---
license: mit
datasets:
- ramnck/beer-classification
base_model:
- google/vit-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
---

# Model Card for Model ID

Student project

Beer classification model
Finetuned from vit-base
65 classes
Manually collected dataset

## Model Details

### Model Description



- **Developed by:** Karetnikov Roman Khmilevsky Alexey Koreshkov Nicolay
- **Model type:** Vit for image classification
- **License:** MIT
- **Finetuned from model:** google/vit-base-patch16-224 


## Uses

Use for classification of beer from enlisted types

## Bias, Risks, and Limitations

May not be 100% accurate

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

```python

from transformers import ViTForImageClassification, ViTImageProcessor

model = ViTForImageClassification.from_pretrained("ramnck/beer-classificator")
processor = ViTImageProcessor.from_pretrained("ramnck/beer-classificator")

```

## Training Details

### Training Data

65 classes, around 50 photos per class, all photos was taken by our hands

### Training Procedure

SFT

#### Preprocessing [optional]

Resize to 224x224

#### Training Hyperparameters

look in .ipynb

#### Speeds, Sizes, Times [optional]

idk

## Evaluation

yes

### Testing Data, Factors & Metrics

#### Testing Data

yes

#### Factors

it`s bad testing data (looks very alike as train data)

#### Metrics

Final Test Accuracy: 0.9982

### Results

it works

#### Summary

have a nice day

## Environmental Impact

i ate 20 KFC wings during train
i hope i spoiled air as much as it was possible


## Model Card Contact

write me on my HF/GH