Instructions to use leftthomas/resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leftthomas/resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="leftthomas/resnet50", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("leftthomas/resnet50", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("leftthomas/resnet50", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| - resnet | |
| license: afl-3.0 | |
| datasets: | |
| - imagenet | |
| 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-50 | |
| Pretrained model on [ImageNet](http://www.image-net.org/). The ResNet architecture was introduced in | |
| [this paper](https://arxiv.org/abs/1512.03385). | |
| ## Intended uses | |
| You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head | |
| to fine-tune it on a downstream task (another classification task with different labels, image segmentation or | |
| object detection, to name a few). | |
| ## Evaluation results | |
| This model has a top1-accuracy of 76.13% and a top-5 accuracy of 92.86% in the evaluation set of ImageNet. | |