Instructions to use timm/seresnext26ts.ch_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/seresnext26ts.ch_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/seresnext26ts.ch_in1k", pretrained=True) - Transformers
How to use timm/seresnext26ts.ch_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/seresnext26ts.ch_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("timm/seresnext26ts.ch_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf5730558ad35425b8a1186f8ac6c490685d8115e1e41e538e7d636d1a1790ca
- Size of remote file:
- 41.8 MB
- SHA256:
- cb40efe508b4fa97e3de92a99af9794cbc35d90914a845dbe1e4075fe92726be
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