Many tasks have a pre-trainedpipelineready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image: >>>importrequests>>>fromPILimportImage>>>fromtransformersimportpipeline# Download an image with cute cats>>>url="https://huggingf...
A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using ...
For an overview of the ecosystem of HuggingFace for computer vision (June 2022), refer tothis notebookwith correspondingvideo. Currently, it contains the following demos: ... more to come! 🤗 If you have any questions regarding these demos, feel free to open an issue on this repository. ...
pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为音频(Audio)、计算机视觉(Computer vision)、自然语言处理(NLP)、多模态(Multimodal)等4大类,28小类任务(tasks),共计覆盖32万个模型。 今天介绍Audio音频的第二篇,自动语音识别(automatic-speech-recognition),在huggingface...
DGFormer: A Dynamic Kernel withGaussian Fusion Transformer forSemantic Image Segmentation Despite the significant recent success of Vision Transformers in computer vision, they struggle with dense prediction tasks due to their limited capability... H Yang,L Tang,T Wu,... - International Conference on...
ALBERT(来自 Google Research and the Toyota Technological Institute at Chicago) 伴随论文ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, 由 Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。
Vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. ...
pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为语音(Audio)、计算机视觉(Computer vision)、自然语言处理(NLP)、多模态(Multimodal)等4大类,28小类任务(tasks)。共计覆盖32万个模型 本文对pipeline进行整体介绍,之后本专栏以每个task为主题,分别介绍各种task使用方法。
This is why CNNs and Transformers are tailored for different types of data and tasks. CNNs dominate in the field of computer vision due to their efficiency in processing spatial information, while Transformers are the go-to choice for complex sequential tasks, especially in NLP, due to their ...
from_pretrained("facebook/wav2vec2-base") audio_input = [dataset[0]["audio"]["array"]] feature_extractor(audio_input, sampling_rate=16000) # 创建一个函数来预处理数据集,使音频样本的长度相同。指定最大样本长度,特征提取器将填充或截断序列以匹配它: def preprocess_function(examples): audio_...