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---
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: 0.0787
- Validation Loss: 0.1007
- Validation Mean Iou: 0.7646
- Validation Mean Accuracy: 0.8701
- Validation Overall Accuracy: 0.9687
- Validation Accuracy Unlabeled: 0.6791
- Validation Accuracy Objeto Interes: 0.9475
- Validation Accuracy Agua: 0.9838
- Validation Iou Unlabeled: 0.5173
- Validation Iou Objeto Interes: 0.8005
- Validation Iou Agua: 0.9760
- Epoch: 39
## 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 |
| 0.1645 | 0.1638 | 0.7110 | 0.8204 | 0.9586 | 0.6026 | 0.8779 | 0.9808 | 0.4292 | 0.7369 | 0.9670 | 8 |
| 0.1574 | 0.1359 | 0.7140 | 0.8058 | 0.9616 | 0.5380 | 0.8933 | 0.9861 | 0.4197 | 0.7527 | 0.9695 | 9 |
| 0.1737 | 0.1421 | 0.7075 | 0.8042 | 0.9596 | 0.5320 | 0.8965 | 0.9841 | 0.4037 | 0.7513 | 0.9675 | 10 |
| 0.1608 | 0.1613 | 0.7046 | 0.8348 | 0.9564 | 0.6010 | 0.9285 | 0.9750 | 0.4156 | 0.7325 | 0.9655 | 11 |
| 0.1425 | 0.1387 | 0.7268 | 0.8355 | 0.9618 | 0.6140 | 0.9109 | 0.9816 | 0.4499 | 0.7605 | 0.9698 | 12 |
| 0.1299 | 0.1230 | 0.7198 | 0.8184 | 0.9628 | 0.5475 | 0.9225 | 0.9851 | 0.4286 | 0.7595 | 0.9714 | 13 |
| 0.1286 | 0.1279 | 0.7267 | 0.8320 | 0.9630 | 0.5856 | 0.9270 | 0.9833 | 0.4473 | 0.7614 | 0.9715 | 14 |
| 0.1322 | 0.1201 | 0.7428 | 0.8380 | 0.9651 | 0.6334 | 0.8954 | 0.9854 | 0.4772 | 0.7791 | 0.9722 | 15 |
| 0.1203 | 0.1076 | 0.7439 | 0.8294 | 0.9663 | 0.6001 | 0.9000 | 0.9880 | 0.4712 | 0.7872 | 0.9732 | 16 |
| 0.1154 | 0.1314 | 0.7417 | 0.8557 | 0.9633 | 0.6671 | 0.9198 | 0.9802 | 0.4752 | 0.7794 | 0.9706 | 17 |
| 0.1145 | 0.1098 | 0.7446 | 0.8438 | 0.9662 | 0.6183 | 0.9281 | 0.9852 | 0.4827 | 0.7770 | 0.9739 | 18 |
| 0.1131 | 0.0994 | 0.7500 | 0.8368 | 0.9676 | 0.6077 | 0.9145 | 0.9881 | 0.4834 | 0.7919 | 0.9748 | 19 |
| 0.1101 | 0.1157 | 0.7590 | 0.8657 | 0.9664 | 0.7130 | 0.9015 | 0.9827 | 0.5107 | 0.7928 | 0.9733 | 20 |
| 0.1045 | 0.1099 | 0.7513 | 0.8565 | 0.9664 | 0.6570 | 0.9288 | 0.9835 | 0.4959 | 0.7841 | 0.9739 | 21 |
| 0.1031 | 0.1045 | 0.7511 | 0.8522 | 0.9668 | 0.6398 | 0.9323 | 0.9846 | 0.4911 | 0.7878 | 0.9743 | 22 |
| 0.1038 | 0.1245 | 0.7335 | 0.8535 | 0.9628 | 0.6322 | 0.9488 | 0.9794 | 0.4609 | 0.7683 | 0.9713 | 23 |
| 0.0989 | 0.1130 | 0.7476 | 0.8608 | 0.9652 | 0.6641 | 0.9372 | 0.9813 | 0.4895 | 0.7805 | 0.9729 | 24 |
| 0.0961 | 0.0993 | 0.7534 | 0.8560 | 0.9672 | 0.6481 | 0.9356 | 0.9844 | 0.4949 | 0.7904 | 0.9748 | 25 |
| 0.0931 | 0.0977 | 0.7616 | 0.8574 | 0.9684 | 0.6623 | 0.9242 | 0.9858 | 0.5099 | 0.7995 | 0.9754 | 26 |
| 0.0913 | 0.0899 | 0.7685 | 0.8547 | 0.9701 | 0.6575 | 0.9184 | 0.9883 | 0.5192 | 0.8096 | 0.9768 | 27 |
| 0.0899 | 0.0984 | 0.7572 | 0.8550 | 0.9683 | 0.6393 | 0.9398 | 0.9858 | 0.5015 | 0.7940 | 0.9759 | 28 |
| 0.0918 | 0.1307 | 0.7440 | 0.8719 | 0.9635 | 0.6838 | 0.9545 | 0.9773 | 0.4872 | 0.7735 | 0.9713 | 29 |
| 0.0919 | 0.1239 | 0.7405 | 0.8590 | 0.9641 | 0.6442 | 0.9526 | 0.9801 | 0.4707 | 0.7784 | 0.9725 | 30 |
| 0.0925 | 0.0990 | 0.7699 | 0.8629 | 0.9696 | 0.6859 | 0.9163 | 0.9865 | 0.5271 | 0.8067 | 0.9761 | 31 |
| 0.0889 | 0.1069 | 0.7563 | 0.8708 | 0.9664 | 0.6864 | 0.9450 | 0.9811 | 0.5038 | 0.7913 | 0.9738 | 32 |
| 0.0836 | 0.0913 | 0.7707 | 0.8617 | 0.9702 | 0.6714 | 0.9265 | 0.9873 | 0.5265 | 0.8086 | 0.9770 | 33 |
| 0.0822 | 0.1041 | 0.7645 | 0.8788 | 0.9672 | 0.7170 | 0.9383 | 0.9809 | 0.5161 | 0.8035 | 0.9740 | 34 |
| 0.0803 | 0.0981 | 0.7699 | 0.8721 | 0.9691 | 0.6987 | 0.9334 | 0.9843 | 0.5291 | 0.8046 | 0.9759 | 35 |
| 0.0800 | 0.1018 | 0.7597 | 0.8681 | 0.9678 | 0.6728 | 0.9485 | 0.9830 | 0.5104 | 0.7935 | 0.9752 | 36 |
| 0.0779 | 0.0975 | 0.7727 | 0.8769 | 0.9692 | 0.7185 | 0.9286 | 0.9837 | 0.5349 | 0.8075 | 0.9757 | 37 |
| 0.0756 | 0.0984 | 0.7697 | 0.8742 | 0.9691 | 0.7003 | 0.9385 | 0.9838 | 0.5280 | 0.8051 | 0.9760 | 38 |
| 0.0787 | 0.1007 | 0.7646 | 0.8701 | 0.9687 | 0.6791 | 0.9475 | 0.9838 | 0.5173 | 0.8005 | 0.9760 | 39 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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