Instructions to use rishitunu/ecc_segformerv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishitunu/ecc_segformerv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rishitunu/ecc_segformerv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("rishitunu/ecc_segformerv2") model = SegformerForSemanticSegmentation.from_pretrained("rishitunu/ecc_segformerv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from rishitunu/ecc_segformerv2: direct link, hf CLI and curl.
- Browser
- Download file 462 Bytes
-
https://huggingface.co/rishitunu/ecc_segformerv2/resolve/main/eval_results.json
- Command line
-
hf download hf://rishitunu/ecc_segformerv2/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/rishitunu/ecc_segformerv2/resolve/main/eval_results.json
462 Bytes
Invalid JSON:Unexpected token 'N', ..."kground": NaN,
""... is not valid JSON
| { | |
| "epoch": 39.84, | |
| "eval_accuracy_background": NaN, | |
| "eval_accuracy_crack": 0.19244279056826877, | |
| "eval_iou_background": 0.0, | |
| "eval_iou_crack": 0.17234731981663806, | |
| "eval_loss": 0.3477784991264343, | |
| "eval_mean_accuracy": 0.19244279056826877, | |
| "eval_mean_iou": 0.08617365990831903, | |
| "eval_overall_accuracy": 0.19244279056826877, | |
| "eval_runtime": 443.3756, | |
| "eval_samples_per_second": 0.399, | |
| "eval_steps_per_second": 0.101 | |
| } |