---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-base-patch16-224
  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: 0.8138537787800912
---



# Caracam (gen 2)

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: 0.7005
- Accuracy: 0.8139

## Model description

***This is the model the Caracam mobile app will be is using upon release*** \
Second Generation of [Caracam](https://huggingface.co/Takekazuchi/Caracam)! with a tested accuracy of 92% and a reported accuracy of 81% this makes this model of Caracam at least 1.5x more accurate than gen 1!\
If you wish to support this project please head over to my [Patreon](https://www.patreon.com/Caracam) or my [Cashapp](https://cash.app/$Clippayy) \
even if you don't want to support us financially thank you for even just coming to this page in the first place as the traffic helps more people find this project!

## Intended uses & limitations

***NOT FOR COMMERCIAL USE OUTSIDE OF OFFICIAL CARACAM MOBILE APP***
\
***Limitations***
\
This model assumes that its input image contains a well-cropped car. \
If a non-car image is given or if the car is not well-cropped, the output of the model may be meaningless. 

## Expected Updates

EXPECTED RELEASE DATE OF CARACAM: 12/01 (Date pushed back due to complications with tflite support for ViT model architecture) \
Future versions of Caracam mobile app to have price-prediction with links via web-integration as well as more general information on the predicted car model. \
User-Polls and comments with like/dislike system to be addded for feedback on new cars and anything else users want added to the app/model.


## Non-Suitable Usecases

Do NOT use this model to determine whether an object is a car or not.

## Suitable Usecases

DO use this model to determine the make/model/year of a car \
DO take pictures of your animals to see what car they look like and send the results to my [Twitter](https://twitter.com/caracam0) so i can retweet them!

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 2.9008        | 1.0   | 21451  | 2.0266          | 0.5235   |
| 1.747         | 2.0   | 42902  | 1.2724          | 0.6764   |
| 1.4453        | 3.0   | 64353  | 1.0682          | 0.7189   |
| 1.1809        | 4.0   | 85804  | 0.9663          | 0.7445   |
| 1.2318        | 5.0   | 107255 | 0.8934          | 0.7596   |
| 0.8664        | 6.0   | 128706 | 0.8309          | 0.7782   |
| 1.0645        | 7.0   | 150157 | 0.7863          | 0.7890   |
| 1.1092        | 8.0   | 171608 | 0.7503          | 0.7974   |
| 0.8655        | 9.0   | 193059 | 0.7204          | 0.8076   |
| 0.6357        | 10.0  | 214510 | 0.7005          | 0.8139   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2