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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-11745
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: 0.8352490421455939
- name: Recall
type: recall
value: 0.8352490421455939
- name: F1
type: f1
value: 0.8308048120146843
- name: Precision
type: precision
value: 0.8376123328771266
---
<!-- 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-11745
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: 0.3657
- Accuracy: 0.8352
- Recall: 0.8352
- F1: 0.8308
- Precision: 0.8376
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5885 | 0.9974 | 293 | 0.5902 | 0.7910 | 0.7910 | 0.7773 | 0.7940 |
| 0.4253 | 1.9983 | 587 | 0.4608 | 0.8174 | 0.8174 | 0.8128 | 0.8149 |
| 0.4941 | 2.9991 | 881 | 0.4111 | 0.8335 | 0.8335 | 0.8309 | 0.8369 |
| 0.4876 | 4.0 | 1175 | 0.4002 | 0.8378 | 0.8378 | 0.8349 | 0.8426 |
| 0.3936 | 4.9974 | 1468 | 0.3882 | 0.8246 | 0.8246 | 0.8221 | 0.8361 |
| 0.3928 | 5.9983 | 1762 | 0.3640 | 0.8421 | 0.8421 | 0.8404 | 0.8443 |
| 0.3752 | 6.9991 | 2056 | 0.3553 | 0.8442 | 0.8442 | 0.8409 | 0.8469 |
| 0.2994 | 8.0 | 2350 | 0.3540 | 0.8259 | 0.8259 | 0.8245 | 0.8271 |
| 0.2614 | 8.9974 | 2643 | 0.3591 | 0.8378 | 0.8378 | 0.8360 | 0.8386 |
| 0.2632 | 9.9745 | 2930 | 0.3483 | 0.8404 | 0.8404 | 0.8399 | 0.8419 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.18.0
- Tokenizers 0.19.1
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