Loading
Tem um workflow do ComfyUI? Gere uma documentação legível direto no navegador.Torne um workflow legível.Experimentar agora ↗
ResNet model trained on imagenet-1k. It was introduced in the paper Deep Residual Learning for Image Recognition and first released in this repository.
Fonte do modelo
Descrição da fonte
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.
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.
Fontes
1 fonteVerificado 17 de set.
Artefatos de modelo
3 artefatosTrechos de fonte
2 trechosmodel image
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.
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.
tf_model.h5
h5 · 44,8 MB · SHA-256 64320beb5ab7…5c96 · Hugging Face
--- 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.  ## 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).
Source context: 4 downloads · 0 likes · Pipeline image-classification · Repo glory0117/resnet-18