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---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: deit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-14687
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: 0.8458918688803746
- name: Recall
type: recall
value: 0.8458918688803746
- name: F1
type: f1
value: 0.843745130911636
- name: Precision
type: precision
value: 0.8521498018011563
---
<!-- 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. -->
# deit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-14687
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3635
- Accuracy: 0.8459
- Recall: 0.8459
- F1: 0.8437
- Precision: 0.8521
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6153 | 0.9974 | 293 | 0.6607 | 0.7739 | 0.7739 | 0.7506 | 0.7444 |
| 0.5075 | 1.9983 | 587 | 0.5850 | 0.7927 | 0.7927 | 0.7767 | 0.8081 |
| 0.5278 | 2.9991 | 881 | 0.4721 | 0.8199 | 0.8199 | 0.8170 | 0.8301 |
| 0.445 | 4.0 | 1175 | 0.4495 | 0.8186 | 0.8186 | 0.8136 | 0.8192 |
| 0.3781 | 4.9974 | 1468 | 0.4018 | 0.8263 | 0.8263 | 0.8249 | 0.8326 |
| 0.4025 | 5.9983 | 1762 | 0.4356 | 0.8221 | 0.8221 | 0.8195 | 0.8245 |
| 0.3409 | 6.9991 | 2056 | 0.3876 | 0.8267 | 0.8267 | 0.8248 | 0.8330 |
| 0.3181 | 8.0 | 2350 | 0.3849 | 0.8391 | 0.8391 | 0.8372 | 0.8436 |
| 0.3042 | 8.9974 | 2643 | 0.3850 | 0.8280 | 0.8280 | 0.8285 | 0.8347 |
| 0.2475 | 9.9983 | 2937 | 0.3624 | 0.8493 | 0.8493 | 0.8475 | 0.8571 |
| 0.2339 | 10.9991 | 3231 | 0.3865 | 0.8318 | 0.8318 | 0.8281 | 0.8307 |
| 0.2455 | 12.0 | 3525 | 0.3337 | 0.8387 | 0.8387 | 0.8371 | 0.8433 |
| 0.2127 | 12.9974 | 3818 | 0.3685 | 0.8306 | 0.8306 | 0.8281 | 0.8356 |
| 0.2288 | 13.9983 | 4112 | 0.3545 | 0.8370 | 0.8370 | 0.8352 | 0.8385 |
| 0.2534 | 14.9991 | 4406 | 0.3587 | 0.8429 | 0.8429 | 0.8398 | 0.8537 |
| 0.1911 | 16.0 | 4700 | 0.3573 | 0.8387 | 0.8387 | 0.8367 | 0.8396 |
| 0.2118 | 16.9974 | 4993 | 0.3676 | 0.8370 | 0.8370 | 0.8356 | 0.8415 |
| 0.22 | 17.9983 | 5287 | 0.3469 | 0.8357 | 0.8357 | 0.8326 | 0.8412 |
| 0.1938 | 18.9991 | 5581 | 0.3512 | 0.8365 | 0.8365 | 0.8343 | 0.8363 |
| 0.1816 | 19.9489 | 5860 | 0.3323 | 0.8463 | 0.8463 | 0.8449 | 0.8476 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.18.0
- Tokenizers 0.19.1
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