---
license: apache-2.0
base_model: microsoft/cvt-13
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: cvt-13-finetuned-flower
  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: 0.9368421052631579
    - name: Precision
      type: precision
      value: 0.9374630861809764
    - name: Recall
      type: recall
      value: 0.9368421052631579
    - name: F1
      type: f1
      value: 0.9341589949056075
---

<!-- 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. -->

# cvt-13-finetuned-flower

This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2151
- Accuracy: 0.9368
- Precision: 0.9375
- Recall: 0.9368
- F1: 0.9342

## 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: 0.005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0555        | 1.0   | 40   | 0.3933          | 0.8766   | 0.8828    | 0.8766 | 0.8713 |
| 1.1941        | 2.0   | 80   | 1.0797          | 0.6726   | 0.7515    | 0.6726 | 0.6546 |
| 1.2286        | 3.0   | 120  | 0.8459          | 0.7347   | 0.7820    | 0.7347 | 0.7343 |
| 1.209         | 4.0   | 160  | 0.6660          | 0.7880   | 0.8173    | 0.7880 | 0.7833 |
| 1.1158        | 5.0   | 200  | 0.7348          | 0.7597   | 0.7809    | 0.7597 | 0.7561 |
| 1.1113        | 6.0   | 240  | 0.6387          | 0.8062   | 0.8164    | 0.8062 | 0.7986 |
| 1.0332        | 7.0   | 280  | 0.6555          | 0.7887   | 0.8064    | 0.7887 | 0.7831 |
| 1.0234        | 8.0   | 320  | 0.5776          | 0.8276   | 0.8447    | 0.8276 | 0.8177 |
| 0.9997        | 9.0   | 360  | 0.5784          | 0.8214   | 0.8421    | 0.8214 | 0.8169 |
| 0.9421        | 10.0  | 400  | 0.4667          | 0.8486   | 0.8600    | 0.8486 | 0.8453 |
| 0.9057        | 11.0  | 440  | 0.4508          | 0.8541   | 0.8711    | 0.8541 | 0.8487 |
| 0.8662        | 12.0  | 480  | 0.3517          | 0.8911   | 0.8938    | 0.8911 | 0.8868 |
| 0.8341        | 13.0  | 520  | 0.3191          | 0.8976   | 0.9021    | 0.8976 | 0.8945 |
| 0.757         | 14.0  | 560  | 0.2785          | 0.9183   | 0.9199    | 0.9183 | 0.9144 |
| 0.7906        | 15.0  | 600  | 0.2698          | 0.9201   | 0.9218    | 0.9201 | 0.9172 |
| 0.7464        | 16.0  | 640  | 0.2594          | 0.9216   | 0.9232    | 0.9216 | 0.9188 |
| 0.7335        | 17.0  | 680  | 0.2491          | 0.9263   | 0.9281    | 0.9263 | 0.9240 |
| 0.7085        | 18.0  | 720  | 0.2396          | 0.9303   | 0.9304    | 0.9303 | 0.9272 |
| 0.7177        | 19.0  | 760  | 0.2171          | 0.9350   | 0.9355    | 0.9350 | 0.9321 |
| 0.6735        | 20.0  | 800  | 0.2151          | 0.9368   | 0.9375    | 0.9368 | 0.9342 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.15.2