Image Classification
Transformers
Safetensors
English
lse-dinov2
vision
dinov2
scale
consistency
custom_code
Instructions to use ashiq24/dinov2-base-lse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashiq24/dinov2-base-lse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ashiq24/dinov2-base-lse", 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("ashiq24/dinov2-base-lse", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 0f95e7a13aa543fef87a836afab0d4f6d3adb91cac10bc45e1f4104e2a706a78
- Size of remote file:
- 9.51 MB
- SHA256:
- cf16241e4d0a81b1b80d50f615cfb35af2d16bcd99ff9b2a6455db5d49ee7820
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