Instructions to use MinhLe999/3class_EfficientFormer30M_ForTesting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MinhLe999/3class_EfficientFormer30M_ForTesting with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MinhLe999/3class_EfficientFormer30M_ForTesting", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("MinhLe999/3class_EfficientFormer30M_ForTesting", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3ec41d13b730243aaa9fc7117fcb240b5b6cae43dc9478c1e8ce3f81cc340bc
- Size of remote file:
- 5.27 kB
- SHA256:
- 314a43a6ba5152a9c4a7122c532683a5b014c09343e334e826f3679c2abae0e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.