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: swin-tiny-patch4-window7-224-finetuned-plant-classification
  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: 0.7557471264367817
---

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# swin-tiny-patch4-window7-224-finetuned-plant-classification

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: 0.6592
- Accuracy: 0.7557

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8257        | 1.0   | 268  | 0.7941          | 0.6695   |
| 0.7235        | 2.0   | 537  | 0.7696          | 0.6695   |
| 0.6939        | 3.0   | 806  | 0.7428          | 0.6724   |
| 0.665         | 4.0   | 1075 | 0.6884          | 0.7328   |
| 0.6846        | 5.0   | 1343 | 0.7144          | 0.6954   |
| 0.6391        | 6.0   | 1612 | 0.6854          | 0.7155   |
| 0.6172        | 7.0   | 1881 | 0.6698          | 0.7011   |
| 0.6332        | 8.0   | 2150 | 0.6510          | 0.7126   |
| 0.5679        | 9.0   | 2418 | 0.6323          | 0.7299   |
| 0.5109        | 10.0  | 2687 | 0.6629          | 0.7098   |
| 0.5594        | 11.0  | 2956 | 0.6556          | 0.7270   |
| 0.4874        | 12.0  | 3225 | 0.6627          | 0.7155   |
| 0.4687        | 13.0  | 3493 | 0.6645          | 0.7299   |
| 0.4686        | 14.0  | 3762 | 0.6469          | 0.7213   |
| 0.4862        | 15.0  | 4031 | 0.6602          | 0.7356   |
| 0.4432        | 16.0  | 4300 | 0.6550          | 0.7270   |
| 0.4368        | 17.0  | 4568 | 0.6472          | 0.7385   |
| 0.3815        | 18.0  | 4837 | 0.6557          | 0.7557   |
| 0.3674        | 19.0  | 5106 | 0.6638          | 0.7529   |
| 0.4224        | 19.94 | 5360 | 0.6592          | 0.7557   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
