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tsec_vit_model

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tsec_vit_model
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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: tsec_vit_model
  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: 0.7689873417721519
---

<!-- 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. -->

# tsec_vit_model

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4821
- Accuracy: 0.7690

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4963        | 0.9873 | 39   | 0.5699          | 0.6725   |
| 0.4959        | 2.0    | 79   | 0.5485          | 0.7152   |
| 0.4879        | 2.9873 | 118  | 0.4886          | 0.7690   |
| 0.5243        | 4.0    | 158  | 0.5133          | 0.7468   |
| 0.4654        | 4.9873 | 197  | 0.4927          | 0.7516   |
| 0.4776        | 6.0    | 237  | 0.4901          | 0.7642   |
| 0.4767        | 6.9873 | 276  | 0.4652          | 0.7816   |
| 0.4465        | 8.0    | 316  | 0.4795          | 0.7642   |
| 0.467         | 8.9873 | 355  | 0.4691          | 0.7484   |
| 0.4121        | 9.8734 | 390  | 0.4821          | 0.7690   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1