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")# 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
- Xet hash:
- 7e6cc20c9504e2ad1ae3fb43c9c1fd6fad07484c5bddd2cfd3e4c411e221696c
- Size of remote file:
- 1.15 kB
- SHA256:
- ced645c982ca62347d9cf52c6d3973157fab44c68f31a6b744aecdbf2717a609
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.