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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k_GI_diagnosis
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: 0.88125
---
<!-- 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-base-patch16-224-in21k_GI_diagnosis
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: 0.5797
- Accuracy: 0.8812
- Weighted f1: 0.8740
- Micro f1: 0.8812
- Macro f1: 0.8740
- Weighted recall: 0.8812
- Micro recall: 0.8812
- Macro recall: 0.8813
- Weighted precision: 0.9157
- Micro precision: 0.8812
- Macro precision: 0.9157
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.3805 | 1.0 | 200 | 0.5006 | 0.8638 | 0.8531 | 0.8638 | 0.8531 | 0.8638 | 0.8638 | 0.8638 | 0.9111 | 0.8638 | 0.9111 |
| 1.3805 | 2.0 | 400 | 0.2538 | 0.9375 | 0.9365 | 0.9375 | 0.9365 | 0.9375 | 0.9375 | 0.9375 | 0.9455 | 0.9375 | 0.9455 |
| 0.0628 | 3.0 | 600 | 0.5797 | 0.8812 | 0.8740 | 0.8812 | 0.8740 | 0.8812 | 0.8812 | 0.8813 | 0.9157 | 0.8812 | 0.9157 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.12.1
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