transformers tensorboard safetensors swin image-classification generated_from_trainer dataset:imagefolder base_model:weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-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: 0.8350594553126198
---

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

This model is a fine-tuned version of [weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification](https://huggingface.co/weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4259
- Accuracy: 0.8351

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4225        | 1.0   | 183  | 0.3415          | 0.8703   |
| 0.4843        | 2.0   | 367  | 0.4024          | 0.8381   |
| 0.4679        | 3.0   | 550  | 0.4014          | 0.8385   |
| 0.4431        | 4.0   | 734  | 0.3986          | 0.8331   |
| 0.4263        | 5.0   | 917  | 0.4119          | 0.8351   |
| 0.3869        | 6.0   | 1101 | 0.4217          | 0.8278   |
| 0.3432        | 7.0   | 1284 | 0.4229          | 0.8305   |
| 0.3522        | 8.0   | 1468 | 0.4283          | 0.8347   |
| 0.3337        | 9.0   | 1651 | 0.4180          | 0.8301   |
| 0.2963        | 10.0  | 1835 | 0.4219          | 0.8366   |
| 0.3025        | 11.0  | 2018 | 0.4236          | 0.8335   |
| 0.2751        | 12.0  | 2202 | 0.4238          | 0.8366   |
| 0.271         | 13.0  | 2385 | 0.4314          | 0.8324   |
| 0.2416        | 14.0  | 2569 | 0.4229          | 0.8328   |
| 0.2507        | 14.96 | 2745 | 0.4259          | 0.8351   |


### Framework versions

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