---
license: mit
base_model: nvidia/mit-b0
tags:
- generated_from_keras_callback
model-index:
- name: slm-segformer-080823
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# slm-segformer-080823

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0357
- Validation Loss: 0.0383
- Validation Mean Iou: 0.8453
- Validation Mean Accuracy: 0.9366
- Validation Overall Accuracy: 0.9869
- Validation Per Category Iou: [0.98646921 0.70414361]
- Validation Per Category Accuracy: [0.99072207 0.88237991]
- Epoch: 9

## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Per Category Iou | Validation Per Category Accuracy | Epoch |
|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------------:|:-----:|
| 0.4798     | 0.1807          | 0.6747              | 0.7770                   | 0.9674                      | [0.96669254 0.38268484]     | [0.98185208 0.57215982]          | 0     |
| 0.1552     | 0.1046          | 0.7352              | 0.7991                   | 0.9779                      | [0.97745298 0.49298956]     | [0.99154204 0.60674898]          | 1     |
| 0.0981     | 0.1042          | 0.7744              | 0.9090                   | 0.9779                      | [0.97719564 0.5715319 ]     | [0.98310851 0.8349177 ]          | 2     |
| 0.0744     | 0.0978          | 0.7876              | 0.9431                   | 0.9784                      | [0.97773288 0.59755377]     | [0.98113179 0.90515736]          | 3     |
| 0.0611     | 0.0728          | 0.8224              | 0.9456                   | 0.9836                      | [0.98310869 0.66170563]     | [0.98654807 0.90455283]          | 4     |
| 0.0513     | 0.0531          | 0.8330              | 0.9282                   | 0.9856                      | [0.98518512 0.68084932]     | [0.99000668 0.86647783]          | 5     |
| 0.0469     | 0.0514          | 0.8326              | 0.9460                   | 0.9850                      | [0.98451475 0.68075519]     | [0.9879771  0.90405278]          | 6     |
| 0.0413     | 0.0406          | 0.8452              | 0.9360                   | 0.9869                      | [0.9864742  0.70392259]     | [0.99077125 0.88115845]          | 7     |
| 0.0385     | 0.0412          | 0.8495              | 0.9309                   | 0.9875                      | [0.98715291 0.71182272]     | [0.99186047 0.86989475]          | 8     |
| 0.0357     | 0.0383          | 0.8453              | 0.9366                   | 0.9869                      | [0.98646921 0.70414361]     | [0.99072207 0.88237991]          | 9     |


### Framework versions

- Transformers 4.31.0
- TensorFlow 2.12.0
- Tokenizers 0.13.3