transformers tensorboard safetensors swin image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swin-tiny-patch4-window7-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5874543393374481
---

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# cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough

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: 0.9991
- Accuracy: 0.5875

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5983        | 1.0   | 1240  | 1.3399          | 0.4436   |
| 1.5587        | 2.0   | 2481  | 1.3298          | 0.4366   |
| 1.4955        | 3.0   | 3721  | 1.1679          | 0.5129   |
| 1.4884        | 4.0   | 4962  | 1.1331          | 0.5299   |
| 1.4413        | 5.0   | 6202  | 1.1286          | 0.5287   |
| 1.4395        | 6.0   | 7443  | 1.1316          | 0.5226   |
| 1.4497        | 7.0   | 8683  | 1.2127          | 0.4844   |
| 1.3988        | 8.0   | 9924  | 1.1119          | 0.5301   |
| 1.4457        | 9.0   | 11164 | 1.0984          | 0.5389   |
| 1.4153        | 10.0  | 12405 | 1.1226          | 0.5269   |
| 1.3962        | 11.0  | 13645 | 1.0610          | 0.5573   |
| 1.3911        | 12.0  | 14886 | 1.0540          | 0.5595   |
| 1.3617        | 13.0  | 16126 | 1.0646          | 0.5530   |
| 1.3766        | 14.0  | 17367 | 1.0722          | 0.5532   |
| 1.3693        | 15.0  | 18607 | 1.0243          | 0.5721   |
| 1.3624        | 16.0  | 19848 | 1.0212          | 0.5763   |
| 1.3638        | 17.0  | 21088 | 1.0667          | 0.5580   |
| 1.4007        | 18.0  | 22329 | 1.0314          | 0.5730   |
| 1.3415        | 19.0  | 23569 | 1.0191          | 0.5755   |
| 1.3802        | 20.0  | 24810 | 1.0142          | 0.5770   |
| 1.3572        | 21.0  | 26050 | 1.0125          | 0.5771   |
| 1.2962        | 22.0  | 27291 | 1.0167          | 0.5763   |
| 1.2831        | 23.0  | 28531 | 1.0043          | 0.5829   |
| 1.3272        | 24.0  | 29772 | 0.9990          | 0.5858   |
| 1.3197        | 25.0  | 31012 | 1.0033          | 0.5830   |
| 1.3203        | 26.0  | 32253 | 1.0075          | 0.5818   |
| 1.3172        | 27.0  | 33493 | 1.0008          | 0.5852   |
| 1.3197        | 28.0  | 34734 | 1.0016          | 0.5847   |
| 1.2879        | 29.0  | 35974 | 1.0017          | 0.5867   |
| 1.2907        | 29.99 | 37200 | 0.9991          | 0.5875   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
