Update README.md

README.md

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----
-license: apache-2.0
-tags:
-- vision
-- image-classification
-datasets:
-- imagenet-1k
----
-
-# 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.
-
-### 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 AutoFeatureExtractor, ResNetForImageClassification
-import torch
-from datasets import load_dataset
-
-dataset = load_dataset("huggingface/cats-image")
-image = dataset["test"]["image"][0]
-
-feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
-model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
-
-inputs = feature_extractor(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).
-
-### 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}
-}
-```