Instructions to use crocutacrocuto/dinov2-large-MEG7-20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crocutacrocuto/dinov2-large-MEG7-20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="crocutacrocuto/dinov2-large-MEG7-20") 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("crocutacrocuto/dinov2-large-MEG7-20") model = AutoModelForImageClassification.from_pretrained("crocutacrocuto/dinov2-large-MEG7-20") - Notebooks
- Google Colab
- Kaggle
dinov2-large-MEG7-20 / runs /Apr04_16-58-01_hugo-Precision-7960-Tower /events.out.tfevents.1743778689.hugo-Precision-7960-Tower.538069.0
- Xet hash:
- db71fbfd6401bb5ceec2da2f64ad73feb3c2568eefc362aafa7efb68ff420886
- Size of remote file:
- 1.26 MB
- SHA256:
- e3c0c1a92f112f94957385dd057d996a891fe5edc0ad550bd31c593efc57f623
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