metadata

base_model: apple/mobilevitv2-1.0-imagenet1k-256
datasets:
  - webdataset
library_name: transformers
license: other
metrics:
  - accuracy
  - f1
  - precision
  - recall
tags:
  - generated_from_trainer
model-index:
  - name: mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-10-21-frost
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: webdataset
          type: webdataset
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.9444444444444444
            name: Accuracy
          - type: f1
            value: 0.8544819557625145
            name: F1
          - type: precision
            value: 0.8615023474178404
            name: Precision
          - type: recall
            value: 0.8475750577367206
            name: Recall

mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-10-21-frost

This model is a fine-tuned version of apple/mobilevitv2-1.0-imagenet1k-256 on the webdataset dataset. It achieves the following results on the evaluation set:

Loss: 0.1539
Accuracy: 0.9444
F1: 0.8545
Precision: 0.8615
Recall: 0.8476

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
0.6635	1.7544	100	0.6513	0.7604	0.5705	0.4355	0.8268
0.4461	3.5088	200	0.3972	0.8769	0.7292	0.6322	0.8614
0.2599	5.2632	300	0.2404	0.9227	0.8049	0.7821	0.8291
0.2074	7.0175	400	0.1942	0.9347	0.8256	0.8488	0.8037
0.167	8.7719	500	0.1772	0.9364	0.8354	0.8326	0.8383
0.1661	10.5263	600	0.1653	0.9342	0.8259	0.8417	0.8106
0.1603	12.2807	700	0.1649	0.9409	0.8473	0.8425	0.8522
0.1523	14.0351	800	0.1568	0.9467	0.8592	0.8735	0.8453
0.1506	15.7895	900	0.1548	0.9431	0.8494	0.8657	0.8337
0.1485	17.5439	1000	0.1539	0.9444	0.8545	0.8615	0.8476
0.1263	19.2982	1100	0.1521	0.944	0.8535	0.8595	0.8476
0.1444	21.0526	1200	0.1552	0.9418	0.8471	0.8561	0.8383
0.1133	22.8070	1300	0.1531	0.9449	0.8561	0.8601	0.8522
0.1019	24.5614	1400	0.1577	0.9431	0.8491	0.8675	0.8314
0.1141	26.3158	1500	0.1560	0.9413	0.8472	0.8492	0.8453
0.1087	28.0702	1600	0.1573	0.9422	0.8492	0.8531	0.8453
0.1015	29.8246	1700	0.1545	0.9422	0.8488	0.8548	0.8430

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1