metadata
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swin-tiny-patch4-window7-224-crack-detector
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:
accuracy: 0.9384615384615385
- name: F1
type: f1
value:
f1: 0.9382975252490704
- name: Precision
type: precision
value:
precision: 0.9382005688460371
- name: Recall
type: recall
value:
recall: 0.9395073274524703
swin-tiny-patch4-window7-224-crack-detector
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1807
- Accuracy: {'accuracy': 0.9384615384615385}
- F1: {'f1': 0.9382975252490704}
- Precision: {'precision': 0.9382005688460371}
- Recall: {'recall': 0.9395073274524703}
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3863 | 1.0 | 195 | 0.3349 | {'accuracy': 0.885576923076923} | {'f1': 0.8829318618369404} | {'precision': 0.8830357915066687} | {'recall': 0.8864842943431257} |
| 0.2685 | 2.0 | 390 | 0.2715 | {'accuracy': 0.9080128205128205} | {'f1': 0.9106277459775055} | {'precision': 0.9130231253775549} | {'recall': 0.9148104520472664} |
| 0.2235 | 3.0 | 585 | 0.1807 | {'accuracy': 0.9384615384615385} | {'f1': 0.9382975252490704} | {'precision': 0.9382005688460371} | {'recall': 0.9395073274524703} |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0
- Datasets 2.17.1
- Tokenizers 0.15.2