---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: allsky-stars-detected-v2
  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: 0.994750656167979
---

<!-- 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. -->

# allsky-stars-detected-v2

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: 0.0171
- Accuracy: 0.9948

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1339
- 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
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0394        | 1.0   | 270  | 0.0407          | 0.9895   |
| 0.0973        | 2.0   | 540  | 0.0709          | 0.9843   |
| 0.0057        | 3.0   | 810  | 0.0425          | 0.9869   |
| 0.0403        | 4.0   | 1080 | 0.0499          | 0.9869   |
| 0.0608        | 5.0   | 1350 | 0.0171          | 0.9948   |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.5.0+cpu
- Datasets 3.0.1
- Tokenizers 0.21.0