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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: segformer-class-classWeights-augmentation
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: 0.9655172413793104
- name: F1
type: f1
value: 0.964683592269799
- name: Precision
type: precision
value: 0.9674329501915708
- name: Recall
type: recall
value: 0.9655172413793104
---
<!-- 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. -->
# segformer-class-classWeights-augmentation
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: 0.1855
- Accuracy: 0.9655
- F1: 0.9647
- Precision: 0.9674
- Recall: 0.9655
- Learning Rate: 0.0000
## 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: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log | 0.89 | 6 | 0.1113 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1153 | 1.93 | 13 | 0.0929 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.2246 | 2.96 | 20 | 0.1026 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.2246 | 4.0 | 27 | 0.0391 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1433 | 4.89 | 33 | 0.0673 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1816 | 5.93 | 40 | 0.0794 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1816 | 6.96 | 47 | 0.0687 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1448 | 8.0 | 54 | 0.1123 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
| 0.1124 | 8.89 | 60 | 0.1855 | 0.9655 | 0.9647 | 0.9674 | 0.9655 | 0.0000 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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