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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: 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: 0.9337777777777778 |
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- name: F1 |
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type: f1 |
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value: 0.826945412311266 |
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- name: Precision |
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type: precision |
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value: 0.8259860788863109 |
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- name: Recall |
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type: recall |
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value: 0.827906976744186 |
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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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# 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: 0.1725 |
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- Accuracy: 0.9338 |
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- F1: 0.8269 |
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- Precision: 0.8260 |
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- Recall: 0.8279 |
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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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| 0.6549 | 1.7544 | 100 | 0.6289 | 0.82 | 0.6260 | 0.5191 | 0.7884 | |
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| 0.4616 | 3.5088 | 200 | 0.4192 | 0.8867 | 0.7296 | 0.6706 | 0.8 | |
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| 0.3101 | 5.2632 | 300 | 0.3071 | 0.9036 | 0.7318 | 0.7810 | 0.6884 | |
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| 0.2932 | 7.0175 | 400 | 0.2486 | 0.908 | 0.7460 | 0.7896 | 0.7070 | |
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| 0.2652 | 8.7719 | 500 | 0.2279 | 0.9138 | 0.7674 | 0.7921 | 0.7442 | |
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| 0.2253 | 10.5263 | 600 | 0.2100 | 0.9218 | 0.7859 | 0.8240 | 0.7512 | |
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| 0.2257 | 12.2807 | 700 | 0.1951 | 0.9249 | 0.8019 | 0.8085 | 0.7953 | |
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| 0.2468 | 14.0351 | 800 | 0.1906 | 0.9307 | 0.8199 | 0.8142 | 0.8256 | |
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| 0.1796 | 15.7895 | 900 | 0.1949 | 0.9276 | 0.8120 | 0.8055 | 0.8186 | |
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| 0.1888 | 17.5439 | 1000 | 0.1807 | 0.9307 | 0.8178 | 0.8216 | 0.8140 | |
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| 0.202 | 19.2982 | 1100 | 0.1772 | 0.9342 | 0.8287 | 0.8249 | 0.8326 | |
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| 0.1824 | 21.0526 | 1200 | 0.1826 | 0.9276 | 0.8080 | 0.8186 | 0.7977 | |
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| 0.1808 | 22.8070 | 1300 | 0.1682 | 0.9347 | 0.8297 | 0.8268 | 0.8326 | |
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| 0.1792 | 24.5614 | 1400 | 0.1688 | 0.9364 | 0.8324 | 0.8392 | 0.8256 | |
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| 0.1852 | 26.3158 | 1500 | 0.1725 | 0.9338 | 0.8269 | 0.8260 | 0.8279 | |
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| 0.177 | 28.0702 | 1600 | 0.1690 | 0.9351 | 0.8282 | 0.8381 | 0.8186 | |
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| 0.1857 | 29.8246 | 1700 | 0.1708 | 0.9298 | 0.8176 | 0.8119 | 0.8233 | |
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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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