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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: pipe-failure_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# pipe-failure_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0255
- Accuracy: 1.0
## Model description
Image classification model using a pretrained Vision Transformer to categorize different types of pipe failures.
## Intended uses & limitations
Diagnostic for Failure on Pipe through image recognition
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 15 | 0.0516 | 0.9867 |
| No log | 2.0 | 30 | 0.0441 | 0.9867 |
| No log | 3.0 | 45 | 0.0497 | 0.9733 |
| No log | 4.0 | 60 | 0.0464 | 0.9867 |
| No log | 5.0 | 75 | 0.0677 | 0.9867 |
| No log | 6.0 | 90 | 0.0208 | 1.0 |
| No log | 7.0 | 105 | 0.0183 | 1.0 |
| No log | 8.0 | 120 | 0.0943 | 0.9733 |
| No log | 9.0 | 135 | 0.0216 | 1.0 |
| No log | 10.0 | 150 | 0.0148 | 1.0 |
| No log | 11.0 | 165 | 0.0144 | 1.0 |
| No log | 12.0 | 180 | 0.0188 | 1.0 |
| No log | 13.0 | 195 | 0.0602 | 0.9867 |
| No log | 14.0 | 210 | 0.0882 | 0.9733 |
| No log | 15.0 | 225 | 0.0314 | 0.9867 |
| No log | 16.0 | 240 | 0.0127 | 1.0 |
| No log | 17.0 | 255 | 0.0119 | 1.0 |
| No log | 18.0 | 270 | 0.0117 | 1.0 |
| No log | 19.0 | 285 | 0.0114 | 1.0 |
| No log | 20.0 | 300 | 0.0131 | 1.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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