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vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter

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  1. README.md +21 -23
  2. model.safetensors +1 -1
  3. runs/May02_12-13-38_60f4804cf903/events.out.tfevents.1714653661.60f4804cf903.9256.1 +3 -0
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8339719029374202
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  - name: Recall
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  type: recall
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- value: 0.8339719029374202
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  - name: F1
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  type: f1
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- value: 0.8319571049551264
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  - name: Precision
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  type: precision
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- value: 0.8325133593723552
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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
@@ -44,11 +44,11 @@ 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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3507
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- - Accuracy: 0.8340
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- - Recall: 0.8340
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- - F1: 0.8320
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- - Precision: 0.8325
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  ## Model description
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@@ -71,8 +71,6 @@ The following hyperparameters were used during training:
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 32
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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
@@ -80,18 +78,18 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
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- |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
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- | No log | 0.9974 | 293 | 0.6168 | 0.7923 | 0.7923 | 0.7737 | 0.7684 |
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- | No log | 1.9983 | 587 | 0.4599 | 0.8110 | 0.8110 | 0.8056 | 0.8085 |
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- | No log | 2.9991 | 881 | 0.4305 | 0.8233 | 0.8233 | 0.8211 | 0.8250 |
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- | No log | 4.0 | 1175 | 0.3966 | 0.8365 | 0.8365 | 0.8323 | 0.8452 |
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- | No log | 4.9974 | 1468 | 0.4100 | 0.8221 | 0.8221 | 0.8195 | 0.8219 |
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- | No log | 5.9983 | 1762 | 0.3890 | 0.8412 | 0.8412 | 0.8375 | 0.8466 |
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- | No log | 6.9991 | 2056 | 0.3659 | 0.8357 | 0.8357 | 0.8335 | 0.8386 |
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- | No log | 8.0 | 2350 | 0.3562 | 0.8395 | 0.8395 | 0.8379 | 0.8403 |
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- | No log | 8.9974 | 2643 | 0.3613 | 0.8382 | 0.8382 | 0.8373 | 0.8391 |
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- | 0.4339 | 9.9745 | 2930 | 0.3405 | 0.8455 | 0.8455 | 0.8447 | 0.8467 |
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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: 0.8518518518518519
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  - name: Recall
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  type: recall
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+ value: 0.8518518518518519
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  - name: F1
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  type: f1
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+ value: 0.8508141812977819
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  - name: Precision
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  type: precision
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+ value: 0.8576385720576808
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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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3278
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+ - Accuracy: 0.8519
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+ - Recall: 0.8519
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+ - F1: 0.8508
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+ - Precision: 0.8576
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  ## Model description
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
 
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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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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
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+ | No log | 1.0 | 1175 | 0.5572 | 0.8076 | 0.8076 | 0.7937 | 0.8043 |
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+ | No log | 2.0 | 2350 | 0.4673 | 0.8284 | 0.8284 | 0.8271 | 0.8347 |
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+ | No log | 3.0 | 3525 | 0.4109 | 0.8344 | 0.8344 | 0.8301 | 0.8367 |
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+ | No log | 4.0 | 4700 | 0.3984 | 0.8382 | 0.8382 | 0.8339 | 0.8375 |
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+ | No log | 5.0 | 5875 | 0.3886 | 0.8412 | 0.8412 | 0.8398 | 0.8467 |
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+ | No log | 6.0 | 7050 | 0.3520 | 0.8493 | 0.8493 | 0.8481 | 0.8519 |
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+ | No log | 7.0 | 8225 | 0.4229 | 0.8416 | 0.8416 | 0.8399 | 0.8512 |
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+ | No log | 8.0 | 9400 | 0.3140 | 0.8612 | 0.8612 | 0.8600 | 0.8656 |
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+ | No log | 9.0 | 10575 | 0.3399 | 0.8421 | 0.8421 | 0.8403 | 0.8464 |
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+ | 0.4263 | 10.0 | 11750 | 0.3399 | 0.8476 | 0.8476 | 0.8468 | 0.8536 |
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  ### Framework versions
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