Instructions to use joelg/sam-vit-base-cotes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joelg/sam-vit-base-cotes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="joelg/sam-vit-base-cotes")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("joelg/sam-vit-base-cotes") model = AutoModelForMaskGeneration.from_pretrained("joelg/sam-vit-base-cotes") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/sam-vit-base
tags:
- generated_from_trainer
model-index:
- name: sam-vit-base-cotes
results: []
sam-vit-base-cotes
This model is a fine-tuned version of facebook/sam-vit-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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 3.0
Training results
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2