---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-resnet-18
  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: 0.6217151244059268
    - name: F1
      type: f1
      value: 0.5152478617168957
    - name: Precision
      type: precision
      value: 0.5801734570391287
    - name: Recall
      type: recall
      value: 0.4633910592025775
---

<!-- 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-resnet-18

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6730
- Accuracy: 0.6217
- F1: 0.5152
- Precision: 0.5802
- Recall: 0.4634

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5705        | 1.0   | 2015 | 0.6879          | 0.5897   | 0.4460 | 0.5384    | 0.3807 |
| 0.5309        | 2.0   | 4031 | 0.6788          | 0.6091   | 0.4859 | 0.5657    | 0.4258 |
| 0.5263        | 3.0   | 6047 | 0.7020          | 0.6036   | 0.4322 | 0.5709    | 0.3477 |
| 0.496         | 4.0   | 8060 | 0.6730          | 0.6217   | 0.5152 | 0.5802    | 0.4634 |


### Framework versions

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