Instructions to use webnn/efficientnet-lite4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use webnn/efficientnet-lite4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="webnn/efficientnet-lite4") 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("webnn/efficientnet-lite4") model = AutoModelForImageClassification.from_pretrained("webnn/efficientnet-lite4") - Notebooks
- Google Colab
- Kaggle
| { | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "per_model_config": { | |
| "model": { | |
| "op_types": [ | |
| "Flatten", | |
| "Relu", | |
| "GlobalAveragePool", | |
| "Conv", | |
| "Gemm", | |
| "Add", | |
| "MaxPool" | |
| ], | |
| "weight_type": "QUInt8" | |
| } | |
| } | |
| } |