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
- Xet hash:
- d7ffe0313a415c6a83f77241b7f2cdf504935e7ad2623b816a93690d707ce48f
- Size of remote file:
- 21.4 MB
- SHA256:
- 90189b79e86365ece2efce69b510a59c1d044421276a9151bafe152989b32b2a
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