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---
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
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