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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
model-index:
- name: segmentation_model_50ep_2
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. -->
# segmentation_model_50ep_2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0151
- Mean Iou: 0.4992
- Mean Accuracy: 0.5002
- Overall Accuracy: 0.9980
- Per Category Iou: [0.9979567074182948, 0.0004395926441497546]
- Per Category Accuracy: [0.9999017103951866, 0.00046175157765122367]
## 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: 16
- eval_batch_size: 16
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------:|:--------------------------------------------:|
| 0.0176 | 12.1951 | 1000 | 0.0153 | 0.4991 | 0.5001 | 0.9978 | [0.9978437819175541, 0.00041657987919183504] | [0.9997885648043022, 0.00046175157765122367] |
| 0.0173 | 24.3902 | 2000 | 0.0153 | 0.4991 | 0.5001 | 0.9978 | [0.9978095148690534, 0.0004100657472081357] | [0.999754230969827, 0.00046175157765122367] |
| 0.0144 | 36.5854 | 3000 | 0.0146 | 0.4991 | 0.5001 | 0.9980 | [0.9979986133831826, 0.00026932399676811203] | [0.9999440574585123, 0.00027705094659073417] |
| 0.0208 | 48.7805 | 4000 | 0.0151 | 0.4992 | 0.5002 | 0.9980 | [0.9979567074182948, 0.0004395926441497546] | [0.9999017103951866, 0.00046175157765122367] |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.2.0
- Datasets 2.4.0
- Tokenizers 0.20.3
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