Instructions to use Thastp/efficientnet_b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thastp/efficientnet_b1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thastp/efficientnet_b1", 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_b1", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("Thastp/efficientnet_b1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4257ec3c7705c3f8aea02b64b3f73e64e5c9f8473cc9263e6fa11392d8b7310
- Size of remote file:
- 31.5 MB
- SHA256:
- f1813e3c9f91308823701bc60e65f1417a1bc776274096c60784f4756a5a1d11
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