Image Classification
Transformers
PyTorch
Safetensors
English
beit
computer-vision
medical
healthcare
indian-healthcare
skin-conditions
medical-imaging
dinov2
Eval Results (legacy)
Instructions to use datdevsteve/dinov2-nivra-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datdevsteve/dinov2-nivra-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="datdevsteve/dinov2-nivra-finetuned") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("datdevsteve/dinov2-nivra-finetuned") model = AutoModelForImageClassification.from_pretrained("datdevsteve/dinov2-nivra-finetuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea090f2a4694c020b98e88af018b078504ea2d926f8964839aa937cbf06c5456
- Size of remote file:
- 343 MB
- SHA256:
- 981ab15c8668d7036154481f2a3eed6028a5652469717c166d92607aaa77624b
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