Instructions to use hf-internal-testing/tiny-random-ConvNextV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvNextV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ConvNextV2ForImageClassification") 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-ConvNextV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ConvNextV2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-ConvNextV2ForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 330 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-ConvNextV2ForImageClassification/resolve/refs%2Fpr%2F8/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-ConvNextV2ForImageClassification@refs/pr/8/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-ConvNextV2ForImageClassification/resolve/refs%2Fpr%2F8/model.safetensors
330 kB
- Xet hash:
- c5cb55d9b9b8f252aae419f9e7e8a4a69ea17c8684b2df67f03d2d4e20b30271
- Size of remote file:
- 330 kB
- SHA256:
- e01095f7aa83952a06b84a2921bf6e9a410d0abf39371a0389200ba7a4ce63fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.