Upload README.md

README.md

@@ -0,0 +1,65 @@
+---
+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 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])
+tiger cat
+```
+
+
+
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/resnet).