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Tharsana
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vit-base-brain-tumor

Image Classification
Transformers
TensorBoard
Safetensors
vit
brain-tumor
medical-imaging
Generated from Trainer
Eval Results (legacy)
Model card Files
xet
 Metrics Community
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  1. README.md +21 -24
  2. model.safetensors +1 -1
  3. runs/May24_11-08-17_ip-10-192-11-87/events.out.tfevents.1748084904.ip-10-192-11-87.1786.27 +2 -2
  4. runs/May24_11-08-17_ip-10-192-11-87/events.out.tfevents.1748085215.ip-10-192-11-87.1786.28 +3 -0
README.md CHANGED
@@ -3,9 +3,6 @@ library_name: transformers
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224
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  tags:
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- - image-classification
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- - brain-tumor
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- - medical-imaging
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  - generated_from_trainer
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  datasets:
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  - imagefolder
@@ -18,7 +15,7 @@ model-index:
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  name: Image Classification
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  type: image-classification
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  dataset:
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- name: your-username/brain-tumor
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  type: imagefolder
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  config: default
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  split: train
@@ -26,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.967741935483871
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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
@@ -34,10 +31,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base-brain-tumor
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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 your-username/brain-tumor dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1629
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- - Accuracy: 0.9677
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  ## Model description
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@@ -56,7 +53,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0003
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  - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
@@ -68,21 +65,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.2046 | 1.0 | 47 | 0.2298 | 0.9239 |
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- | 0.1805 | 2.0 | 94 | 0.2084 | 0.9130 |
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- | 0.1828 | 3.0 | 141 | 0.1987 | 0.9130 |
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- | 0.1703 | 4.0 | 188 | 0.1941 | 0.9239 |
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- | 0.1662 | 5.0 | 235 | 0.2039 | 0.9565 |
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- | 0.1633 | 6.0 | 282 | 0.1899 | 0.9130 |
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- | 0.1673 | 7.0 | 329 | 0.1863 | 0.9130 |
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- | 0.1521 | 8.0 | 376 | 0.1789 | 0.9348 |
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- | 0.159 | 9.0 | 423 | 0.1748 | 0.9239 |
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- | 0.1541 | 10.0 | 470 | 0.1741 | 0.9348 |
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- | 0.1482 | 11.0 | 517 | 0.1750 | 0.9674 |
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- | 0.1551 | 12.0 | 564 | 0.1727 | 0.9674 |
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- | 0.1634 | 13.0 | 611 | 0.1718 | 0.9674 |
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- | 0.1498 | 14.0 | 658 | 0.1706 | 0.9674 |
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- | 0.1493 | 15.0 | 705 | 0.1703 | 0.9674 |
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224
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  tags:
 
 
 
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  - generated_from_trainer
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  datasets:
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  - imagefolder
 
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  name: Image Classification
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  type: image-classification
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  dataset:
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+ name: imagefolder
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  type: imagefolder
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  config: default
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  split: train
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.862453531598513
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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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  # vit-base-brain-tumor
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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: 0.3625
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+ - Accuracy: 0.8625
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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: 2e-05
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  - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6486 | 1.0 | 135 | 0.6040 | 0.7127 |
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+ | 0.5363 | 2.0 | 270 | 0.5300 | 0.8060 |
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+ | 0.4727 | 3.0 | 405 | 0.4835 | 0.8209 |
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+ | 0.4318 | 4.0 | 540 | 0.4525 | 0.8358 |
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+ | 0.4027 | 5.0 | 675 | 0.4298 | 0.8358 |
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+ | 0.3791 | 6.0 | 810 | 0.4145 | 0.8358 |
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+ | 0.3764 | 7.0 | 945 | 0.4017 | 0.8433 |
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+ | 0.3575 | 8.0 | 1080 | 0.3913 | 0.8433 |
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+ | 0.352 | 9.0 | 1215 | 0.3840 | 0.8470 |
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+ | 0.3466 | 10.0 | 1350 | 0.3775 | 0.8433 |
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+ | 0.3447 | 11.0 | 1485 | 0.3733 | 0.8433 |
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+ | 0.3361 | 12.0 | 1620 | 0.3697 | 0.8470 |
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+ | 0.3352 | 13.0 | 1755 | 0.3672 | 0.8470 |
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+ | 0.3347 | 14.0 | 1890 | 0.3657 | 0.8470 |
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+ | 0.3365 | 15.0 | 2025 | 0.3653 | 0.8470 |
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  ### Framework versions
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