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msi-swinv2-tiny-pretrain

Image Classification
Transformers
Safetensors
swinv2
Generated from Trainer
Eval Results
Inference Endpoints
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End of training

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  1. README.md +11 -12
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9341225626740947
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2728
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- - Accuracy: 0.9341
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 64
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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: 4
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.1112 | 1.0 | 1562 | 0.2388 | 0.9206 |
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- | 0.0675 | 2.0 | 3125 | 0.2360 | 0.9386 |
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- | 0.0699 | 3.0 | 4687 | 0.2265 | 0.9432 |
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- | 0.0387 | 4.0 | 6248 | 0.2728 | 0.9341 |
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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: 0.7247910863509749
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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 [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 3.0045
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+ - Accuracy: 0.7248
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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  - seed: 42
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  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 512
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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: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.182 | 1.0 | 195 | -0.8006 | 0.7227 |
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+ | 0.1055 | 2.0 | 391 | -1.0724 | 0.7400 |
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+ | 0.0931 | 2.99 | 585 | 3.0045 | 0.7248 |
 
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  ### Framework versions
model.safetensors CHANGED
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