Instructions to use rishitunu/FINAL_ecc_segformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishitunu/FINAL_ecc_segformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rishitunu/FINAL_ecc_segformer")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("rishitunu/FINAL_ecc_segformer") model = SegformerForSemanticSegmentation.from_pretrained("rishitunu/FINAL_ecc_segformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from rishitunu/FINAL_ecc_segformer: direct link, hf CLI and curl.
- Browser
- Download file 462 Bytes
-
https://huggingface.co/rishitunu/FINAL_ecc_segformer/resolve/main/eval_results.json
- Command line
-
hf download hf://rishitunu/FINAL_ecc_segformer/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/rishitunu/FINAL_ecc_segformer/resolve/main/eval_results.json
462 Bytes
Invalid JSON:Unexpected token 'N', ..."kground": NaN,
""... is not valid JSON
| { | |
| "epoch": 18.25, | |
| "eval_accuracy_background": NaN, | |
| "eval_accuracy_crack": 0.39388432615865027, | |
| "eval_iou_background": 0.0, | |
| "eval_iou_crack": 0.3935762911195175, | |
| "eval_loss": 0.07488969713449478, | |
| "eval_mean_accuracy": 0.39388432615865027, | |
| "eval_mean_iou": 0.19678814555975874, | |
| "eval_overall_accuracy": 0.39388432615865027, | |
| "eval_runtime": 392.3604, | |
| "eval_samples_per_second": 0.494, | |
| "eval_steps_per_second": 0.247 | |
| } |