Instructions to use atitaarora/segformer-b0-scene-parse-150 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atitaarora/segformer-b0-scene-parse-150 with Transformers:
# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("atitaarora/segformer-b0-scene-parse-150") model = SegformerForSemanticSegmentation.from_pretrained("atitaarora/segformer-b0-scene-parse-150") - Notebooks
- Google Colab
- Kaggle
| license: other | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - scene_parse_150 | |
| model-index: | |
| - name: segformer-b0-scene-parse-150 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # segformer-b0-scene-parse-150 | |
| This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 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: 50 | |
| ### Framework versions | |
| - Transformers 4.28.1 | |
| - Pytorch 2.0.0+cu118 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.13.3 | |