This Operator generates feature vectors from the pytorch pretrained **Resnet50** model[1], which is trained on [imagenet dataset](https://image-net.org/download.php).
This Operator generates feature vectors from the pytorch pretrained **Resnet** 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)”[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].
- supported types: `str`, for example 'resnet50' or 'resnet101'
- framework:
- the framework of the model
- supported types: `str`, default is 'pytorch'
@ -47,7 +47,7 @@ You can get the required python package by [requirements.txt](./requirements.txt
## How it works
The `towhee/resnet50-image-embedding` Operator implements the function of image embedding, which can add to the pipeline. For example, it's the key Operator named embedding_model within [image-embedding-resnet50](https://hub.towhee.io/towhee/image-embedding-resnet50) pipeline, and it is the red box in the picture below.
The `towhee/resnet-image-embedding` Operator implements the function of image embedding, which can add to the pipeline. For example, it's the key Operator named embedding_model within [image-embedding-resnet](https://hub.towhee.io/towhee/image-embedding-resnet) pipeline, and it is the red box in the picture below.
