Instructions to use Hasano20/segformer-b4-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hasano20/segformer-b4-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Hasano20/segformer-b4-finetuned")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Hasano20/segformer-b4-finetuned") model = SegformerForSemanticSegmentation.from_pretrained("Hasano20/segformer-b4-finetuned") - Notebooks
- Google Colab
- Kaggle
segformer-b4-finetuned
This model is a fine-tuned version of nvidia/mit-b4 on the Hasano20/Set1 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for Hasano20/segformer-b4-finetuned
Base model
nvidia/mit-b4