Instructions to use hf-tiny-model-private/tiny-random-SegformerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SegformerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/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-tiny-model-private/tiny-random-SegformerForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-SegformerForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-tiny-model-private/tiny-random-SegformerForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 3.01 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-SegformerForImageClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-SegformerForImageClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-SegformerForImageClassification/resolve/refs%2Fpr%2F1/model.safetensors
3.01 MB
- Xet hash:
- 9df114ccccb20c6b8fb58a653d3a347553ee911b0db3b25cc31df111e5e6e86c
- Size of remote file:
- 3.01 MB
- SHA256:
- 3462bbe3aeab8f3137fc3ba4c28b8739ab57d435e4e6ad7eb154e6157c0673c0
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