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msi-vit-small

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
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msi-vit-small
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---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-vit-small
  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: 0.599979032708974
    - name: F1
      type: f1
      value: 0.2863021385373153
    - name: Precision
      type: precision
      value: 0.6335540838852097
    - name: Recall
      type: recall
      value: 0.18493757551349174
---

<!-- 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-vit-small

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5796
- Accuracy: 0.6000
- F1: 0.2863
- Precision: 0.6336
- Recall: 0.1849

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3142        | 1.0   | 1008  | 0.8965          | 0.6329   | 0.5060 | 0.6079    | 0.4333 |
| 0.2063        | 2.0   | 2016  | 1.5189          | 0.6062   | 0.3005 | 0.6550    | 0.1950 |
| 0.19          | 3.0   | 3024  | 1.4818          | 0.6270   | 0.3399 | 0.7318    | 0.2213 |
| 0.1718        | 4.0   | 4032  | 1.2353          | 0.6046   | 0.4096 | 0.5816    | 0.3161 |
| 0.161         | 5.0   | 5040  | 1.5953          | 0.6342   | 0.3508 | 0.7623    | 0.2278 |
| 0.1805        | 6.0   | 6048  | 1.0789          | 0.6552   | 0.4647 | 0.7119    | 0.3449 |
| 0.1619        | 7.0   | 7056  | 1.2646          | 0.5479   | 0.2591 | 0.4484    | 0.1822 |
| 0.1655        | 8.0   | 8064  | 1.7155          | 0.5910   | 0.2654 | 0.6011    | 0.1703 |
| 0.17          | 9.0   | 9072  | 2.1142          | 0.5797   | 0.1729 | 0.5913    | 0.1012 |
| 0.1703        | 10.0  | 10080 | 1.5796          | 0.6000   | 0.2863 | 0.6336    | 0.1849 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0