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---
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-FINALConcreteClassifier-SWIN50epochsAUGMENTED
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: 1.0
- name: F1
type: f1
value:
f1: 1.0
- name: Precision
type: precision
value:
precision: 1.0
- name: Recall
type: recall
value:
recall: 1.0
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-FINALConcreteClassifier-SWIN50epochsAUGMENTED
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: {'accuracy': 1.0}
- F1: {'f1': 1.0}
- Precision: {'precision': 1.0}
- Recall: {'recall': 1.0}
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:-----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
| 0.3875 | 0.9994 | 407 | 0.2752 | {'accuracy': 0.9272224781206817} | {'f1': 0.9299076962468851} | {'precision': 0.9308936484753314} | {'recall': 0.9298532516284993} |
| 0.2001 | 1.9988 | 814 | 0.0583 | {'accuracy': 0.983110701673576} | {'f1': 0.9837765293059086} | {'precision': 0.9846788595224822} | {'recall': 0.9836211079426627} |
| 0.1626 | 2.9982 | 1221 | 0.0207 | {'accuracy': 0.9938584369722094} | {'f1': 0.9941597712458348} | {'precision': 0.9943896461187967} | {'recall': 0.9941051527238169} |
| 0.088 | 4.0 | 1629 | 0.0088 | {'accuracy': 0.9969292184861047} | {'f1': 0.9970539871142889} | {'precision': 0.9970656946831583} | {'recall': 0.9970776666292009} |
| 0.1079 | 4.9994 | 2036 | 0.0046 | {'accuracy': 0.9987716873944419} | {'f1': 0.9988142853329625} | {'precision': 0.9988066339632395} | {'recall': 0.99882263684388} |
| 0.102 | 5.9988 | 2443 | 0.0034 | {'accuracy': 0.9989252264701366} | {'f1': 0.9989565946802677} | {'precision': 0.998933981872335} | {'recall': 0.9989857043158454} |
| 0.0594 | 6.9982 | 2850 | 0.0118 | {'accuracy': 0.9972362966374942} | {'f1': 0.9973346644159505} | {'precision': 0.9973144572332442} | {'recall': 0.9974051297029489} |
| 0.0335 | 8.0 | 3258 | 0.0030 | {'accuracy': 0.9987716873944419} | {'f1': 0.9988034164628696} | {'precision': 0.9987863396601946} | {'recall': 0.9988260749455921} |
| 0.0368 | 8.9994 | 3665 | 0.0036 | {'accuracy': 0.9990787655458314} | {'f1': 0.999110823927686} | {'precision': 0.99909200968523} | {'recall': 0.9991359447004609} |
| 0.0564 | 9.9988 | 4072 | 0.0040 | {'accuracy': 0.9984646092430524} | {'f1': 0.998509715288995} | {'precision': 0.9984881711855396} | {'recall': 0.9985402551521871} |
| 0.052 | 10.9982 | 4479 | 0.0021 | {'accuracy': 0.9989252264701366} | {'f1': 0.998956584824745} | {'precision': 0.9989419496612204} | {'recall': 0.9989777168523596} |
| 0.0429 | 12.0 | 4887 | 0.0033 | {'accuracy': 0.9983110701673575} | {'f1': 0.9983570515623278} | {'precision': 0.9984174575960668} | {'recall': 0.9983115930842853} |
| 0.047 | 12.9994 | 5294 | 0.0008 | {'accuracy': 0.9998464609243052} | {'f1': 0.9998504202011455} | {'precision': 0.9998534583821805} | {'recall': 0.9998475609756098} |
| 0.0391 | 13.9988 | 5701 | 0.0005 | {'accuracy': 0.9998464609243052} | {'f1': 0.999851770829272} | {'precision': 0.9998561565017261} | {'recall': 0.9998475609756098} |
| 0.0499 | 14.9982 | 6108 | 0.0011 | {'accuracy': 0.9995393827729157} | {'f1': 0.9995512387635233} | {'precision': 0.9995614035087719} | {'recall': 0.9995426829268292} |
| 0.0351 | 16.0 | 6516 | 0.0003 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.021 | 16.9994 | 6923 | 0.0054 | {'accuracy': 0.9984646092430524} | {'f1': 0.9985038406196534} | {'precision': 0.9985498839907192} | {'recall': 0.9984756097560976} |
| 0.0384 | 17.9988 | 7330 | 0.0004 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0093 | 18.9982 | 7737 | 0.0007 | {'accuracy': 0.9995393827729157} | {'f1': 0.999555371210602} | {'precision': 0.9995443499392467} | {'recall': 0.9995679723502304} |
| 0.0264 | 20.0 | 8145 | 0.0004 | {'accuracy': 0.9998464609243052} | {'f1': 0.9998528788154148} | {'precision': 0.9998499399759904} | {'recall': 0.9998559907834101} |
| 0.0191 | 20.9994 | 8552 | 0.0002 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.05 | 21.9988 | 8959 | 0.0002 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0155 | 22.9982 | 9366 | 0.0003 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0164 | 24.0 | 9774 | 0.0038 | {'accuracy': 0.9987716873944419} | {'f1': 0.998813860406548} | {'precision': 0.9988584474885844} | {'recall': 0.998780487804878} |
| 0.0202 | 24.9994 | 10181 | 0.0004 | {'accuracy': 0.9998464609243052} | {'f1': 0.9998504202011455} | {'precision': 0.9998534583821805} | {'recall': 0.9998475609756098} |
| 0.0576 | 25.9988 | 10588 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0098 | 26.9982 | 10995 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0091 | 28.0 | 11403 | 0.0001 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0259 | 28.9994 | 11810 | 0.0004 | {'accuracy': 0.9995393827729157} | {'f1': 0.999555371210602} | {'precision': 0.9995443499392467} | {'recall': 0.9995679723502304} |
| 0.0064 | 29.9988 | 12217 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0097 | 30.9982 | 12624 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0102 | 32.0 | 13032 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0082 | 32.9994 | 13439 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0094 | 33.9988 | 13846 | 0.0002 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0085 | 34.9982 | 14253 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0079 | 36.0 | 14661 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.006 | 36.9994 | 15068 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0039 | 37.9988 | 15475 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.023 | 38.9982 | 15882 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0026 | 40.0 | 16290 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0289 | 40.9994 | 16697 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0026 | 41.9988 | 17104 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0155 | 42.9982 | 17511 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0016 | 44.0 | 17919 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0005 | 44.9994 | 18326 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0058 | 45.9988 | 18733 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0012 | 46.9982 | 19140 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.001 | 48.0 | 19548 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0016 | 48.9994 | 19955 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
| 0.0015 | 49.9693 | 20350 | 0.0000 | {'accuracy': 1.0} | {'f1': 1.0} | {'precision': 1.0} | {'recall': 1.0} |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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