This Operator generates feature vectors from the pytorch pretrained **Resnet50** mode, which is trained on [imagenet dataset](https://image-net.org/download.php).
This Operator generates feature vectors from the pytorch pretrained **Resnet50** model[1], 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"[1].
**Resnet** models were proposed in “[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)”[2], 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 were difficult due to the problem of vanishing gradients"[3].
## Interface
@ -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
@ -105,7 +105,8 @@ Here is the main file structure of the `resnet50-image-embedding` Operator. If y
