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) - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +3 -2
config.json
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{
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"architectures": [
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"
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],
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"auto_map": {
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"AutoConfig": "configuration_efficientnet.EfficientNetConfig",
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"AutoModel": "modeling_efficientnet.EfficientNetModel"
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},
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"global_pool": "avg",
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"model_name": "efficientnet_b0",
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{
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"architectures": [
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"EfficientNetModelForImageClassification"
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],
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"auto_map": {
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"AutoConfig": "configuration_efficientnet.EfficientNetConfig",
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"AutoModel": "modeling_efficientnet.EfficientNetModel",
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"AutoModelForImageClassification": "modeling_efficientnet.EfficientNetModelForImageClassification"
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},
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"global_pool": "avg",
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"model_name": "efficientnet_b0",
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