---
license: other
base_model: google/mobilenet_v2_0.75_160
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: day-night
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9965357967667436
---

<!-- 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. -->

# day-night

This model is a fine-tuned version of [google/mobilenet_v2_0.75_160](https://huggingface.co/google/mobilenet_v2_0.75_160) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0117
- Accuracy: 0.9965

## 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: 0.0001
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0899        | 0.6   | 200  | 0.0934          | 0.9711   |
| 0.026         | 1.19  | 400  | 0.0225          | 0.9942   |
| 0.0689        | 1.79  | 600  | 1.5236          | 0.7032   |
| 0.0193        | 2.38  | 800  | 0.0117          | 0.9965   |
| 0.028         | 2.98  | 1000 | 0.0186          | 0.9919   |
| 0.0159        | 3.57  | 1200 | 0.0150          | 0.9954   |
| 0.0194        | 4.17  | 1400 | 0.0369          | 0.9919   |
| 0.0081        | 4.76  | 1600 | 0.0471          | 0.9850   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3