Instructions to use fireviewer/dfine-xlarge-fire-smoke-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fireviewer/dfine-xlarge-fire-smoke-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="fireviewer/dfine-xlarge-fire-smoke-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForObjectDetection tokenizer = AutoTokenizer.from_pretrained("fireviewer/dfine-xlarge-fire-smoke-v2") model = AutoModelForObjectDetection.from_pretrained("fireviewer/dfine-xlarge-fire-smoke-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_convert_annotations": true, | |
| "do_normalize": false, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "format": "coco_detection", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "RTDetrImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "pad_size": { | |
| "height": 768, | |
| "width": 768 | |
| }, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "max_height": 768, | |
| "max_width": 768 | |
| } | |
| } | |