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HorcruxNo13
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swiftformer-xs

Image Classification
Transformers
PyTorch
swiftformer
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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  1. README.md +23 -23
  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: 0.49666666666666665
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  - name: Precision
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  type: precision
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- value: 0.576645299145299
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  - name: Recall
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  type: recall
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- value: 0.49666666666666665
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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: 0.7148
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- - Accuracy: 0.4967
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- - Precision: 0.5766
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- - Recall: 0.4967
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- - F1 Score: 0.5199
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  ## Model description
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@@ -77,21 +77,21 @@ The following hyperparameters were used during training:
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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 | 0.7206 | 0.4875 | 0.5692 | 0.4875 | 0.5143 |
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- | No log | 2.0 | 8 | 0.8267 | 0.3083 | 0.5185 | 0.3083 | 0.2536 |
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- | No log | 3.0 | 12 | 0.8364 | 0.3042 | 0.5217 | 0.3042 | 0.2378 |
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- | 0.7296 | 4.0 | 16 | 0.7752 | 0.3583 | 0.5585 | 0.3583 | 0.3480 |
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- | 0.7296 | 5.0 | 20 | 0.7457 | 0.4083 | 0.5889 | 0.4083 | 0.4184 |
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- | 0.7296 | 6.0 | 24 | 0.7550 | 0.3958 | 0.5997 | 0.3958 | 0.3947 |
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- | 0.7296 | 7.0 | 28 | 0.7793 | 0.3375 | 0.5840 | 0.3375 | 0.2911 |
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- | 0.7104 | 8.0 | 32 | 0.7950 | 0.3125 | 0.5672 | 0.3125 | 0.2374 |
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- | 0.7104 | 9.0 | 36 | 0.7954 | 0.3042 | 0.5574 | 0.3042 | 0.2180 |
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- | 0.7104 | 10.0 | 40 | 0.7952 | 0.3125 | 0.5932 | 0.3125 | 0.2273 |
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- | 0.7104 | 11.0 | 44 | 0.8032 | 0.3083 | 0.5825 | 0.3083 | 0.2200 |
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- | 0.6998 | 12.0 | 48 | 0.8041 | 0.3083 | 0.5825 | 0.3083 | 0.2200 |
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- | 0.6998 | 13.0 | 52 | 0.8064 | 0.3208 | 0.6495 | 0.3208 | 0.2315 |
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- | 0.6998 | 14.0 | 56 | 0.8158 | 0.3125 | 0.6309 | 0.3125 | 0.2167 |
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- | 0.6994 | 15.0 | 60 | 0.8079 | 0.3208 | 0.6495 | 0.3208 | 0.2315 |
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  ### Framework versions
 
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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
 
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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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  | 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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