Instructions to use fshala/segformer-cloud with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fshala/segformer-cloud with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="fshala/segformer-cloud")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("fshala/segformer-cloud") model = SegformerForSemanticSegmentation.from_pretrained("fshala/segformer-cloud", device_map="auto") - Notebooks
- Google Colab
- Kaggle
segformer-cloud
This model is a fine-tuned version of nvidia/mit-b0 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
- 3
Model tree for fshala/segformer-cloud
Base model
nvidia/mit-b0