Instructions to use hf-internal-testing/tiny-random-SegformerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SegformerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SegformerForImageClassification") 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-SegformerForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SegformerForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-SegformerForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 3.01 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-SegformerForImageClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-SegformerForImageClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-SegformerForImageClassification/resolve/refs%2Fpr%2F1/model.safetensors
3.01 MB
- Xet hash:
- 78ad4710348c4ab477505fbd929c7bc6d0ef13443851255c58a0d948ee303cb2
- Size of remote file:
- 3.01 MB
- SHA256:
- 57ee8eda55146666871fbd5384b1947f14913c1e2edf10f3e7d0f69ff321612b
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