Instructions to use suous/recnext_a0.dist_300e_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use suous/recnext_a0.dist_300e_in1k with timm:
import timm model = timm.create_model("hf_hub:suous/recnext_a0.dist_300e_in1k", pretrained=True) - Transformers
How to use suous/recnext_a0.dist_300e_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="suous/recnext_a0.dist_300e_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("suous/recnext_a0.dist_300e_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6eddf51c10d78d610d61d89707d9cb21758d5e269ff4d5a5d11eaccb6ae7caf
- Size of remote file:
- 12.8 MB
- SHA256:
- d6b002a7d848b837598caaf274c0a8ad5445b5bed80f7ea8a7544c07042570cc
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