metadata

base_model: apple/mobilevit-xx-small
datasets:
  - webdataset
library_name: transformers
license: other
metrics:
  - accuracy
  - f1
  - precision
  - recall
tags:
  - generated_from_trainer
model-index:
  - name: frost-mobile-apple__mobilevit-xx-small-v2024-10-22
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: webdataset
          type: webdataset
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.9475555555555556
            name: Accuracy
          - type: f1
            value: 0.8700440528634361
            name: F1
          - type: precision
            value: 0.8681318681318682
            name: Precision
          - type: recall
            value: 0.8719646799116998
            name: Recall

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: 0.1381
Accuracy: 0.9476
F1: 0.8700
Precision: 0.8681
Recall: 0.8720

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