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---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: beit-base-patch16-224
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: 0.7933333333333333
- name: Precision
type: precision
value: 0.7853286177424108
- name: Recall
type: recall
value: 0.7933333333333333
---
<!-- 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. -->
# beit-base-patch16-224
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8531
- Accuracy: 0.7933
- Precision: 0.7853
- Recall: 0.7933
- F1 Score: 0.7662
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.5815 | 0.7292 | 0.6273 | 0.7292 | 0.6259 |
| No log | 2.0 | 8 | 0.5493 | 0.7333 | 0.6901 | 0.7333 | 0.6863 |
| No log | 3.0 | 12 | 0.5545 | 0.7667 | 0.7575 | 0.7667 | 0.7147 |
| 0.5698 | 4.0 | 16 | 0.5706 | 0.7667 | 0.7503 | 0.7667 | 0.7221 |
| 0.5698 | 5.0 | 20 | 0.5800 | 0.7667 | 0.7575 | 0.7667 | 0.7147 |
| 0.5698 | 6.0 | 24 | 0.5929 | 0.7833 | 0.7772 | 0.7833 | 0.7451 |
| 0.5698 | 7.0 | 28 | 0.5783 | 0.7833 | 0.7677 | 0.7833 | 0.7672 |
| 0.2938 | 8.0 | 32 | 0.5665 | 0.7875 | 0.7793 | 0.7875 | 0.7821 |
| 0.2938 | 9.0 | 36 | 0.7751 | 0.7875 | 0.7770 | 0.7875 | 0.7571 |
| 0.2938 | 10.0 | 40 | 0.7088 | 0.7917 | 0.7816 | 0.7917 | 0.7843 |
| 0.2938 | 11.0 | 44 | 0.8799 | 0.8042 | 0.7972 | 0.8042 | 0.7808 |
| 0.0834 | 12.0 | 48 | 0.8367 | 0.7875 | 0.7793 | 0.7875 | 0.7821 |
| 0.0834 | 13.0 | 52 | 0.9200 | 0.7958 | 0.7834 | 0.7958 | 0.7758 |
| 0.0834 | 14.0 | 56 | 0.8821 | 0.8 | 0.7879 | 0.8 | 0.7869 |
| 0.0358 | 15.0 | 60 | 0.8674 | 0.7875 | 0.7753 | 0.7875 | 0.7777 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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