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---
language:
- en
tags:
- ResNet-50
---
# ResNet-50
## Model Description
ResNet-50 model from [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) paper.
## Original implementation
Follow [this link](https://huggingface.co/microsoft/resnet-50) to see the original implementation.
# How to use
You can use the `base` model that returns `last_hidden_state`.
```python
from transformers import AutoFeatureExtractor
from onnxruntime import InferenceSession
from datasets import load_dataset
# load image
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
# load model
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
session = InferenceSession("onnx/model.onnx")
# ONNX Runtime expects NumPy arrays as input
inputs = feature_extractor(image, return_tensors="np")
outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
```
Or you can use the model with classification head that returns `logits`.
```python
from transformers import AutoFeatureExtractor
from onnxruntime import InferenceSession
from datasets import load_dataset
# load image
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
# load model
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
session = InferenceSession("onnx/model_cls.onnx")
# ONNX Runtime expects NumPy arrays as input
inputs = feature_extractor(image, return_tensors="np")
outputs = session.run(output_names=["logits"], input_feed=dict(inputs))
```
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