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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: 0.85
- name: Precision
type: precision
value: 0.8455590062111802
- name: Recall
type: recall
value: 0.85
---
<!-- 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: 0.4871
- Accuracy: 0.85
- Precision: 0.8456
- Recall: 0.85
- F1 Score: 0.8464
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.5784 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
| No log | 2.0 | 8 | 0.5813 | 0.7375 | 0.7030 | 0.7375 | 0.6441 |
| No log | 3.0 | 12 | 0.5486 | 0.7417 | 0.7297 | 0.7417 | 0.7343 |
| No log | 4.0 | 16 | 0.5394 | 0.7542 | 0.7333 | 0.7542 | 0.7370 |
| No log | 5.0 | 20 | 0.5067 | 0.775 | 0.7658 | 0.775 | 0.7321 |
| No log | 6.0 | 24 | 0.5542 | 0.7958 | 0.7966 | 0.7958 | 0.7613 |
| No log | 7.0 | 28 | 0.4753 | 0.7958 | 0.7834 | 0.7958 | 0.7758 |
| 0.5325 | 8.0 | 32 | 0.5265 | 0.7792 | 0.7661 | 0.7792 | 0.7448 |
| 0.5325 | 9.0 | 36 | 0.4789 | 0.8208 | 0.8134 | 0.8208 | 0.8067 |
| 0.5325 | 10.0 | 40 | 0.4939 | 0.7875 | 0.7932 | 0.7875 | 0.7900 |
| 0.5325 | 11.0 | 44 | 0.4917 | 0.8042 | 0.8032 | 0.8042 | 0.8037 |
| 0.5325 | 12.0 | 48 | 0.5001 | 0.8083 | 0.8019 | 0.8083 | 0.8041 |
| 0.5325 | 13.0 | 52 | 0.4742 | 0.8 | 0.7897 | 0.8 | 0.7915 |
| 0.5325 | 14.0 | 56 | 0.5439 | 0.7875 | 0.8037 | 0.7875 | 0.7932 |
| 0.3381 | 15.0 | 60 | 0.5436 | 0.8333 | 0.8265 | 0.8333 | 0.8263 |
| 0.3381 | 16.0 | 64 | 0.4989 | 0.8375 | 0.8312 | 0.8375 | 0.8288 |
| 0.3381 | 17.0 | 68 | 0.4949 | 0.8333 | 0.8282 | 0.8333 | 0.8296 |
| 0.3381 | 18.0 | 72 | 0.4709 | 0.8292 | 0.8283 | 0.8292 | 0.8287 |
| 0.3381 | 19.0 | 76 | 0.4680 | 0.8167 | 0.8133 | 0.8167 | 0.8147 |
| 0.3381 | 20.0 | 80 | 0.5053 | 0.8417 | 0.8362 | 0.8417 | 0.8371 |
| 0.3381 | 21.0 | 84 | 0.5480 | 0.8458 | 0.8459 | 0.8458 | 0.8322 |
| 0.3381 | 22.0 | 88 | 0.4548 | 0.8542 | 0.8512 | 0.8542 | 0.8522 |
| 0.2076 | 23.0 | 92 | 0.4891 | 0.8458 | 0.8407 | 0.8458 | 0.8376 |
| 0.2076 | 24.0 | 96 | 0.4981 | 0.85 | 0.8486 | 0.85 | 0.8492 |
| 0.2076 | 25.0 | 100 | 0.4993 | 0.8458 | 0.8426 | 0.8458 | 0.8438 |
| 0.2076 | 26.0 | 104 | 0.5026 | 0.8542 | 0.8503 | 0.8542 | 0.8514 |
| 0.2076 | 27.0 | 108 | 0.4944 | 0.8542 | 0.8522 | 0.8542 | 0.8530 |
| 0.2076 | 28.0 | 112 | 0.4821 | 0.8542 | 0.8549 | 0.8542 | 0.8545 |
| 0.2076 | 29.0 | 116 | 0.4714 | 0.8583 | 0.8559 | 0.8583 | 0.8568 |
| 0.138 | 30.0 | 120 | 0.4705 | 0.8583 | 0.8559 | 0.8583 | 0.8568 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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