generated_from_trainer base_model:a license:apache-2.0 model-index region:us
---
license: apache-2.0
base_model: a
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: eunoiawiira-vgg-realwaste-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: 0.8501
---

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# eunoiawiira-vgg-realwaste-classification 

This CNN model is a fine-tuned version of VGG11 and VGG16 on the imagefolder dataset of RealWaste Dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6688
- Accuracy: 0.8365

## Model description

This CNN model is a fine-tuned version of VGG16 based on imagefolder dataset from kaggle.com/datasets/joebeachcapital/realwaste
https://doi.org/10.3390/info14120633

## Intended uses & limitations

This CNN model intended to classify an object especially a waste items across 12 major material types
- Cardboard
- Food Organics
- Glass
- Metal
- Miscellaneous Trash
- Mixed
- Paper
- Plastic
- Porcelain
- Rubber
- Textille Trash
- Vegetation

Dataset source is available at dataset_source.text

## Training and evaluation data

More information needed

## Training procedure

More information needed

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- optimizer: Adam 
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss |  Epoch | Validation Loss | Accuracy |
|:-------------:|:------:|:---------------:|:--------:|
| 1.3587        |  1/10  |      0.8157     |  0.7285  |
| 0.8003        |  2/10  |      0.6563     |  0.7568  |
| 0.6828        |  3/10  |      0.5165     |  0.8218  |
| 0.5429        |  4/10  |      0.5163     |  0.8103  |
| 0.5080        |  5/10  |      0.4336     |  0.8470  |
| 0.4128        |  6/10  |      0.5926     |  0.8124  |
| 0.4008        |  7/10  |      0.5306     |  0.8270  |
| 0.3897        |  8/10  |      0.3438     |  0.8816  |
| 0.3073        |  9/10  |      0.4028     |  0.8753  |
| 0.3842        | 10/10  |      0.4557     |  0.8501  |


Epoch	            : 10
Train Loss	        : 0.30419
Validation Accuracy	: 0.8501
Validation Loss	    : 0.45567

### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121