Instructions to use hf-internal-testing/tiny-random-Dinov2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Dinov2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-Dinov2ForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Dinov2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-Dinov2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-Dinov2ForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 318 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-Dinov2ForImageClassification/resolve/ae44840966456aae33641df2c8c8a4af5b457b24/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Dinov2ForImageClassification@ae44840966456aae33641df2c8c8a4af5b457b24/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-Dinov2ForImageClassification/resolve/ae44840966456aae33641df2c8c8a4af5b457b24/pytorch_model.bin
318 kB
- Xet hash:
- a873f1135696fbc39156557c3abaf7630db17312aa06333dba7b5e2c462a3199
- Size of remote file:
- 318 kB
- SHA256:
- eb179984c527a2e312caebba1b515bdd8fde156a0b4de9714c21856e78a4591d
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