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readme md timm efficientformerv2 s2 snap dist in1k at main

A EfficientFormer-V2 image classification model. Pretrained with distillation on ImageNet-1k. There was an error while loading. Please reload this page. Efficientformerv2 s2.snap dist in1k is an image classification model that's designed to be efficient and fast. With 12.7 million parameters, it's relatively lightweight, and its performance is on par with other models in its class.

But what really sets it apart is its speed - it can process images quickly and accurately, making it a great choice for applications where speed is crucial. Plus, it's been pre-trained on ImageNet-1k, which means it's already learned to recognize a wide range of objects and features. Whether you're building a computer vision system or just need a reliable image classifier, Efficientformerv2 s2.snap dist in1k is definitely worth considering. Meet the EfficientFormer-V2 image classification model! This model is designed to be fast and efficient, while still delivering great results. The EfficientFormer-V2 model is a powerful tool for image classification and feature extraction.

It’s designed to be efficient and fast, making it perfect for mobile devices and applications where speed is crucial. The model is trained on the ImageNet-1k dataset and can classify images into one of the 1000 classes. But what does that mean for you? Imagine you’re building an app that needs to identify objects in images. With the EfficientFormer-V2, you can do just that. For example, if you upload a picture of a cat, the model can tell you that it’s a cat with high accuracy.

But the model can do more than just classify images. It can also extract features from images, which can be used for other tasks like object detection, segmentation, and more. Think of it like this: the model is not just looking at the image as a whole, but it’s also looking at the individual parts that make up the image. This can be really useful for tasks like image search, where you need to find similar images. There was an error while loading. Please reload this page.

There was an error while loading. Please reload this page. A EfficientFormer-V2 image classification model. Pretrained with distillation on ImageNet-1k. Models are trained on ImageNet-1K and deployed on iPhone 12 with CoreMLTools to get latency. Rethinking Vision Transformers for MobileNet Size and Speed Yanyu Li1,2, Ju Hu1, Yang Wen1, Georgios Evangelidis1, Kamyar Salahi3, Yanzhi Wang2, Sergey Tulyakov1, Jian Ren1 1Snap Inc., 2Northeastern University, 3UC Berkeley

Models are trained on ImageNet-1K and measured by iPhone 12 with CoreMLTools to get latency. EfficientFormer: Vision Transformers at MobileNet Speed Yanyu Li1,2, Genge Yuan1,2, Yang Wen1, Eric Hu1, Georgios Evangelidis1, Sergey Tulyakov1, Yanzhi Wang2, Jian Ren1 1Snap Inc., 2Northeastern University The latency reported in EffcientFormerV2 for iPhone 12 (iOS 16) uses the benchmark tool from XCode 14. The EfficientFormer-V2 image classification model is designed to be efficient and fast. With 26.3 million parameters and 2.6 GMACs, it's smaller and quicker than other models. It's been pre-trained with distillation on ImageNet-1k, which helps it learn from a large dataset.

This model is great for image classification tasks and can even be used for feature extraction and image embeddings. But what really sets it apart is its ability to balance speed and accuracy, making it a great choice for real-world applications. So, if you need a model that can quickly and accurately classify images, the EfficientFormer-V2 is definitely worth considering. The EfficientFormer-V2 model is a type of image classification model that’s designed to be efficient and fast. It’s been trained on a huge dataset called ImageNet-1k, which contains over a million images. This model can classify images into different categories with high accuracy.

It’s been trained on the ImageNet-1k dataset, which contains over 1 million images from 1,000 categories. The EfficientFormer-V2 model can also be used for feature extraction. It can extract features from images that can be used for other tasks like object detection, segmentation, and more. Here are some key statistics about the model:

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A EfficientFormer-V2 Image Classification Model. Pretrained With Distillation On ImageNet-1k.

A EfficientFormer-V2 image classification model. Pretrained with distillation on ImageNet-1k. There was an error while loading. Please reload this page. Efficientformerv2 s2.snap dist in1k is an image classification model that's designed to be efficient and fast. With 12.7 million parameters, it's relatively lightweight, and its performance is on par with other models in its class.

But What Really Sets It Apart Is Its Speed -

But what really sets it apart is its speed - it can process images quickly and accurately, making it a great choice for applications where speed is crucial. Plus, it's been pre-trained on ImageNet-1k, which means it's already learned to recognize a wide range of objects and features. Whether you're building a computer vision system or just need a reliable image classifier, Efficientformerv2 s2.sna...

It’s Designed To Be Efficient And Fast, Making It Perfect

It’s designed to be efficient and fast, making it perfect for mobile devices and applications where speed is crucial. The model is trained on the ImageNet-1k dataset and can classify images into one of the 1000 classes. But what does that mean for you? Imagine you’re building an app that needs to identify objects in images. With the EfficientFormer-V2, you can do just that. For example, if you upl...

But The Model Can Do More Than Just Classify Images.

But the model can do more than just classify images. It can also extract features from images, which can be used for other tasks like object detection, segmentation, and more. Think of it like this: the model is not just looking at the image as a whole, but it’s also looking at the individual parts that make up the image. This can be really useful for tasks like image search, where you need to fin...

There Was An Error While Loading. Please Reload This Page.

There was an error while loading. Please reload this page. A EfficientFormer-V2 image classification model. Pretrained with distillation on ImageNet-1k. Models are trained on ImageNet-1K and deployed on iPhone 12 with CoreMLTools to get latency. Rethinking Vision Transformers for MobileNet Size and Speed Yanyu Li1,2, Ju Hu1, Yang Wen1, Georgios Evangelidis1, Kamyar Salahi3, Yanzhi Wang2, Sergey Tu...