Instructions to use Ganaa0614/mask2former-optimized-panoptic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ganaa0614/mask2former-optimized-panoptic with Transformers:
# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("Ganaa0614/mask2former-optimized-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("Ganaa0614/mask2former-optimized-panoptic") - Notebooks
- Google Colab
- Kaggle
mask2former-optimized-panoptic
This model is a fine-tuned version of facebook/mask2former-swin-tiny-coco-panoptic 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
- 69
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Model tree for Ganaa0614/mask2former-optimized-panoptic
Base model
facebook/mask2former-swin-tiny-coco-panoptic