Instructions to use jaygala223/maskformer-finetuned-for-38-cloud-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaygala223/maskformer-finetuned-for-38-cloud-dataset with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("jaygala223/maskformer-finetuned-for-38-cloud-dataset") model = MaskFormerForInstanceSegmentation.from_pretrained("jaygala223/maskformer-finetuned-for-38-cloud-dataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jaygala223/maskformer-finetuned-for-38-cloud-dataset: direct link, hf CLI and curl.
- Browser
- Download file 411 MB
-
https://huggingface.co/jaygala223/maskformer-finetuned-for-38-cloud-dataset/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jaygala223/maskformer-finetuned-for-38-cloud-dataset/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/jaygala223/maskformer-finetuned-for-38-cloud-dataset/resolve/main/pytorch_model.bin
411 MB
- Xet hash:
- 22d0b12e16a1d681d6d46b1466a841650f84af82c7afb28840c133e7bad64594
- Size of remote file:
- 411 MB
- SHA256:
- 7a2345bcd85936d95366ba1625184699292b05478ef5326339a75ee4c77675f2
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