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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: rsna-intracranial-hemorrhage-detection
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6151724137931035
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rsna-intracranial-hemorrhage-detection
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.
It achieves the following results on the evaluation set:
- Loss: 1.2164
- Accuracy: 0.6152
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5655 | 1.0 | 238 | 1.5235 | 0.4039 |
| 1.3848 | 2.0 | 477 | 1.3622 | 0.4692 |
| 1.2812 | 3.0 | 716 | 1.2811 | 0.5150 |
| 1.2039 | 4.0 | 955 | 1.1795 | 0.5556 |
| 1.1641 | 5.0 | 1193 | 1.1627 | 0.5534 |
| 1.1961 | 6.0 | 1432 | 1.1393 | 0.5705 |
| 1.1382 | 7.0 | 1671 | 1.0921 | 0.5804 |
| 0.9653 | 8.0 | 1910 | 1.0790 | 0.5876 |
| 0.9346 | 9.0 | 2148 | 1.0727 | 0.5931 |
| 0.9083 | 10.0 | 2387 | 1.0605 | 0.5994 |
| 0.8936 | 11.0 | 2626 | 1.0147 | 0.6146 |
| 0.8504 | 12.0 | 2865 | 1.0849 | 0.5818 |
| 0.8544 | 13.0 | 3103 | 1.0349 | 0.6052 |
| 0.7884 | 14.0 | 3342 | 1.0435 | 0.6074 |
| 0.7974 | 15.0 | 3581 | 1.0082 | 0.6127 |
| 0.7921 | 16.0 | 3820 | 1.0438 | 0.6017 |
| 0.709 | 17.0 | 4058 | 1.0484 | 0.6094 |
| 0.6646 | 18.0 | 4297 | 1.0554 | 0.6221 |
| 0.6832 | 19.0 | 4536 | 1.0455 | 0.6124 |
| 0.7076 | 20.0 | 4775 | 1.0905 | 0.6 |
| 0.7442 | 21.0 | 5013 | 1.1094 | 0.6008 |
| 0.6332 | 22.0 | 5252 | 1.0777 | 0.6063 |
| 0.6417 | 23.0 | 5491 | 1.0765 | 0.6141 |
| 0.6267 | 24.0 | 5730 | 1.1057 | 0.6091 |
| 0.6082 | 25.0 | 5968 | 1.0962 | 0.6171 |
| 0.6191 | 26.0 | 6207 | 1.1178 | 0.6039 |
| 0.5654 | 27.0 | 6446 | 1.1386 | 0.5948 |
| 0.5776 | 28.0 | 6685 | 1.1121 | 0.6105 |
| 0.5531 | 29.0 | 6923 | 1.1497 | 0.6030 |
| 0.6275 | 30.0 | 7162 | 1.1796 | 0.6028 |
| 0.5373 | 31.0 | 7401 | 1.1306 | 0.6132 |
| 0.4775 | 32.0 | 7640 | 1.1523 | 0.6058 |
| 0.5469 | 33.0 | 7878 | 1.1634 | 0.6127 |
| 0.4934 | 34.0 | 8117 | 1.1853 | 0.616 |
| 0.5233 | 35.0 | 8356 | 1.2018 | 0.6055 |
| 0.4896 | 36.0 | 8595 | 1.1585 | 0.6108 |
| 0.5122 | 37.0 | 8833 | 1.1874 | 0.6146 |
| 0.4726 | 38.0 | 9072 | 1.1608 | 0.6193 |
| 0.4372 | 39.0 | 9311 | 1.2403 | 0.6132 |
| 0.498 | 40.0 | 9550 | 1.1752 | 0.6201 |
| 0.4813 | 41.0 | 9788 | 1.2005 | 0.6166 |
| 0.4762 | 42.0 | 10027 | 1.2285 | 0.6022 |
| 0.4852 | 43.0 | 10266 | 1.2192 | 0.6119 |
| 0.4332 | 44.0 | 10505 | 1.2391 | 0.6218 |
| 0.3998 | 45.0 | 10743 | 1.1779 | 0.6196 |
| 0.4467 | 46.0 | 10982 | 1.2048 | 0.6284 |
| 0.4332 | 47.0 | 11221 | 1.2302 | 0.6188 |
| 0.4529 | 48.0 | 11460 | 1.2220 | 0.6188 |
| 0.4281 | 49.0 | 11698 | 1.2013 | 0.624 |
| 0.4199 | 49.84 | 11900 | 1.2164 | 0.6152 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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