Instructions to use HIMMPT/my-fine-tuned-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HIMMPT/my-fine-tuned-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="HIMMPT/my-fine-tuned-model")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("HIMMPT/my-fine-tuned-model") model = SegformerForSemanticSegmentation.from_pretrained("HIMMPT/my-fine-tuned-model") - Notebooks
- Google Colab
- Kaggle
my-fine-tuned-model
This model is a fine-tuned version of nvidia/segformer-b1-finetuned-ade-512-512 on the segments/sidewalk-semantic 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Framework versions
- Transformers 4.48.0
- Pytorch 2.1.1+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
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Model tree for HIMMPT/my-fine-tuned-model
Base model
nvidia/segformer-b1-finetuned-ade-512-512