File size: 2,048 Bytes
7044d59 0b3898f 7044d59 b909047 7044d59 3ca8667 7044d59 0b3898f 7044d59 a30051c 7044d59 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: glacier_segmentation_transformer
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. -->
# glacier_segmentation_transformer
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0488
- Mean Iou: 0.9289
- Mean Accuracy: 0.9613
- Overall Accuracy: 0.9689
- Per Category Iou: [0.9484225100312303, 0.875795429449281, 0.9626254685949275]
- Per Category Accuracy: [0.9725549195530643, 0.9263729934338871, 0.9850380570292713]
## 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: 0.00018
- train_batch_size: 100
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------------------------:|:------------------------------------------------------------:|
| 0.1259 | 1.0 | 1405 | 0.0488 | 0.9289 | 0.9613 | 0.9689 | [0.9484225100312303, 0.875795429449281, 0.9626254685949275] | [0.9725549195530643, 0.9263729934338871, 0.9850380570292713] |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|