---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- MiguelCalderon/TGdataTrain
- MiguelCalderon/TGdataTest
metrics:
- accuracy
model-index:
- name: google-vit-base-patch16-224-Waste-O-I-classification
  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: 0.956
language:
- es
- en
pipeline_tag: image-classification
library_name: transformers
---

<!-- 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. -->

# google-vit-base-patch16-224-Waste-O-I-classification

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:
- Accuracy: 0.956
- Loss: 0.3036

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step  | Accuracy | Validation Loss |
|:-------------:|:------:|:-----:|:--------:|:---------------:|
| 0.2168        | 0.1580 | 1000  | 0.9525   | 0.1303          |
| 0.196         | 0.3159 | 2000  | 0.941    | 0.1638          |
| 0.1993        | 0.4739 | 3000  | 0.9285   | 0.2206          |
| 0.1849        | 0.6318 | 4000  | 0.9225   | 0.2288          |
| 0.199         | 0.7898 | 5000  | 0.9105   | 0.3331          |
| 0.2171        | 0.9477 | 6000  | 0.944    | 0.1582          |
| 0.1209        | 1.1057 | 7000  | 0.9495   | 0.1887          |
| 0.114         | 1.2636 | 8000  | 0.932    | 0.1950          |
| 0.1268        | 1.4216 | 9000  | 0.9335   | 0.1965          |
| 0.1272        | 1.5795 | 10000 | 0.9165   | 0.3112          |
| 0.1003        | 1.7375 | 11000 | 0.9575   | 0.1353          |
| 0.0844        | 1.8954 | 12000 | 0.9345   | 0.2635          |
| 0.0757        | 2.0534 | 13000 | 0.952    | 0.1434          |
| 0.053         | 2.2113 | 14000 | 0.933    | 0.3203          |
| 0.0994        | 2.3693 | 15000 | 0.9405   | 0.2165          |
| 0.0248        | 2.5272 | 16000 | 0.951    | 0.2400          |
| 0.0842        | 2.6852 | 17000 | 0.906    | 0.4092          |
| 0.0733        | 2.8432 | 18000 | 0.9515   | 0.1937          |
| 0.0542        | 3.0011 | 19000 | 0.938    | 0.2911          |
| 0.0202        | 3.1591 | 20000 | 0.936    | 0.3648          |
| 0.0237        | 3.3170 | 21000 | 0.9355   | 0.3618          |
| 0.0294        | 3.4750 | 22000 | 0.9255   | 0.4209          |
| 0.0375        | 3.6329 | 23000 | 0.943    | 0.2840          |
| 0.0176        | 3.7909 | 24000 | 0.9525   | 0.2604          |
| 0.0252        | 3.9488 | 25000 | 0.9515   | 0.2500          |
| 0.0024        | 4.1068 | 26000 | 0.9545   | 0.2892          |
| 0.0119        | 4.2647 | 27000 | 0.956    | 0.3036          |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1