metadata

base_model: apple/mobilevitv2-1.0-imagenet1k-256
datasets:
  - webdataset
license: other
metrics:
  - accuracy
  - f1
  - precision
  - recall
tags:
  - generated_from_trainer
model-index:
  - name: mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-7-25-frost
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: webdataset
          type: webdataset
          config: default
          split: train
          args: default
        metrics:
          - type: accuracy
            value: 0.9309734513274336
            name: Accuracy
          - type: f1
            value: 0.8227272727272726
            name: F1
          - type: precision
            value: 0.8457943925233645
            name: Precision
          - type: recall
            value: 0.8008849557522124
            name: Recall

mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-7-25-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: 0.1896
Accuracy: 0.9310
F1: 0.8227
Precision: 0.8458
Recall: 0.8009

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
0.6687	1.5625	100	0.6623	0.7230	0.5335	0.4022	0.7920
0.4454	3.125	200	0.4152	0.8832	0.7490	0.6567	0.8717
0.2835	4.6875	300	0.2661	0.9097	0.7661	0.7952	0.7389
0.2197	6.25	400	0.2151	0.9195	0.7869	0.8358	0.7434
0.1613	7.8125	500	0.2007	0.9292	0.8140	0.8578	0.7743
0.1655	9.375	600	0.1935	0.9310	0.8227	0.8458	0.8009
0.1815	10.9375	700	0.1883	0.9265	0.8074	0.8488	0.7699
0.1316	12.5	800	0.1825	0.9327	0.8273	0.8505	0.8053
0.1612	14.0625	900	0.1837	0.9257	0.8100	0.8287	0.7920
0.118	15.625	1000	0.1896	0.9310	0.8227	0.8458	0.8009
0.1178	17.1875	1100	0.1937	0.9239	0.8028	0.8333	0.7743
0.1248	18.75	1200	0.1913	0.9301	0.8192	0.8483	0.7920
0.1169	20.3125	1300	0.1916	0.9301	0.8167	0.8585	0.7788
0.1094	21.875	1400	0.1925	0.9292	0.8182	0.8411	0.7965
0.1108	23.4375	1500	0.1961	0.9345	0.8333	0.8486	0.8186
0.1089	25.0	1600	0.1993	0.9283	0.8172	0.8341	0.8009
0.0919	26.5625	1700	0.1936	0.9319	0.8262	0.8433	0.8097
0.0969	28.125	1800	0.1978	0.9310	0.8227	0.8458	0.8009
0.1093	29.6875	1900	0.1955	0.9283	0.8172	0.8341	0.8009

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1