Instructions to use varcoder/Augmented-MIT-b5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/Augmented-MIT-b5 with Transformers:
# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("varcoder/Augmented-MIT-b5") model = SegformerForSemanticSegmentation.from_pretrained("varcoder/Augmented-MIT-b5") - Notebooks
- Google Colab
- Kaggle
| license: other | |
| base_model: nvidia/mit-b5 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: Augmented-MIT-b5 | |
| 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. --> | |
| # Augmented-MIT-b5 | |
| This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0371 | |
| - Mean Iou: 0.3355 | |
| - Mean Accuracy: 0.6711 | |
| - Overall Accuracy: 0.6711 | |
| - Accuracy Background: nan | |
| - Accuracy Crack: 0.6711 | |
| - Iou Background: 0.0 | |
| - Iou Crack: 0.6711 | |
| ## 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: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:| | |
| | 0.0365 | 0.14 | 1000 | 0.0446 | 0.3813 | 0.7627 | 0.7627 | nan | 0.7627 | 0.0 | 0.7627 | | |
| | 0.0114 | 0.27 | 2000 | 0.0411 | 0.3691 | 0.7381 | 0.7381 | nan | 0.7381 | 0.0 | 0.7381 | | |
| | 0.0148 | 0.41 | 3000 | 0.0400 | 0.3224 | 0.6448 | 0.6448 | nan | 0.6448 | 0.0 | 0.6448 | | |
| | 0.0134 | 0.54 | 4000 | 0.0413 | 0.2819 | 0.5638 | 0.5638 | nan | 0.5638 | 0.0 | 0.5638 | | |
| | 0.013 | 0.68 | 5000 | 0.0392 | 0.3618 | 0.7235 | 0.7235 | nan | 0.7235 | 0.0 | 0.7235 | | |
| | 0.0532 | 0.81 | 6000 | 0.0373 | 0.3355 | 0.6710 | 0.6710 | nan | 0.6710 | 0.0 | 0.6710 | | |
| | 0.0508 | 0.95 | 7000 | 0.0371 | 0.3355 | 0.6711 | 0.6711 | nan | 0.6711 | 0.0 | 0.6711 | | |
| ### Framework versions | |
| - Transformers 4.31.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.4 | |
| - Tokenizers 0.13.3 | |