Instructions to use hf-internal-testing/tiny-random-BlipForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BlipForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="hf-internal-testing/tiny-random-BlipForQuestionAnswering")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-BlipForQuestionAnswering") model = AutoModelForVisualQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-BlipForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_valid_processor_keys": [ | |
| "images", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_normalize", | |
| "image_mean", | |
| "image_std", | |
| "do_convert_rgb", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "crop_size": 30, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "BlipImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 30, | |
| "width": 30 | |
| } | |
| } | |