Image Classification
Transformers
PyTorch
ONNX
Safetensors
efficientnet
biology
efficientnet-b2
vision
Instructions to use dennisjooo/Birds-Classifier-EfficientNetB2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dennisjooo/Birds-Classifier-EfficientNetB2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dennisjooo/Birds-Classifier-EfficientNetB2") 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("dennisjooo/Birds-Classifier-EfficientNetB2") model = AutoModelForImageClassification.from_pretrained("dennisjooo/Birds-Classifier-EfficientNetB2") - Inference
- Notebooks
- Google Colab
- Kaggle
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Parent(s): 48317c6
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README.md
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@@ -121,7 +121,8 @@ The training set was also augmented using:
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The following are the hyperparameters used for training:
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- **Training regime:** fp32
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- **Learning rate**: 1e-3
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- **Learning rate scheduler**: Reduce on plateau which monitors validation loss with patience of 2 and decay rate of 0.1
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- **Batch size**: 64
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The following are the hyperparameters used for training:
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- **Training regime:** fp32
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- **Loss:** Cross entropy
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- **Optimizer**: Adam with default betas (0.99, 0.999)
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- **Learning rate**: 1e-3
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- **Learning rate scheduler**: Reduce on plateau which monitors validation loss with patience of 2 and decay rate of 0.1
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- **Batch size**: 64
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