Create README.md

README.md

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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))
+```