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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: segformer-class-custom-train
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: 0.9772727272727273
---
<!-- 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-custom-train
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: 0.0588
- Accuracy: 0.9773
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 6 | 0.9547 | 0.6364 |
| 1.0647 | 1.92 | 12 | 0.5731 | 0.8636 |
| 1.0647 | 2.88 | 18 | 0.3149 | 0.9091 |
| 0.5705 | 4.0 | 25 | 0.0585 | 0.9773 |
| 0.2274 | 4.96 | 31 | 0.0815 | 0.9773 |
| 0.2274 | 5.92 | 37 | 0.0824 | 0.9773 |
| 0.1822 | 6.88 | 43 | 0.1408 | 0.9773 |
| 0.1784 | 8.0 | 50 | 0.0778 | 0.9773 |
| 0.1784 | 8.96 | 56 | 0.0613 | 0.9773 |
| 0.117 | 9.6 | 60 | 0.0588 | 0.9773 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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