---
license: other
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mobilenet_v2_1.0_224-cxr-view
  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: 0.929384965831435
---

<!-- 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. -->

# mobilenet_v2_1.0_224-cxr-view

This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2278
- Accuracy: 0.9294

## 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7049        | 1.0   | 109  | 0.6746          | 0.7449   |
| 0.6565        | 2.0   | 219  | 0.6498          | 0.6743   |
| 0.5699        | 3.0   | 328  | 0.5730          | 0.7995   |
| 0.5702        | 4.0   | 438  | 0.5119          | 0.8087   |
| 0.4849        | 5.0   | 547  | 0.4356          | 0.8679   |
| 0.356         | 6.0   | 657  | 0.4641          | 0.8087   |
| 0.3713        | 7.0   | 766  | 0.3407          | 0.8679   |
| 0.4571        | 8.0   | 876  | 0.4896          | 0.7813   |
| 0.3896        | 9.0   | 985  | 0.3124          | 0.8884   |
| 0.3422        | 10.0  | 1095 | 0.2791          | 0.9271   |
| 0.3358        | 11.0  | 1204 | 0.3998          | 0.8246   |
| 0.3658        | 12.0  | 1314 | 0.2716          | 0.9066   |
| 0.4547        | 13.0  | 1423 | 0.5828          | 0.7973   |
| 0.2615        | 14.0  | 1533 | 0.3446          | 0.8542   |
| 0.377         | 15.0  | 1642 | 0.6322          | 0.7312   |
| 0.2846        | 16.0  | 1752 | 0.2621          | 0.9248   |
| 0.3433        | 17.0  | 1861 | 0.3709          | 0.8383   |
| 0.2851        | 18.0  | 1971 | 0.8134          | 0.7312   |
| 0.2298        | 19.0  | 2080 | 0.4324          | 0.8314   |
| 0.3916        | 20.0  | 2190 | 0.3631          | 0.8360   |
| 0.3049        | 21.0  | 2299 | 0.3405          | 0.8633   |
| 0.3068        | 22.0  | 2409 | 0.2585          | 0.9021   |
| 0.3091        | 23.0  | 2518 | 0.2278          | 0.9294   |
| 0.2749        | 24.0  | 2628 | 0.2963          | 0.9043   |
| 0.3543        | 25.0  | 2737 | 0.2637          | 0.8975   |
| 0.3024        | 26.0  | 2847 | 0.2966          | 0.8998   |
| 0.2593        | 27.0  | 2956 | 0.3842          | 0.8542   |
| 0.1979        | 28.0  | 3066 | 0.2711          | 0.8884   |
| 0.2549        | 29.0  | 3175 | 0.3145          | 0.8633   |
| 0.3216        | 29.86 | 3270 | 0.4565          | 0.8155   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3