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README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- image-classification
+
+datasets:
+- imagenet-1k
+
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
+  example_title: Tiger
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
+  example_title: Teapot
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
+  example_title: Palace
+
+---
+
+# ResNet
+
+ResNet model trained on imagenet-1k. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) and first released in [this repository](https://github.com/KaimingHe/deep-residual-networks). 
+
+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 introduced residual connections, they allow to train networks with an unseen number of layers (up to 1000). ResNet won the 2015 ILSVRC & COCO competition, one important milestone in deep computer vision.
+
+![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:
+
+```python
+>>> from transformers import AutoImageProcessor, AutoModelForImageClassification
+>>> import torch
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("huggingface/cats-image")
+>>> image = dataset["test"]["image"][0]
+
+>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-18")
+>>> model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-18")
+
+>>> inputs = image_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])
+tiger cat
+```
+
+
+
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/resnet).

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
+}