transformers safetensors nat image-classification generated_from_trainer dataset:imagefolder base_model:shi-labs/nat-mini-in1k-224 license:mit model-index autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: shi-labs/nat-mini-in1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi
  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: 0.6215054514956667
---

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# msi

This model is a fine-tuned version of [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4627
- Accuracy: 0.6215

## 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-05
- 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2672        | 1.0   | 2015 | 1.0553          | 0.6028   |
| 0.1972        | 2.0   | 4031 | 1.1869          | 0.6437   |
| 0.1732        | 3.0   | 6045 | 1.4627          | 0.6215   |


### Framework versions

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