Instructions to use Thamer/resnet-fine_tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thamer/resnet-fine_tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thamer/resnet-fine_tuned") 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("Thamer/resnet-fine_tuned") model = AutoModelForImageClassification.from_pretrained("Thamer/resnet-fine_tuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e4951144d400f2b283edca3906f5aff105fa4c021146448706c1ade48d444b9
- Size of remote file:
- 3.96 kB
- SHA256:
- 64e932b52e21471db1889835fa3712100e33896ec91d211aab08d7e4883c90c6
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