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README.md
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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: 0.9297777777777778
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- name: F1
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type: f1
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value: 0.8175519630484989
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- name: Precision
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type: precision
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value: 0.8119266055045872
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- name: Recall
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type: recall
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value: 0.8232558139534883
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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: 0.1708
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- Accuracy: 0.9298
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- F1: 0.8176
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- Precision: 0.8119
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- Recall: 0.8233
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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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runs/Oct22_15-45-30_2ae384978577/events.out.tfevents.1729611948.2ae384978577.344.0
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version https://git-lfs.github.com/spec/v1
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size
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version https://git-lfs.github.com/spec/v1
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size 50146
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