metadata

license: mit
base_model: shi-labs/nat-mini-in1k-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: msi-nat-mini-pretrain
    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: 0.8704735376044568

msi-nat-mini-pretrain

This model is a fine-tuned version of shi-labs/nat-mini-in1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6286
Accuracy: 0.8705

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
0.1151	1.0	1562	0.2480	0.9242
0.0453	2.0	3125	0.5128	0.8816
0.0466	3.0	4686	0.6286	0.8705

Framework versions

Transformers 4.35.2
Pytorch 2.0.1+cu118
Datasets 2.15.0
Tokenizers 0.15.0