Instructions to use psychefr/blip-vqa-satquery with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use psychefr/blip-vqa-satquery with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="psychefr/blip-vqa-satquery")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("psychefr/blip-vqa-satquery") model = AutoModelForVisualQuestionAnswering.from_pretrained("psychefr/blip-vqa-satquery", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 792 Bytes
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"_name_or_path": "/content/drive/MyDrive/SatQuery/checkpoints/blip_vqa_best_rebalanced",
"architectures": [
"BlipForQuestionAnswering"
],
"image_text_hidden_size": 256,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"label_smoothing": 0.0,
"logit_scale_init_value": 2.6592,
"model_type": "blip",
"projection_dim": 512,
"text_config": {
"_attn_implementation_autoset": true,
"initializer_factor": 1.0,
"model_type": "blip_text_model",
"num_attention_heads": 12
},
"torch_dtype": "float32",
"transformers_version": "4.46.3",
"vision_config": {
"_attn_implementation_autoset": true,
"dropout": 0.0,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"model_type": "blip_vision_model",
"num_channels": 3
}
}
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