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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: xyz
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: 0.9101851851851852
---
<!-- 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. -->
# xyz
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3224
- Accuracy: 0.9102
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2269 | 0.37 | 100 | 1.2058 | 0.6019 |
| 1.0891 | 0.74 | 200 | 0.9254 | 0.7046 |
| 0.5328 | 1.11 | 300 | 0.7417 | 0.7741 |
| 0.5259 | 1.48 | 400 | 0.7145 | 0.7722 |
| 0.4889 | 1.85 | 500 | 0.5621 | 0.825 |
| 0.2753 | 2.22 | 600 | 0.5251 | 0.8444 |
| 0.2569 | 2.59 | 700 | 0.5792 | 0.8259 |
| 0.2251 | 2.96 | 800 | 0.4169 | 0.8731 |
| 0.086 | 3.33 | 900 | 0.4182 | 0.8843 |
| 0.1352 | 3.7 | 1000 | 0.3711 | 0.8880 |
| 0.0608 | 4.07 | 1100 | 0.3430 | 0.9046 |
| 0.0175 | 4.44 | 1200 | 0.3241 | 0.9185 |
| 0.0149 | 4.81 | 1300 | 0.3224 | 0.9102 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|