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car_identified_model_11

Image Classification
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Safetensors
mobilevit
Generated from Trainer
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  2. model.safetensors +1 -1
README.md CHANGED
@@ -5,9 +5,28 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - imagefolder
 
 
 
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  model-index:
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  - name: car_identified_model_11
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -16,6 +35,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # car_identified_model_11
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  This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on the imagefolder dataset.
 
 
 
 
 
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  ## Model description
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@@ -42,7 +66,53 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 128
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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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- - num_epochs: 200
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - generated_from_trainer
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  datasets:
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  - imagefolder
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+ metrics:
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+ - f1
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+ - accuracy
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  model-index:
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  - name: car_identified_model_11
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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: imagefolder
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+ type: imagefolder
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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: F1
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+ type: f1
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+ value: 0.7241379310344829
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.08333333333333333
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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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  # car_identified_model_11
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  This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6686
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+ - F1: 0.7241
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+ - Roc Auc: 0.6667
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+ - Accuracy: 0.0833
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  ## Model description
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  - total_train_batch_size: 128
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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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+ - num_epochs: 50
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.2582 | 1.0 | 1 | 0.6938 | 0.5926 | 0.5417 | 0.0833 |
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+ | 0.2582 | 2.0 | 2 | 0.6937 | 0.6415 | 0.6042 | 0.0833 |
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+ | 0.2582 | 3.0 | 4 | 0.6918 | 0.6429 | 0.5833 | 0.0 |
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+ | 0.2582 | 4.0 | 5 | 0.6893 | 0.6316 | 0.5625 | 0.0 |
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+ | 0.2582 | 5.0 | 6 | 0.6871 | 0.6667 | 0.6042 | 0.0833 |
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+ | 0.2582 | 6.0 | 8 | 0.6844 | 0.6786 | 0.625 | 0.0833 |
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+ | 0.2582 | 7.0 | 9 | 0.6827 | 0.7018 | 0.6458 | 0.0833 |
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+ | 0.2582 | 8.0 | 10 | 0.6817 | 0.6667 | 0.6042 | 0.0833 |
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+ | 0.2582 | 9.0 | 11 | 0.6809 | 0.6897 | 0.625 | 0.0833 |
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+ | 0.2582 | 10.0 | 12 | 0.6804 | 0.6897 | 0.625 | 0.0833 |
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+ | 0.2582 | 11.0 | 14 | 0.6792 | 0.6897 | 0.625 | 0.0833 |
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+ | 0.2582 | 12.0 | 15 | 0.6787 | 0.7119 | 0.6458 | 0.0833 |
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+ | 0.2582 | 13.0 | 16 | 0.6780 | 0.7119 | 0.6458 | 0.0833 |
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+ | 0.2582 | 14.0 | 18 | 0.6771 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 15.0 | 19 | 0.6765 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 16.0 | 20 | 0.6762 | 0.7458 | 0.6875 | 0.0833 |
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+ | 0.2582 | 17.0 | 21 | 0.6758 | 0.7333 | 0.6667 | 0.0833 |
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+ | 0.2582 | 18.0 | 22 | 0.6753 | 0.7458 | 0.6875 | 0.0833 |
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+ | 0.2582 | 19.0 | 24 | 0.6744 | 0.7333 | 0.6667 | 0.0833 |
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+ | 0.2582 | 20.0 | 25 | 0.6740 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 21.0 | 26 | 0.6737 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 22.0 | 28 | 0.6733 | 0.7458 | 0.6875 | 0.0833 |
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+ | 0.2582 | 23.0 | 29 | 0.6725 | 0.7458 | 0.6875 | 0.0833 |
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+ | 0.2582 | 24.0 | 30 | 0.6720 | 0.7368 | 0.6875 | 0.0833 |
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+ | 0.2582 | 25.0 | 31 | 0.6719 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 26.0 | 32 | 0.6713 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 27.0 | 34 | 0.6711 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 28.0 | 35 | 0.6705 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 29.0 | 36 | 0.6700 | 0.7368 | 0.6875 | 0.0833 |
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+ | 0.2582 | 30.0 | 38 | 0.6696 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 31.0 | 39 | 0.6695 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 32.0 | 40 | 0.6693 | 0.7368 | 0.6875 | 0.1667 |
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+ | 0.2582 | 33.0 | 41 | 0.6692 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 34.0 | 42 | 0.6694 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 35.0 | 44 | 0.6692 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 36.0 | 45 | 0.6693 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 37.0 | 46 | 0.6693 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 38.0 | 48 | 0.6690 | 0.7241 | 0.6667 | 0.0833 |
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+ | 0.2582 | 39.0 | 49 | 0.6689 | 0.7368 | 0.6875 | 0.0833 |
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+ | 0.2582 | 40.0 | 50 | 0.6686 | 0.7241 | 0.6667 | 0.0833 |
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+
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  ### Framework versions
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