metadata

license: apache-2.0
base_model: WinKawaks/vit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: msi-vit-small-1218-2
    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: 0.6164383561643836
          - name: F1
            type: f1
            value: 0.3276157804459692
          - name: Precision
            type: precision
            value: 0.6840624200562804
          - name: Recall
            type: recall
            value: 0.2153846153846154

msi-vit-small-1218-2

This model is a fine-tuned version of WinKawaks/vit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.3372
Accuracy: 0.6164
F1: 0.3276
Precision: 0.6841
Recall: 0.2154

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
0.4367	1.0	1008	0.6603	0.6572	0.5313	0.6530	0.4478
0.2161	2.0	2016	0.8021	0.6329	0.4989	0.6118	0.4211
0.169	3.0	3024	1.4062	0.6010	0.2653	0.6592	0.1661
0.1543	4.0	4032	1.1498	0.6259	0.3670	0.6903	0.2499
0.1534	5.0	5040	1.5067	0.6208	0.3519	0.6808	0.2373
0.1596	6.0	6048	0.8837	0.6504	0.6505	0.5744	0.7498
0.1504	7.0	7056	1.0030	0.6302	0.4192	0.6580	0.3075
0.1795	8.0	8064	1.3908	0.5953	0.2950	0.6041	0.1952
0.1636	9.0	9072	1.1040	0.6290	0.4619	0.6230	0.3671
0.1629	10.0	10080	1.3372	0.6164	0.3276	0.6841	0.2154

Framework versions

Transformers 4.36.0
Pytorch 2.0.1+cu117
Datasets 2.15.0
Tokenizers 0.15.0