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---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask
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: 0.8450404427415922
- name: Recall
type: recall
value: 0.8450404427415922
- name: F1
type: f1
value: 0.8442233792705293
- name: Precision
type: precision
value: 0.8494143266059094
---
<!-- 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-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3480
- Accuracy: 0.8450
- Recall: 0.8450
- F1: 0.8442
- Precision: 0.8494
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5859 | 0.9974 | 293 | 0.6117 | 0.8114 | 0.8114 | 0.7891 | 0.8139 |
| 0.5281 | 1.9983 | 587 | 0.4362 | 0.8442 | 0.8442 | 0.8375 | 0.8484 |
| 0.4214 | 2.9991 | 881 | 0.4228 | 0.8438 | 0.8438 | 0.8392 | 0.8529 |
| 0.4221 | 4.0 | 1175 | 0.4121 | 0.8382 | 0.8382 | 0.8331 | 0.8495 |
| 0.4127 | 4.9974 | 1468 | 0.3692 | 0.8476 | 0.8476 | 0.8454 | 0.8511 |
| 0.3122 | 5.9983 | 1762 | 0.3741 | 0.8408 | 0.8408 | 0.8394 | 0.8462 |
| 0.3079 | 6.9991 | 2056 | 0.3628 | 0.8429 | 0.8429 | 0.8403 | 0.8445 |
| 0.2851 | 8.0 | 2350 | 0.3635 | 0.8412 | 0.8412 | 0.8389 | 0.8412 |
| 0.297 | 8.9974 | 2643 | 0.3407 | 0.8510 | 0.8510 | 0.8497 | 0.8545 |
| 0.2109 | 9.9745 | 2930 | 0.3566 | 0.8421 | 0.8421 | 0.8406 | 0.8418 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.19.0
- Tokenizers 0.19.1
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