From e62917a41b283e1bb3561a11619de445ac1473d3 Mon Sep 17 00:00:00 2001 From: shiyu22 Date: Thu, 9 Dec 2021 14:15:02 +0800 Subject: [PATCH] Update README Signed-off-by: shiyu22 --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index f07b0bf..2f128e9 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ Authors: Kyle, shiyu22 ## Overview -This Operator generates feature vectors from the pytorch pretrained **Resnet50** mode, which is trained on [COCO dataset](https://cocodataset.org/#download). +This Operator generates feature vectors from the pytorch pretrained **Resnet50** mode, which is trained on [imagenet dataset](https://image-net.org/download.php). -**Resnet** models were proposed in “[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)”, this model was the winner of ImageNet challenge in 2015. The fundamental breakthrough with ResNet was it allowed us to train extremely deep neural networks with 150+layers successfully. Prior to ResNet training very deep neural networks was difficult due to the problem of vanishing gradients. +**Resnet** models were proposed in “[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)”, this model was the winner of ImageNet challenge in 2015. "The fundamental breakthrough with ResNet was it allowed us to train extremely deep neural networks with 150+layers successfully. Prior to ResNet training very deep neural networks was difficult due to the problem of vanishing gradients"[1]. ## Interface @@ -32,7 +32,7 @@ This Operator generates feature vectors from the pytorch pretrained **Resnet50** ### Requirements -You can get the required python package by [requirements.txt](./requirements.txt) and [pytorch/requirements.txt](./pytorch/requirements.txt). In fact, Towhee will automatically install these packages when you first load the Operator Repo, so you don't need to install them manually, here is just a list. +You can get the required python package by [requirements.txt](./requirements.txt) and [pytorch/requirements.txt](./pytorch/requirements.txt). Towhee will automatically install these packages when you first load the Operator Repo, so you don't need to install them manually, here is just a list. - towhee - torch @@ -45,7 +45,7 @@ The `towhee/resnet50-image-embedding` Operator implements the function of image ![img](./pic/operator.png) -When using this Operator to write Pipline's Yaml file, you need to declare the following content according to the interface of Resnet50ImageEmbedding class: +When using this Operator to write Pipeline's Yaml file, you need to declare the following content according to the interface of Resnet50ImageEmbedding class: ```yaml operators: @@ -80,7 +80,7 @@ dataframes: vtype: 'numpy.ndarray' ``` -We can see that in yaml, the **operator** part declares the `init_args` of the class and the` input` and `output`dataframe, and the **dataframe** declares the parameter `name` and `vtype`. +We can see that in yaml, the **operator** part declares the `init_args` of the class and the ` input` and `output` dataframe, and the **dataframe** declares the parameter `name` and `vtype`. ### File Structure