---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: categorAI_img
  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: 0.8378378378378378
---

<!-- 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. -->

# categorAI_img

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: 0.7080
- Accuracy: 0.8378

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9091  | 5    | 1.8872          | 0.3784   |
| 7.7979        | 1.9091  | 10   | 1.7777          | 0.6419   |
| 7.7979        | 2.9091  | 15   | 1.6224          | 0.6622   |
| 6.9519        | 3.9091  | 20   | 1.4667          | 0.6959   |
| 6.9519        | 4.9091  | 25   | 1.3353          | 0.7365   |
| 5.7562        | 5.9091  | 30   | 1.2522          | 0.7703   |
| 5.7562        | 6.9091  | 35   | 1.1617          | 0.7838   |
| 4.7446        | 7.9091  | 40   | 1.0967          | 0.7635   |
| 4.7446        | 8.9091  | 45   | 1.0362          | 0.7568   |
| 4.0655        | 9.9091  | 50   | 0.9349          | 0.8108   |
| 4.0655        | 10.9091 | 55   | 0.9393          | 0.7905   |
| 3.5041        | 11.9091 | 60   | 0.8859          | 0.7838   |
| 3.5041        | 12.9091 | 65   | 0.9039          | 0.7770   |
| 3.0788        | 13.9091 | 70   | 0.8123          | 0.8041   |
| 3.0788        | 14.9091 | 75   | 0.7946          | 0.8243   |
| 2.7461        | 15.9091 | 80   | 0.8003          | 0.8311   |
| 2.7461        | 16.9091 | 85   | 0.8101          | 0.7703   |
| 2.4988        | 17.9091 | 90   | 0.7111          | 0.8176   |
| 2.4988        | 18.9091 | 95   | 0.7439          | 0.8243   |
| 2.3122        | 19.9091 | 100  | 0.7542          | 0.7905   |
| 2.3122        | 20.9091 | 105  | 0.7323          | 0.8311   |
| 2.3408        | 21.9091 | 110  | 0.7175          | 0.8243   |
| 2.3408        | 22.9091 | 115  | 0.7652          | 0.8041   |
| 2.2846        | 23.9091 | 120  | 0.7211          | 0.8176   |
| 2.2846        | 24.9091 | 125  | 0.7080          | 0.8378   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1.post306
- Datasets 3.2.0
- Tokenizers 0.21.0