Instructions to use date3k2/dl-resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use date3k2/dl-resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="date3k2/dl-resnet50") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("date3k2/dl-resnet50") model = AutoModelForImageClassification.from_pretrained("date3k2/dl-resnet50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from date3k2/dl-resnet50: direct link, hf CLI and curl.
- Browser
- Download file 176 Bytes
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https://huggingface.co/date3k2/dl-resnet50/resolve/main/README.md
- Command line
-
hf download hf://date3k2/dl-resnet50/README.md
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curl -L -o README.md https://huggingface.co/date3k2/dl-resnet50/resolve/main/README.md
176 Bytes
metadata
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
This model has been pushed to the Hub using ****:
- Repo: [More Information Needed]
- Docs: [More Information Needed]