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--- |
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base_model: apple/mobilevitv2-1.0-imagenet1k-256 |
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datasets: |
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- webdataset |
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license: other |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-7-25-frost |
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results: |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: webdataset |
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type: webdataset |
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config: default |
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split: train |
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args: default |
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metrics: |
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- type: accuracy |
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value: 0.9309734513274336 |
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name: Accuracy |
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- type: f1 |
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value: 0.8227272727272726 |
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name: F1 |
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- type: precision |
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value: 0.8457943925233645 |
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name: Precision |
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- type: recall |
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value: 0.8008849557522124 |
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name: Recall |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mobilevitv2-1.0-imagenet1k-256-finetuned_v2024-7-25-frost |
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This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the webdataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1896 |
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- Accuracy: 0.9310 |
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- F1: 0.8227 |
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- Precision: 0.8458 |
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- Recall: 0.8009 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.6687 | 1.5625 | 100 | 0.6623 | 0.7230 | 0.5335 | 0.4022 | 0.7920 | |
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| 0.4454 | 3.125 | 200 | 0.4152 | 0.8832 | 0.7490 | 0.6567 | 0.8717 | |
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| 0.2835 | 4.6875 | 300 | 0.2661 | 0.9097 | 0.7661 | 0.7952 | 0.7389 | |
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| 0.2197 | 6.25 | 400 | 0.2151 | 0.9195 | 0.7869 | 0.8358 | 0.7434 | |
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| 0.1613 | 7.8125 | 500 | 0.2007 | 0.9292 | 0.8140 | 0.8578 | 0.7743 | |
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| 0.1655 | 9.375 | 600 | 0.1935 | 0.9310 | 0.8227 | 0.8458 | 0.8009 | |
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| 0.1815 | 10.9375 | 700 | 0.1883 | 0.9265 | 0.8074 | 0.8488 | 0.7699 | |
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| 0.1316 | 12.5 | 800 | 0.1825 | 0.9327 | 0.8273 | 0.8505 | 0.8053 | |
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| 0.1612 | 14.0625 | 900 | 0.1837 | 0.9257 | 0.8100 | 0.8287 | 0.7920 | |
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| 0.118 | 15.625 | 1000 | 0.1896 | 0.9310 | 0.8227 | 0.8458 | 0.8009 | |
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| 0.1178 | 17.1875 | 1100 | 0.1937 | 0.9239 | 0.8028 | 0.8333 | 0.7743 | |
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| 0.1248 | 18.75 | 1200 | 0.1913 | 0.9301 | 0.8192 | 0.8483 | 0.7920 | |
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| 0.1169 | 20.3125 | 1300 | 0.1916 | 0.9301 | 0.8167 | 0.8585 | 0.7788 | |
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| 0.1094 | 21.875 | 1400 | 0.1925 | 0.9292 | 0.8182 | 0.8411 | 0.7965 | |
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| 0.1108 | 23.4375 | 1500 | 0.1961 | 0.9345 | 0.8333 | 0.8486 | 0.8186 | |
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| 0.1089 | 25.0 | 1600 | 0.1993 | 0.9283 | 0.8172 | 0.8341 | 0.8009 | |
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| 0.0919 | 26.5625 | 1700 | 0.1936 | 0.9319 | 0.8262 | 0.8433 | 0.8097 | |
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| 0.0969 | 28.125 | 1800 | 0.1978 | 0.9310 | 0.8227 | 0.8458 | 0.8009 | |
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| 0.1093 | 29.6875 | 1900 | 0.1955 | 0.9283 | 0.8172 | 0.8341 | 0.8009 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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