---
license: other
base_model: nvidia/mit-b1
tags:
- generated_from_keras_callback
model-index:
- name: Lit4pCol4b/mit-b1_segformer_ADE20k_RGB_IS_v1
  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. -->

# Lit4pCol4b/mit-b1_segformer_ADE20k_RGB_IS_v1

This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1835
- Validation Loss: 0.2237
- Validation Mean Iou: 0.6866
- Validation Mean Accuracy: 0.8243
- Validation Overall Accuracy: 0.9510
- Validation Accuracy Unlabeled: 0.5945
- Validation Accuracy Objeto Interes: 0.9077
- Validation Accuracy Agua: 0.9707
- Validation Iou Unlabeled: 0.3844
- Validation Iou Objeto Interes: 0.7151
- Validation Iou Agua: 0.9601
- Epoch: 7

## 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 Accuracy Unlabeled | Validation Accuracy Objeto Interes | Validation Accuracy Agua | Validation Iou Unlabeled | Validation Iou Objeto Interes | Validation Iou Agua | Epoch |
|:----------:|:---------------:|:-------------------:|:------------------------:|:---------------------------:|:-----------------------------:|:----------------------------------:|:------------------------:|:------------------------:|:-----------------------------:|:-------------------:|:-----:|
| 0.6580     | 0.6718          | 0.4196              | 0.6281                   | 0.8767                      | 0.0254                        | 0.9466                             | 0.9124                   | 0.0232                   | 0.3345                        | 0.9012              | 0     |
| 0.4551     | 0.5040          | 0.5131              | 0.6832                   | 0.9126                      | 0.1813                        | 0.9216                             | 0.9467                   | 0.1234                   | 0.4837                        | 0.9322              | 1     |
| 0.3472     | 0.2565          | 0.5381              | 0.6560                   | 0.9375                      | 0.1035                        | 0.8839                             | 0.9805                   | 0.0930                   | 0.5671                        | 0.9542              | 2     |
| 0.2846     | 0.2434          | 0.6188              | 0.7343                   | 0.9442                      | 0.3415                        | 0.8847                             | 0.9767                   | 0.2514                   | 0.6486                        | 0.9564              | 3     |
| 0.2383     | 0.2245          | 0.6401              | 0.7568                   | 0.9469                      | 0.4203                        | 0.8735                             | 0.9767                   | 0.2975                   | 0.6644                        | 0.9586              | 4     |
| 0.2075     | 0.2243          | 0.6606              | 0.7809                   | 0.9501                      | 0.4690                        | 0.8975                             | 0.9764                   | 0.3332                   | 0.6879                        | 0.9608              | 5     |
| 0.1943     | 0.1820          | 0.6721              | 0.7704                   | 0.9559                      | 0.4301                        | 0.8964                             | 0.9847                   | 0.3423                   | 0.7083                        | 0.9658              | 6     |
| 0.1835     | 0.2237          | 0.6866              | 0.8243                   | 0.9510                      | 0.5945                        | 0.9077                             | 0.9707                   | 0.3844                   | 0.7151                        | 0.9601              | 7     |


### Framework versions

- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0