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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from datdevsteve/dinov2-nivra-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/datdevsteve/dinov2-nivra-finetuned/resolve/main/training_args.bin
- Command line
-
hf download hf://datdevsteve/dinov2-nivra-finetuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/datdevsteve/dinov2-nivra-finetuned/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 19393cd91e6a598f92bd6612913916c297ba1d76f42f5ad5f60e6a257e9e0d9f
- Size of remote file:
- 5.91 kB
- SHA256:
- abe8733155da54318d5245cdc0cf546690e48ec85f9745f3aafab2d978b9b744
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