Understanding Class Activation Mapping (CAM) in Deep Learning - Zilliz blog: Class Activation Mapping (CAM) is used to visualize and understand the decision-making of convolutional neural networks (CNNs) for computer vision tasks.
- Vector Database Use Cases: Video Similarity Search - Zilliz: Experience a 10x performance boost and unparalleled precision when your video similarity search system is powered by Zilliz Cloud.
- How to Get the Right Vector Embeddings - Zilliz blog: A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
- Building a Video Analysis System with Milvus Vector Database - Zilliz blog: Learn how Milvus powers the AI analysis of video content.
- 4 Steps to Building a Video Search System - Zilliz blog: Searching for videos by image with Milvus
- Understanding ImageNet: A Key Resource for Computer Vision and AI Research: The large-scale image database with over 14 million annotated images. Learn how this dataset supports advancements in computer vision.
- Everything You Need to Know About Zero Shot Learning - Zilliz blog: A comprehensive guide to Zero-Shot Learning, covering its methodologies, its relations with similarity search, and popular Zero-Shot Classification Models.
- What is a Generative Adversarial Network? An Easy Guide: Just like we classify animal fossils into domains, kingdoms, and phyla, we classify AI networks, too. At the highest level, we classify AI networks as "discriminative" and "generative." A generative neural network is an AI that creates something new. This differs from a discriminative network, which classifies something that already exists into particular buckets. Kind of like we're doing right now, by bucketing generative adversarial networks (GANs) into appropriate classifications. So, if you were in a situation where you wanted to use textual tags to create a new visual image, like with Midjourney, you'd use a generative network. However, if you had a giant pile of data that you needed to classify and tag, you'd use a discriminative model.
5.3 KiB
Action Classification with VideoSwinTransformer
Author: Jinling xu
Description
An action classification operator generates labels of human activities (with corresponding scores) and extracts features for the input video. It transforms the video into frames and loads pre-trained models by model names. This operator has implemented pre-trained models from VideoSwinTransformer and maps vectors with labels.
Code Example
Use the pretrained VideoSwinTransformer model ('swin_t_k400_1k') to classify and generate a vector for the given video path './archery.mp4' (download).
Write a pipeline with explicit inputs/outputs name specifications:
from towhee import pipe, ops, DataCollection
p = (
pipe.input('path')
.map('path', 'frames', ops.video_decode.ffmpeg())
.map('frames', ('labels', 'scores', 'features'),
ops.action_classification.video_swin_transformer(model_name='swin_t_k400_1k'))
.output('path', 'labels', 'scores', 'features')
)
DataCollection(p('./archery.mp4')).show()
Factory Constructor
Create the operator via the following factory method
action_classification.video_swin_transformer( model_name='swin_tiny_patch244_window877_kinetics400_1k', skip_preprocess=False, classmap=None, topk=5)
Parameters:
model_name: str
The name of pre-trained model. Supported model names:
- swin_t_k400_1k
- swin_s_k400_1k
- swin_b_k400_1k
- swin_b_k400_22k
skip_preprocess: bool
Flag to control whether to skip UniformTemporalSubsample in video transforms, defaults to False. If set to True, the step of UniformTemporalSubsample will be skipped. In this case, the user should guarantee that all the input video frames are already reprocessed properly, and thus can be fed to model directly.
classmap: Dict[str: int]:
Dictionary that maps class names to one hot vectors. If not given, the operator will load the default class map dictionary.
topk: int
The topk labels & scores to present in result. The default value is 5.
Interface
A video classification operator generates a list of class labels and a corresponding vector in numpy.ndarray given a video input data.
Parameters:
video: List[towhee.types.VideoFrame]
Input video data should be a list of towhee.types.VideoFrame representing video frames in order.
Returns:
labels, scores, features: Tuple(List[str], List[float], numpy.ndarray)
- labels: predicted class names.
- scores: possibility scores ranking from high to low corresponding to predicted labels.
- features: a video embedding in shape of (768,) representing features extracted by model.
More Resources
- Understanding Class Activation Mapping (CAM) in Deep Learning - Zilliz blog: Class Activation Mapping (CAM) is used to visualize and understand the decision-making of convolutional neural networks (CNNs) for computer vision tasks.
- Vector Database Use Cases: Video Similarity Search - Zilliz: Experience a 10x performance boost and unparalleled precision when your video similarity search system is powered by Zilliz Cloud.
- How to Get the Right Vector Embeddings - Zilliz blog: A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
- Building a Video Analysis System with Milvus Vector Database - Zilliz blog: Learn how Milvus powers the AI analysis of video content.
- 4 Steps to Building a Video Search System - Zilliz blog: Searching for videos by image with Milvus
- Understanding ImageNet: A Key Resource for Computer Vision and AI Research: The large-scale image database with over 14 million annotated images. Learn how this dataset supports advancements in computer vision.
- Everything You Need to Know About Zero Shot Learning - Zilliz blog: A comprehensive guide to Zero-Shot Learning, covering its methodologies, its relations with similarity search, and popular Zero-Shot Classification Models.
- What is a Generative Adversarial Network? An Easy Guide: Just like we classify animal fossils into domains, kingdoms, and phyla, we classify AI networks, too. At the highest level, we classify AI networks as "discriminative" and "generative." A generative neural network is an AI that creates something new. This differs from a discriminative network, which classifies something that already exists into particular buckets. Kind of like we're doing right now, by bucketing generative adversarial networks (GANs) into appropriate classifications. So, if you were in a situation where you wanted to use textual tags to create a new visual image, like with Midjourney, you'd use a generative network. However, if you had a giant pile of data that you needed to classify and tag, you'd use a discriminative model.