---
license: apache-2.0
base_model: Xrenya/pvt-small-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: pvt-small-224-ConcreteClassifier-PVT
  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:
        accuracy: 0.17665369649805449
    - name: F1
      type: f1
      value:
        f1: 0.04289493575207861
    - name: Precision
      type: precision
      value:
        precision: 0.025236242356864926
    - name: Recall
      type: recall
      value:
        recall: 0.14285714285714285
---

<!-- 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-small-224-ConcreteClassifier-PVT

This model is a fine-tuned version of [Xrenya/pvt-small-224](https://huggingface.co/Xrenya/pvt-small-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9419
- Accuracy: {'accuracy': 0.17665369649805449}
- F1: {'f1': 0.04289493575207861}
- Precision: {'precision': 0.025236242356864926}
- Recall: {'recall': 0.14285714285714285}

## 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: 0.001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy                          | F1                          | Precision                           | Recall                          |
|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------:|:---------------------------:|:-----------------------------------:|:-------------------------------:|
| 1.981         | 1.0   | 1927  | 1.9584          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.951         | 2.0   | 3854  | 1.9447          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.9799        | 3.0   | 5781  | 1.9498          | {'accuracy': 0.13618677042801555} | {'f1': 0.03424657534246575} | {'precision': 0.019455252918287935} | {'recall': 0.14285714285714285} |
| 1.9458        | 4.0   | 7708  | 1.9412          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9444        | 5.0   | 9635  | 1.9408          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9441        | 6.0   | 11562 | 1.9427          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9379        | 7.0   | 13489 | 1.9433          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.9529        | 8.0   | 15416 | 1.9432          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.9305        | 9.0   | 17343 | 1.9463          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.94          | 10.0  | 19270 | 1.9412          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.945         | 11.0  | 21197 | 1.9432          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.9294        | 12.0  | 23124 | 1.9444          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.9339        | 13.0  | 25051 | 1.9415          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.934         | 14.0  | 26978 | 1.9408          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9275        | 15.0  | 28905 | 1.9423          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9539        | 16.0  | 30832 | 1.9440          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9584        | 17.0  | 32759 | 1.9412          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9409        | 18.0  | 34686 | 1.9405          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.9522        | 19.0  | 36613 | 1.9405          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9296        | 20.0  | 38540 | 1.9410          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9272        | 21.0  | 40467 | 1.9412          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.9399        | 22.0  | 42394 | 1.9413          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9258        | 23.0  | 44321 | 1.9413          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9481        | 24.0  | 46248 | 1.9422          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.948         | 25.0  | 48175 | 1.9423          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.918         | 26.0  | 50102 | 1.9416          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.938         | 27.0  | 52029 | 1.9414          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9207        | 28.0  | 53956 | 1.9410          | {'accuracy': 0.1556420233463035}  | {'f1': 0.03848003848003848} | {'precision': 0.022234574763757644} | {'recall': 0.14285714285714285} |
| 1.9472        | 29.0  | 55883 | 1.9404          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |
| 1.9355        | 30.0  | 57810 | 1.9419          | {'accuracy': 0.17665369649805449} | {'f1': 0.04289493575207861} | {'precision': 0.025236242356864926} | {'recall': 0.14285714285714285} |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0
- Datasets 2.17.1
- Tokenizers 0.15.2