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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 AutoImageProcessor, ResNetForImageClassification
+import torch
+from datasets import load_dataset
+
+dataset = load_dataset("huggingface/cats-image")
+image = dataset["test"]["image"][0]
+
+processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
+model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
+
+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).
+
+### 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}
+}
+```

preprocessor_config.json

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+{
+  "crop_pct": 0.875,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "ConvNextFeatureExtractor",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "size": 224
+}