Image-to-Text
Transformers
Safetensors
Vietnamese
blip
image-text-to-text
Generated from Trainer
xedap
pricing
Instructions to use hquan21/ai-bike-pricing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hquan21/ai-bike-pricing with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="hquan21/ai-bike-pricing")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("hquan21/ai-bike-pricing") model = AutoModelForImageTextToText.from_pretrained("hquan21/ai-bike-pricing") - Notebooks
- Google Colab
- Kaggle
ai-bike-pricing
This model is a fine-tuned version of Salesforce/blip-image-captioning-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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
- 6
Model tree for hquan21/ai-bike-pricing
Base model
Salesforce/blip-image-captioning-base