---
library_name: keras-hub
license: apache-2.0
tags:
- image-classification
pipeline_tag: image-segmentation
---
### Model Overview
# Model Summary

Instantiates the ResNet architecture amended by “bag of tricks” modifications.
## Reference
[Bag of Tricks for Image Classification with Convolutional Neural Networks](https://arxiv.org/abs/1812.01187)

ResNetVd introduces two key modifications to the standard ResNet. First, the initial convolutional layer is replaced by a series of three successive convolutional layers. Second, shortcut connections use an additional pooling operation rather than performing downsampling within the convolutional layers themselves.
## Links

* [ResNetVD Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/resnetvd-quickstart-notebook)
* [ResNet and ResNetVD series Doc](https://paddleclas.readthedocs.io/en/latest/models/ResNet_and_vd_en.html)
* [ResNetVD Model Card](https://arxiv.org/abs/1812.01187)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)

## Installation

Keras and KerasHub can be installed with:

```
pip install -U -q keras-hub
pip install -U -q keras
```

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.

## Presets

The following model checkpoints are provided by the Keras team.Weights have been ported from: [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR). Full code examples for each are available below.


| Preset name            | Parameters | Description                                                                                     |
|------------------------|------------|-------------------------------------------------------------------------------------------------|
| `resnet_vd_18_imagenet`   | 11.72M     | 18-layer ResNetVD model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.|
| `resnet_vd_34_imagenet`  | 21.84M     | 34-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
| `resnet_vd_50_imagenet` | 25.63M     | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution.  |
| `resnet_vd_50_ssld_imagenet`   | 25.63M    | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation.  |
| `resnet_vd_50_ssld_v2_imagenet`           | 25.63M    | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation and AutoAugment.  |
| `resnet_vd_50_ssld_v2_fix_imagenet`           | 25.63M   | 50-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation, AutoAugment and additional fine-tuning of the classification head.                                              |
| `resnet_vd_101_imagenet`        | 44.67M    | 101-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
| `resnet_vd_101_ssld_imagenet`  | 44.67M    | 101-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution with knowledge distillation. |
| `resnet_vd_152_imagenet`          | 60.36M    | 152-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |
| `resnet_vd_200_imagenet`          | 74.93M    | 200-layer ResNet model pre-trained on the ImageNet 1k dataset at a 224x224 resolution. |

## Example Usage
```python
   
from keras_hub.models import ResNetBackbone
import keras
import numpy as np

input_data = np.ones(shape=(8, 224, 224, 3))

# Pretrained backbone
model = ResNetBackbone.from_preset("resnet_vd_200_imagenet")
output = model(input_data)

# Randomly initialized backbone with a custom config
model = ResNetBackbone(
        input_conv_filters=[32, 32, 64],
        input_conv_kernel_sizes=[3, 3, 3],
        stackwise_num_filters=[64, 128, 256, 512],
        stackwise_num_blocks=[3, 4, 5, 6],
        stackwise_num_strides=[1, 2, 2, 2],
        block_type="bottleneck_block_vd",
    )
output = model(input_data)

```

## Example Usage with Hugging Face URI

```python
   
from keras_hub.models import ResNetBackbone
import keras
import numpy as np

input_data = np.ones(shape=(8, 224, 224, 3))

# Pretrained backbone
model = ResNetBackbone.from_preset("hf://keras/resnet_vd_200_imagenet")
output = model(input_data)

# Randomly initialized backbone with a custom config
model = ResNetBackbone(
        input_conv_filters=[32, 32, 64],
        input_conv_kernel_sizes=[3, 3, 3],
        stackwise_num_filters=[64, 128, 256, 512],
        stackwise_num_blocks=[3, 4, 5, 6],
        stackwise_num_strides=[1, 2, 2, 2],
        block_type="bottleneck_block_vd",
    )
output = model(input_data)

```