Blip Image Captioning Base Test Sagemaker Tops 3
This model is a fine-tuned version of Salesforce/blip-image-captioning-base on the None dataset.
Fuente del modelo
- Tipo
- Text model
- Tamaño del archivo
- 944 MB
- Versión
- vpytorch_model
- Actualizado
- 6 oct 2026
- Descargas
- 7
- Fuente
- Hugging Face
Descripción de la fuente
blip-image-captioning-base-test_sagemaker-tops-3
This model is a fine-tuned version of Salesforce/blip-image-captioning-base on the None 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-06
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- distributed_type: sagemaker_model_parallel
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 6 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- license: bsd-3-clause tags: - generated_from_trainer model-index: - name: blip-image-captioning-base-test_sagemaker-tops-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # blip-image-captioning-base-test_sagemaker-tops-3 This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on the None 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-06 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - distributed_type: sagemaker_model_parallel - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2
Source context: 7 downloads · 0 likes · Pipeline image-text-to-text · Library transformers · Repo GHonem/blip-image-captioning-base-test_sagemaker-tops-3
Source context: 12 downloads · 0 likes · Pipeline image-text-to-text · Library transformers · Repo GHonem/blip-image-captioning-base-test_sagemaker-tops-3
