Instructions to use yjwsb233/Resnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yjwsb233/Resnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yjwsb233/Resnet") 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("yjwsb233/Resnet") model = AutoModelForImageClassification.from_pretrained("yjwsb233/Resnet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.
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- train_batch_size:
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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### Framework versions
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.08159916505776756
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Framework versions
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