---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-resnet18
  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: 0.7618322547900013
    - name: F1
      type: f1
      value: 0.7033583563808773
    - name: Precision
      type: precision
      value: 0.7032472149798531
    - name: Recall
      type: recall
      value: 0.7034695329170948
---

<!-- 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. -->

# msi-resnet18

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4869
- Accuracy: 0.7618
- F1: 0.7034
- Precision: 0.7032
- Recall: 0.7035

## 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: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6225        | 1.0   | 1970  | 0.6131          | 0.6718   | 0.5197 | 0.6298    | 0.4424 |
| 0.5749        | 2.0   | 3941  | 0.5577          | 0.7138   | 0.6061 | 0.6771    | 0.5486 |
| 0.5506        | 3.0   | 5911  | 0.5347          | 0.7355   | 0.6367 | 0.7096    | 0.5773 |
| 0.5304        | 4.0   | 7882  | 0.5114          | 0.7501   | 0.6615 | 0.7250    | 0.6082 |
| 0.5196        | 5.0   | 9852  | 0.5057          | 0.7503   | 0.6932 | 0.6838    | 0.7028 |
| 0.5125        | 6.0   | 11823 | 0.4920          | 0.7610   | 0.6983 | 0.7078    | 0.6890 |
| 0.5016        | 7.0   | 13793 | 0.4929          | 0.7578   | 0.7055 | 0.6890    | 0.7228 |
| 0.4871        | 8.0   | 15764 | 0.4796          | 0.7683   | 0.7042 | 0.7222    | 0.6870 |
| 0.5069        | 9.0   | 17734 | 0.4766          | 0.7743   | 0.6996 | 0.7512    | 0.6545 |
| 0.5059        | 10.0  | 19700 | 0.4869          | 0.7618   | 0.7034 | 0.7032    | 0.7035 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0