Instructions to use FredZhang7/efficientnetv25_rw_s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FredZhang7/efficientnetv25_rw_s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="FredZhang7/efficientnetv25_rw_s", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FredZhang7/efficientnetv25_rw_s", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
ea15e8c
1
Parent(s): 5f51f01
Update configuration_customefficientnetv2.py
Browse files
configuration_customefficientnetv2.py
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from transformers import PretrainedConfig
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class
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model_type = "EfficientNetV2_5"
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keys_to_ignore_at_inference = ["efficientnetv2"]
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from transformers import PretrainedConfig
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class CustomEfficientNetV2Config(PretrainedConfig):
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model_type = "EfficientNetV2_5"
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keys_to_ignore_at_inference = ["efficientnetv2"]
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