Instructions to use iSEE-Laboratory/llmdet_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iSEE-Laboratory/llmdet_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="iSEE-Laboratory/llmdet_base")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("iSEE-Laboratory/llmdet_base") model = AutoModelForZeroShotObjectDetection.from_pretrained("iSEE-Laboratory/llmdet_base") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_convert_annotations": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "format": "coco_detection", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "GroundingDinoImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "pad_size": null, | |
| "processor_class": "GroundingDinoProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 1333, | |
| "shortest_edge": 800 | |
| } | |
| } | |