Image Segmentation
Transformers
Safetensors
rf_detr
instance-segmentation
rf-detr
roboflow
coco
quantized
nvfp4
fp4
experimental
Instructions to use Reza2kn/rf-detr-segmentation-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Reza2kn/rf-detr-segmentation-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Reza2kn/rf-detr-segmentation-NVFP4")# Load model directly from transformers import AutoImageProcessor, RfDetrForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("Reza2kn/rf-detr-segmentation-NVFP4") model = RfDetrForInstanceSegmentation.from_pretrained("Reza2kn/rf-detr-segmentation-NVFP4") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base": "/home/rezo/rfdetr-nvfp4/rf-detr-segmentation", | |
| "quant": "/home/rezo/rfdetr-nvfp4/rf-detr-segmentation-nvfp4", | |
| "device": "cuda", | |
| "dtype": "bfloat16", | |
| "metrics": { | |
| "logits": { | |
| "shape": [ | |
| 1, | |
| 200, | |
| 91 | |
| ], | |
| "rel_l2": 0.13868460059165955, | |
| "cosine": 0.9905619621276855, | |
| "max_abs": 4.703125 | |
| }, | |
| "pred_boxes": { | |
| "shape": [ | |
| 1, | |
| 200, | |
| 4 | |
| ], | |
| "rel_l2": 0.5793111324310303, | |
| "cosine": 0.8245475888252258, | |
| "max_abs": 0.974365234375 | |
| }, | |
| "pred_masks": { | |
| "shape": [ | |
| 1, | |
| 200, | |
| 108, | |
| 108 | |
| ], | |
| "rel_l2": 1.0143704414367676, | |
| "cosine": 0.6041960716247559, | |
| "max_abs": 115.126953125 | |
| } | |
| } | |
| } | |