---
license: apache-2.0
base_model: google/bit-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: bit-50
  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: 0.085
---

<!-- 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. -->

# bit-50

This model is a fine-tuned version of [google/bit-50](https://huggingface.co/google/bit-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 7122385408.0
- Accuracy: 0.085

## 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: 0.0002
- 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5528748032.0  | 1.0   | 50   | 7122386944.0    | 0.085    |
| 5155133849.6  | 2.0   | 100  | 7122386944.0    | 0.085    |
| 5068722995.2  | 3.0   | 150  | 7122386944.0    | 0.085    |
| 5613660569.6  | 4.0   | 200  | 7122385408.0    | 0.085    |
| 7499937382.4  | 5.0   | 250  | 7122385408.0    | 0.085    |
| 5806654259.2  | 6.0   | 300  | 7122385408.0    | 0.085    |
| 5483250483.2  | 7.0   | 350  | 7122385408.0    | 0.085    |
| 6852667392.0  | 8.0   | 400  | 7122385408.0    | 0.085    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1