File size: 2,465 Bytes
f1aa805 |
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 64 65 66 67 68 69 |
---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: segcrack9k_conglomerate_train_test
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. -->
# segcrack9k_conglomerate_train_test
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0298
- Mean Iou: 0.3639
- Mean Accuracy: 0.7278
- Overall Accuracy: 0.7278
- Accuracy Background: nan
- Accuracy Crack: 0.7278
- Iou Background: 0.0
- Iou Crack: 0.7278
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.0374 | 0.18 | 1000 | 0.0410 | 0.2472 | 0.4944 | 0.4944 | nan | 0.4944 | 0.0 | 0.4944 |
| 0.0337 | 0.36 | 2000 | 0.0341 | 0.3749 | 0.7497 | 0.7497 | nan | 0.7497 | 0.0 | 0.7497 |
| 0.0209 | 0.55 | 3000 | 0.0318 | 0.3335 | 0.6670 | 0.6670 | nan | 0.6670 | 0.0 | 0.6670 |
| 0.0099 | 0.73 | 4000 | 0.0315 | 0.3371 | 0.6743 | 0.6743 | nan | 0.6743 | 0.0 | 0.6743 |
| 0.026 | 0.91 | 5000 | 0.0298 | 0.3639 | 0.7278 | 0.7278 | nan | 0.7278 | 0.0 | 0.7278 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|