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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter
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: 0.8339719029374202
- name: Recall
type: recall
value: 0.8339719029374202
- name: F1
type: f1
value: 0.8319571049551264
- name: Precision
type: precision
value: 0.8325133593723552
---
<!-- 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. -->
# vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3507
- Accuracy: 0.8340
- Recall: 0.8340
- F1: 0.8320
- Precision: 0.8325
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| No log | 0.9974 | 293 | 0.6168 | 0.7923 | 0.7923 | 0.7737 | 0.7684 |
| No log | 1.9983 | 587 | 0.4599 | 0.8110 | 0.8110 | 0.8056 | 0.8085 |
| No log | 2.9991 | 881 | 0.4305 | 0.8233 | 0.8233 | 0.8211 | 0.8250 |
| No log | 4.0 | 1175 | 0.3966 | 0.8365 | 0.8365 | 0.8323 | 0.8452 |
| No log | 4.9974 | 1468 | 0.4100 | 0.8221 | 0.8221 | 0.8195 | 0.8219 |
| No log | 5.9983 | 1762 | 0.3890 | 0.8412 | 0.8412 | 0.8375 | 0.8466 |
| No log | 6.9991 | 2056 | 0.3659 | 0.8357 | 0.8357 | 0.8335 | 0.8386 |
| No log | 8.0 | 2350 | 0.3562 | 0.8395 | 0.8395 | 0.8379 | 0.8403 |
| No log | 8.9974 | 2643 | 0.3613 | 0.8382 | 0.8382 | 0.8373 | 0.8391 |
| 0.4339 | 9.9745 | 2930 | 0.3405 | 0.8455 | 0.8455 | 0.8447 | 0.8467 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.19.0
- Tokenizers 0.19.1
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