Instructions to use swadhindas324/swinv2-base-patch4-window8-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swadhindas324/swinv2-base-patch4-window8-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="swadhindas324/swinv2-base-patch4-window8-256")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("swadhindas324/swinv2-base-patch4-window8-256") model = AutoModel.from_pretrained("swadhindas324/swinv2-base-patch4-window8-256") - Notebooks
- Google Colab
- Kaggle
swinv2-base-patch4-window8-256
This model is a fine-tuned version of microsoft/swinv2-base-patch4-window8-256 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: 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
Framework versions
- Transformers 5.12.1
- Pytorch 2.12.0+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2
- Downloads last month
- 70
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Model tree for swadhindas324/swinv2-base-patch4-window8-256
Base model
microsoft/swinv2-base-patch4-window8-256