Resnet 18

🚀 ResNet

ResNet is a model trained on ImageNet-1k. It addresses the challenge of training very deep neural networks by introducing residual connections, which significantly enhances the model's performance in image classification tasks.

🚀 Quick Start

ResNet model trained on ImageNet-1k. It was introduced in the paper Deep Residual Learning for Image Recognition and first released in this repository.

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.

✨ Features

• ResNet introduced residual connections, which allow to train networks with an unseen number of layers (up to 1000).
• ResNet won the 2015 ILSVRC & COCO competition, marking an important milestone in deep computer vision.

📚 Documentation

Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.

How to use

Here is how to use this model:

>>> 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.

📄 License

This model is licensed under the Apache-2.0 license.