metadata

library_name: transformers
license: other
base_model: apple/mobilevit-xx-small
tags:
  - generated_from_trainer
datasets:
  - webdataset
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: frost-mobile-apple__mobilevit-xx-small-v2024-10-22
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: webdataset
          type: webdataset
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9497777777777778
          - name: F1
            type: f1
            value: 0.8754134509371555
          - name: Precision
            type: precision
            value: 0.8744493392070485
          - name: Recall
            type: recall
            value: 0.8763796909492274

frost-mobile-apple__mobilevit-xx-small-v2024-10-22

This model is a fine-tuned version of apple/mobilevit-xx-small on the webdataset dataset. It achieves the following results on the evaluation set:

Loss: 0.1343
Accuracy: 0.9498
F1: 0.8754
Precision: 0.8744
Recall: 0.8764

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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.1927	1.7544	100	0.1470	0.9422	0.8565	0.8565	0.8565
0.1601	3.5088	200	0.1499	0.9444	0.8616	0.8644	0.8587
0.1544	5.2632	300	0.1536	0.9391	0.8493	0.8465	0.8521
0.207	7.0175	400	0.1374	0.9436	0.8575	0.8721	0.8433
0.1709	8.7719	500	0.1443	0.9431	0.8587	0.8587	0.8587
0.1548	10.5263	600	0.1572	0.9387	0.8490	0.8416	0.8565
0.1802	12.2807	700	0.1436	0.9458	0.8656	0.8637	0.8675
0.1455	14.0351	800	0.1442	0.9467	0.8667	0.8725	0.8609
0.1514	15.7895	900	0.1500	0.9422	0.8571	0.8534	0.8609
0.1368	17.5439	1000	0.1391	0.9489	0.8718	0.8806	0.8631
0.1515	19.2982	1100	0.1370	0.9476	0.8700	0.8681	0.8720
0.1372	21.0526	1200	0.1393	0.9458	0.8644	0.8702	0.8587
0.1397	22.8070	1300	0.1359	0.9498	0.8746	0.8795	0.8698
0.1398	24.5614	1400	0.1352	0.9489	0.8740	0.8674	0.8808
0.1276	26.3158	1500	0.1381	0.9476	0.8700	0.8681	0.8720
0.1519	28.0702	1600	0.1380	0.9462	0.8666	0.8656	0.8675
0.1479	29.8246	1700	0.1343	0.9498	0.8754	0.8744	0.8764

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.2
Tokenizers 0.19.1