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prakhardixit24
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urinary_carcinoma_classifier_g

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
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End of training

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  1. README.md +34 -7
  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: 0.5
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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 [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6249
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- - Accuracy: 0.5
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  ## Model description
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@@ -61,15 +61,42 @@ 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: 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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- | No log | 1.0 | 1 | 0.6688 | 1.0 |
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- | No log | 2.0 | 2 | 0.6426 | 0.75 |
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- | No log | 3.0 | 3 | 0.6249 | 0.5 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 1.0
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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 [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1879
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+ - Accuracy: 1.0
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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 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 1 | 0.6847 | 0.5 |
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+ | No log | 2.0 | 2 | 0.6741 | 0.75 |
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+ | No log | 3.0 | 3 | 0.6468 | 0.75 |
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+ | No log | 4.0 | 4 | 0.6268 | 0.75 |
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+ | No log | 5.0 | 5 | 0.6053 | 0.75 |
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+ | No log | 6.0 | 6 | 0.5515 | 0.75 |
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+ | No log | 7.0 | 7 | 0.5259 | 1.0 |
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+ | No log | 8.0 | 8 | 0.4513 | 1.0 |
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+ | No log | 9.0 | 9 | 0.4493 | 1.0 |
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+ | 0.1427 | 10.0 | 10 | 0.3979 | 1.0 |
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+ | 0.1427 | 11.0 | 11 | 0.4203 | 1.0 |
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+ | 0.1427 | 12.0 | 12 | 0.3690 | 1.0 |
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+ | 0.1427 | 13.0 | 13 | 0.2793 | 1.0 |
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+ | 0.1427 | 14.0 | 14 | 0.3143 | 1.0 |
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+ | 0.1427 | 15.0 | 15 | 0.2536 | 1.0 |
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+ | 0.1427 | 16.0 | 16 | 0.2509 | 1.0 |
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+ | 0.1427 | 17.0 | 17 | 0.2619 | 1.0 |
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+ | 0.1427 | 18.0 | 18 | 0.2187 | 1.0 |
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+ | 0.1427 | 19.0 | 19 | 0.3027 | 1.0 |
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+ | 0.055 | 20.0 | 20 | 0.2662 | 1.0 |
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+ | 0.055 | 21.0 | 21 | 0.3630 | 0.75 |
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+ | 0.055 | 22.0 | 22 | 0.4297 | 0.75 |
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+ | 0.055 | 23.0 | 23 | 0.3473 | 0.75 |
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+ | 0.055 | 24.0 | 24 | 0.4058 | 0.75 |
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+ | 0.055 | 25.0 | 25 | 0.3959 | 0.75 |
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+ | 0.055 | 26.0 | 26 | 0.2548 | 1.0 |
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+ | 0.055 | 27.0 | 27 | 0.1835 | 1.0 |
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+ | 0.055 | 28.0 | 28 | 0.1909 | 1.0 |
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+ | 0.055 | 29.0 | 29 | 0.4000 | 0.75 |
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+ | 0.029 | 30.0 | 30 | 0.1879 | 1.0 |
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  ### Framework versions
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