Instructions to use Thastp/efficientnet_b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thastp/efficientnet_b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thastp/efficientnet_b0", trust_remote_code=True) 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("Thastp/efficientnet_b0", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("Thastp/efficientnet_b0", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 543 Bytes
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"architectures": [
"EfficientNetModelForImageClassification"
],
"auto_map": {
"AutoConfig": "configuration_efficientnet.EfficientNetConfig",
"AutoModel": "modeling_efficientnet.EfficientNetModel",
"AutoModelForImageClassification": "modeling_efficientnet.EfficientNetModelForImageClassification"
},
"global_pool": "avg",
"model_name": "efficientnet_b0",
"model_type": "efficientnet",
"num_classes": 1000,
"pretrained": true,
"torch_dtype": "float32",
"transformers_version": "4.50.3"
}
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