Instructions to use daking/custom-resnet50d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daking/custom-resnet50d with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="daking/custom-resnet50d", 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("daking/custom-resnet50d", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("daking/custom-resnet50d", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79ca294ed71f038e42d54b6598b90cbbde9d8ba00ba4806b3a6683f6565721c2
- Size of remote file:
- 103 MB
- SHA256:
- 4a945a1d4bcb0c80f4a944dd77dc32441d39c2c06fa67d650e9a9090fde8934b
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