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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: vit-pushup-form-classifier
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: 0.9733333333333334
- name: F1
type: f1
value: 0.9733475783475783
- name: Precision
type: precision
value: 0.9737081183656526
- name: Recall
type: recall
value: 0.9733333333333334
---
<!-- 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. -->
# vit-pushup-form-classifier
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0633
- Accuracy: 0.9733
- F1: 0.9733
- Precision: 0.9737
- Recall: 0.9733
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2205 | 1.0 | 44 | 0.2602 | 0.8533 | 0.8532 | 0.8534 | 0.8533 |
| 0.1148 | 2.0 | 88 | 0.2138 | 0.9 | 0.9000 | 0.9 | 0.9 |
| 0.0869 | 3.0 | 132 | 0.2535 | 0.8933 | 0.8931 | 0.8942 | 0.8933 |
| 0.0592 | 4.0 | 176 | 0.1646 | 0.9 | 0.8997 | 0.9014 | 0.9 |
| 0.0559 | 5.0 | 220 | 0.1587 | 0.9267 | 0.9265 | 0.9283 | 0.9267 |
| 0.034 | 6.0 | 264 | 0.2178 | 0.9133 | 0.9129 | 0.9166 | 0.9133 |
| 0.0171 | 7.0 | 308 | 0.1712 | 0.9267 | 0.9266 | 0.9272 | 0.9267 |
| 0.0107 | 8.0 | 352 | 0.1740 | 0.9267 | 0.9265 | 0.9283 | 0.9267 |
| 0.0139 | 9.0 | 396 | 0.1631 | 0.9333 | 0.9332 | 0.9344 | 0.9333 |
| 0.0046 | 10.0 | 440 | 0.1692 | 0.9333 | 0.9331 | 0.9359 | 0.9333 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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