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--- |
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library_name: transformers |
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license: other |
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base_model: apple/mobilevit-xx-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- webdataset |
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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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model-index: |
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- name: frost-mobile-apple__mobilevit-xx-small-v2024-10-22 |
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results: |
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- task: |
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name: Image Classification |
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type: 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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- name: Accuracy |
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type: accuracy |
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value: 0.9497777777777778 |
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- name: F1 |
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type: f1 |
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value: 0.8754134509371555 |
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- name: Precision |
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type: precision |
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value: 0.8744493392070485 |
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- name: Recall |
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type: recall |
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value: 0.8763796909492274 |
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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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# frost-mobile-apple__mobilevit-xx-small-v2024-10-22 |
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This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co./apple/mobilevit-xx-small) on the webdataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1343 |
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- Accuracy: 0.9498 |
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- F1: 0.8754 |
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- Precision: 0.8744 |
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- Recall: 0.8764 |
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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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| 0.1927 | 1.7544 | 100 | 0.1470 | 0.9422 | 0.8565 | 0.8565 | 0.8565 | |
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| 0.1601 | 3.5088 | 200 | 0.1499 | 0.9444 | 0.8616 | 0.8644 | 0.8587 | |
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| 0.1544 | 5.2632 | 300 | 0.1536 | 0.9391 | 0.8493 | 0.8465 | 0.8521 | |
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| 0.207 | 7.0175 | 400 | 0.1374 | 0.9436 | 0.8575 | 0.8721 | 0.8433 | |
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| 0.1709 | 8.7719 | 500 | 0.1443 | 0.9431 | 0.8587 | 0.8587 | 0.8587 | |
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| 0.1548 | 10.5263 | 600 | 0.1572 | 0.9387 | 0.8490 | 0.8416 | 0.8565 | |
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| 0.1802 | 12.2807 | 700 | 0.1436 | 0.9458 | 0.8656 | 0.8637 | 0.8675 | |
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| 0.1455 | 14.0351 | 800 | 0.1442 | 0.9467 | 0.8667 | 0.8725 | 0.8609 | |
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| 0.1514 | 15.7895 | 900 | 0.1500 | 0.9422 | 0.8571 | 0.8534 | 0.8609 | |
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| 0.1368 | 17.5439 | 1000 | 0.1391 | 0.9489 | 0.8718 | 0.8806 | 0.8631 | |
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| 0.1515 | 19.2982 | 1100 | 0.1370 | 0.9476 | 0.8700 | 0.8681 | 0.8720 | |
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| 0.1372 | 21.0526 | 1200 | 0.1393 | 0.9458 | 0.8644 | 0.8702 | 0.8587 | |
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| 0.1397 | 22.8070 | 1300 | 0.1359 | 0.9498 | 0.8746 | 0.8795 | 0.8698 | |
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| 0.1398 | 24.5614 | 1400 | 0.1352 | 0.9489 | 0.8740 | 0.8674 | 0.8808 | |
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| 0.1276 | 26.3158 | 1500 | 0.1381 | 0.9476 | 0.8700 | 0.8681 | 0.8720 | |
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| 0.1519 | 28.0702 | 1600 | 0.1380 | 0.9462 | 0.8666 | 0.8656 | 0.8675 | |
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| 0.1479 | 29.8246 | 1700 | 0.1343 | 0.9498 | 0.8754 | 0.8744 | 0.8764 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.2 |
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- Tokenizers 0.19.1 |
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