Instructions to use timm/sequencer2d_l.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/sequencer2d_l.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/sequencer2d_l.in1k", pretrained=True) - Transformers
How to use timm/sequencer2d_l.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/sequencer2d_l.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/sequencer2d_l.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfcb53d2afeeadd76957ac1eb6e027967399742f2a7c050f08333fab38cfb20b
- Size of remote file:
- 218 MB
- SHA256:
- d8cd8430e3f43cbbbad916cea40f71ed51dc69ed668e8be3c397f42c1e835327
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