transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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-vit-base-patch16
  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: 0.5851995594482614
---


# Caracam (gen 1)

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: 1.9156
- Accuracy: 0.5852

## Model description

First generation of my AI that tells you what car you took a picture of. \
More versions coming soon with accuracy ratings of 85% and higher! Trained on 70+ brands with 2700+ cars going from 1945-2024. \
***App coming soon (also called Caracam) to Android and IOS*** \
            (Late March - Early April 2024).
In the future I will take user opinion into account on what brands to add. The app will be updated semi-yearly with user-suggested car brands! \
if you wish to support project Caracam please visit my [Patreon](https://www.patreon.com/Caracam) or my [Cashapp](https://cash.app/$Clippayy)!!

## Intended uses & limitations

***NOT FOR COMMERCIAL USE OUTSIDE OF OFFICIAL CARACAM MOBILE APP***

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.0308        | 1.0   | 5362  | 3.6948          | 0.2491   |
| 2.694         | 2.0   | 10725 | 2.2586          | 0.5199   |
| 2.4475        | 3.0   | 16086 | 1.9156          | 0.5852   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0
