Instructions to use rishitunu/ecc_segformer_main with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishitunu/ecc_segformer_main with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rishitunu/ecc_segformer_main")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("rishitunu/ecc_segformer_main") model = SegformerForSemanticSegmentation.from_pretrained("rishitunu/ecc_segformer_main", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from rishitunu/ecc_segformer_main: direct link, hf CLI and curl.
- Browser
- Download file 372 Bytes
-
https://huggingface.co/rishitunu/ecc_segformer_main/resolve/refs%2Fpr%2F2/preprocessor_config.json
- Command line
-
hf download hf://rishitunu/ecc_segformer_main@refs/pr/2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/rishitunu/ecc_segformer_main/resolve/refs%2Fpr%2F2/preprocessor_config.json
372 Bytes
| { | |
| "do_normalize": true, | |
| "do_reduce_labels": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "SegformerImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 512, | |
| "width": 512 | |
| } | |
| } | |