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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Transformers-compatible ONNX weights
Browse files- onnx/model.onnx +3 -0
- onnx/model_quantized.onnx +3 -0
- onnx/quantize_config.json +3 -0
onnx/model.onnx
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onnx/model_quantized.onnx
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onnx/quantize_config.json
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oid sha256:8ca0b629b2de73bc39bc9f37251ccf20fc0eebffd614bd7b2c90ec356f32d737
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size 564
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