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
- Xet hash:
- 3de122dcfb032dc4a57a56d0039c6648cbb60a220d590fc1d41a5023f1993e6d
- Size of remote file:
- 33.7 MB
- SHA256:
- 179d9b6d4229355b0b3d9d1a0cb144971d2dfdc70cf7c68ed38ee73a17991e65
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.