# Load model directly
from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering
processor = AutoProcessor.from_pretrained("fawern/blip-Visual-QuestionAnswering-coco")
model = AutoModelForVisualQuestionAnswering.from_pretrained("fawern/blip-Visual-QuestionAnswering-coco")Quick Links
blip-Visual-QuestionAnswering-coco
This model is a fine-tuned version of Salesforce/blip-vqa-base on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
- Downloads last month
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Model tree for fawern/blip-Visual-QuestionAnswering-coco
Base model
Salesforce/blip-vqa-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="fawern/blip-Visual-QuestionAnswering-coco")