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---
license: apache-2.0
base_model: Zetatech/pvt-tiny-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: pvt-tiny-224
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7833333333333333
- name: Precision
type: precision
value: 0.7680555555555556
- name: Recall
type: recall
value: 0.7833333333333333
---
<!-- 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. -->
# pvt-tiny-224
This model is a fine-tuned version of [Zetatech/pvt-tiny-224](https://huggingface.co/Zetatech/pvt-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4869
- Accuracy: 0.7833
- Precision: 0.7681
- Recall: 0.7833
- F1 Score: 0.7632
## 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: 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.5984 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 2.0 | 8 | 0.6103 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 3.0 | 12 | 0.5861 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 4.0 | 16 | 0.5478 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 5.0 | 20 | 0.5961 | 0.725 | 0.7119 | 0.725 | 0.7171 |
| No log | 6.0 | 24 | 0.5317 | 0.7542 | 0.7261 | 0.7542 | 0.7159 |
| No log | 7.0 | 28 | 0.5620 | 0.7458 | 0.7289 | 0.7458 | 0.7342 |
| 0.5878 | 8.0 | 32 | 0.5281 | 0.7542 | 0.7316 | 0.7542 | 0.6973 |
| 0.5878 | 9.0 | 36 | 0.5434 | 0.7625 | 0.7395 | 0.7625 | 0.7368 |
| 0.5878 | 10.0 | 40 | 0.5236 | 0.775 | 0.7658 | 0.775 | 0.7321 |
| 0.5878 | 11.0 | 44 | 0.5411 | 0.7542 | 0.7382 | 0.7542 | 0.7429 |
| 0.5878 | 12.0 | 48 | 0.5186 | 0.7708 | 0.7507 | 0.7708 | 0.7460 |
| 0.5878 | 13.0 | 52 | 0.5194 | 0.7667 | 0.7500 | 0.7667 | 0.7533 |
| 0.5878 | 14.0 | 56 | 0.5049 | 0.7875 | 0.7739 | 0.7875 | 0.7621 |
| 0.4973 | 15.0 | 60 | 0.5125 | 0.7833 | 0.7691 | 0.7833 | 0.7709 |
| 0.4973 | 16.0 | 64 | 0.5000 | 0.7917 | 0.7804 | 0.7917 | 0.7656 |
| 0.4973 | 17.0 | 68 | 0.5137 | 0.7583 | 0.7560 | 0.7583 | 0.7571 |
| 0.4973 | 18.0 | 72 | 0.4833 | 0.8 | 0.788 | 0.8 | 0.7833 |
| 0.4973 | 19.0 | 76 | 0.4929 | 0.7917 | 0.7816 | 0.7917 | 0.7843 |
| 0.4973 | 20.0 | 80 | 0.4858 | 0.8042 | 0.7930 | 0.8042 | 0.7887 |
| 0.4973 | 21.0 | 84 | 0.4900 | 0.7917 | 0.7777 | 0.7917 | 0.7743 |
| 0.4973 | 22.0 | 88 | 0.4886 | 0.7958 | 0.7829 | 0.7958 | 0.7815 |
| 0.439 | 23.0 | 92 | 0.4841 | 0.7917 | 0.7778 | 0.7917 | 0.7723 |
| 0.439 | 24.0 | 96 | 0.4855 | 0.8 | 0.7883 | 0.8 | 0.7885 |
| 0.439 | 25.0 | 100 | 0.4856 | 0.8 | 0.7879 | 0.8 | 0.7869 |
| 0.439 | 26.0 | 104 | 0.4839 | 0.8 | 0.7879 | 0.8 | 0.7869 |
| 0.439 | 27.0 | 108 | 0.4811 | 0.8 | 0.7879 | 0.8 | 0.7869 |
| 0.439 | 28.0 | 112 | 0.4834 | 0.8 | 0.7889 | 0.8 | 0.7901 |
| 0.439 | 29.0 | 116 | 0.4839 | 0.8 | 0.7889 | 0.8 | 0.7901 |
| 0.4092 | 30.0 | 120 | 0.4838 | 0.8 | 0.7889 | 0.8 | 0.7901 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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