---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
library_name: openvino
---

# ResNet-50 v1.5

ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. 

Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.

This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).

![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png)

## Intended uses & limitations

You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for
fine-tuned versions on a task that interests you.

### convert to openvino
```bash
pip install --upgrade --upgrade-strategy eager optimum[openvino]
optimum-cli export openvino --model microsoft/resnet-50 ov_model/
```

### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

```python
from transformers import AutoImageProcessor, ResNetForImageClassification
from optimum.intel import OVModelForImageClassification
import torch
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained("Charles95/openvino_resnet50")
model = OVModelForImageClassification.from_pretrained("Charles95/openvino_resnet50")

inputs = processor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```

For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet).

### performance improvement

pytorch infer avg ≈66.5ms  
openvino infer avg ≈7.9ms  
3090 with transformers infer avg ≈6.3ms

improve ≈800%

### BibTeX entry and citation info

```bibtex
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
```