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HorcruxNo13
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swiftformer-xs

Image Classification
Transformers
PyTorch
swiftformer
Generated from Trainer
Eval Results
Model card Files
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  1. README.md +40 -25
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -23,13 +23,13 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.84
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  - name: Precision
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  type: precision
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- value: 0.8326758071649712
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  - name: Recall
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  type: recall
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- value: 0.84
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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
@@ -39,11 +39,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co/MBZUAI/swiftformer-xs) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4927
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- - Accuracy: 0.84
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- - Precision: 0.8327
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- - Recall: 0.84
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- - F1 Score: 0.8362
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  ## Model description
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@@ -71,32 +71,47 @@ The following hyperparameters were used during training:
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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: 15
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | No log | 1.0 | 4 | 0.7316 | 0.4208 | 0.8091 | 0.4208 | 0.4926 |
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- | No log | 2.0 | 8 | 0.6456 | 0.675 | 0.8159 | 0.675 | 0.7248 |
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- | No log | 3.0 | 12 | 0.5771 | 0.7917 | 0.8229 | 0.7917 | 0.8055 |
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- | 0.7138 | 4.0 | 16 | 0.4992 | 0.8333 | 0.8287 | 0.8333 | 0.8310 |
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- | 0.7138 | 5.0 | 20 | 0.4925 | 0.8292 | 0.8406 | 0.8292 | 0.8345 |
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- | 0.7138 | 6.0 | 24 | 0.4964 | 0.825 | 0.8435 | 0.825 | 0.8333 |
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- | 0.7138 | 7.0 | 28 | 0.4998 | 0.825 | 0.8435 | 0.825 | 0.8333 |
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- | 0.6892 | 8.0 | 32 | 0.4999 | 0.825 | 0.8481 | 0.825 | 0.8350 |
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- | 0.6892 | 9.0 | 36 | 0.5067 | 0.8167 | 0.8498 | 0.8167 | 0.8304 |
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- | 0.6892 | 10.0 | 40 | 0.5162 | 0.8125 | 0.8484 | 0.8125 | 0.8273 |
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- | 0.6892 | 11.0 | 44 | 0.5315 | 0.7792 | 0.8389 | 0.7792 | 0.8026 |
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- | 0.6782 | 12.0 | 48 | 0.5287 | 0.7875 | 0.8411 | 0.7875 | 0.8088 |
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- | 0.6782 | 13.0 | 52 | 0.5404 | 0.7708 | 0.8367 | 0.7708 | 0.7965 |
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- | 0.6782 | 14.0 | 56 | 0.5656 | 0.7667 | 0.8457 | 0.7667 | 0.7957 |
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- | 0.6742 | 15.0 | 60 | 0.5479 | 0.775 | 0.8427 | 0.775 | 0.8008 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.33.2
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.5
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  - Tokenizers 0.13.3
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.57
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  - name: Precision
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  type: precision
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+ value: 0.59945
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  - name: Recall
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  type: recall
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+ value: 0.57
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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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  This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co/MBZUAI/swiftformer-xs) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6833
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+ - Accuracy: 0.57
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+ - Precision: 0.5995
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+ - Recall: 0.57
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+ - F1 Score: 0.5828
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  ## Model description
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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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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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+ | No log | 1.0 | 4 | 0.6713 | 0.6292 | 0.6454 | 0.6292 | 0.6365 |
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+ | No log | 2.0 | 8 | 0.7142 | 0.475 | 0.6155 | 0.475 | 0.5020 |
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+ | No log | 3.0 | 12 | 0.7298 | 0.425 | 0.6026 | 0.425 | 0.4435 |
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+ | No log | 4.0 | 16 | 0.7389 | 0.4792 | 0.6408 | 0.4792 | 0.5023 |
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+ | No log | 5.0 | 20 | 0.7427 | 0.4792 | 0.6408 | 0.4792 | 0.5023 |
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+ | No log | 6.0 | 24 | 0.7235 | 0.5083 | 0.6424 | 0.5083 | 0.5348 |
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+ | No log | 7.0 | 28 | 0.6893 | 0.5875 | 0.6687 | 0.5875 | 0.6107 |
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+ | 0.6981 | 8.0 | 32 | 0.6816 | 0.6042 | 0.6847 | 0.6042 | 0.6264 |
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+ | 0.6981 | 9.0 | 36 | 0.6866 | 0.6042 | 0.6888 | 0.6042 | 0.6266 |
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+ | 0.6981 | 10.0 | 40 | 0.7005 | 0.575 | 0.6751 | 0.575 | 0.5996 |
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+ | 0.6981 | 11.0 | 44 | 0.7127 | 0.525 | 0.6554 | 0.525 | 0.5510 |
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+ | 0.6981 | 12.0 | 48 | 0.7098 | 0.5333 | 0.6595 | 0.5333 | 0.5593 |
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+ | 0.6981 | 13.0 | 52 | 0.7126 | 0.5208 | 0.6579 | 0.5208 | 0.5463 |
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+ | 0.6981 | 14.0 | 56 | 0.7114 | 0.5292 | 0.6575 | 0.5292 | 0.5551 |
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+ | 0.6656 | 15.0 | 60 | 0.6908 | 0.5667 | 0.6712 | 0.5667 | 0.5917 |
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+ | 0.6656 | 16.0 | 64 | 0.6804 | 0.5833 | 0.6749 | 0.5833 | 0.6073 |
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+ | 0.6656 | 17.0 | 68 | 0.6806 | 0.5958 | 0.6808 | 0.5958 | 0.6188 |
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+ | 0.6656 | 18.0 | 72 | 0.6884 | 0.5583 | 0.6629 | 0.5583 | 0.5838 |
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+ | 0.6656 | 19.0 | 76 | 0.6821 | 0.5708 | 0.6647 | 0.5708 | 0.5955 |
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+ | 0.6656 | 20.0 | 80 | 0.6663 | 0.6042 | 0.6806 | 0.6042 | 0.6261 |
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+ | 0.6656 | 21.0 | 84 | 0.6717 | 0.6 | 0.6787 | 0.6 | 0.6223 |
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+ | 0.6656 | 22.0 | 88 | 0.6682 | 0.6083 | 0.6826 | 0.6083 | 0.6299 |
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+ | 0.6443 | 23.0 | 92 | 0.6683 | 0.6167 | 0.6946 | 0.6167 | 0.6381 |
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+ | 0.6443 | 24.0 | 96 | 0.6733 | 0.6 | 0.6911 | 0.6 | 0.6230 |
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+ | 0.6443 | 25.0 | 100 | 0.6647 | 0.6083 | 0.6866 | 0.6083 | 0.6302 |
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+ | 0.6443 | 26.0 | 104 | 0.6729 | 0.6083 | 0.6907 | 0.6083 | 0.6305 |
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+ | 0.6443 | 27.0 | 108 | 0.6740 | 0.6042 | 0.6930 | 0.6042 | 0.6268 |
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+ | 0.6443 | 28.0 | 112 | 0.6809 | 0.5917 | 0.6916 | 0.5917 | 0.6153 |
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+ | 0.6443 | 29.0 | 116 | 0.6778 | 0.6042 | 0.7017 | 0.6042 | 0.6270 |
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+ | 0.6313 | 30.0 | 120 | 0.6794 | 0.5958 | 0.6935 | 0.5958 | 0.6192 |
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  ### Framework versions
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+ - Transformers 4.33.3
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.5
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  - Tokenizers 0.13.3
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