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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: 0.8585666824869482
---
<!-- 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: 0.4344
- Accuracy: 0.8586
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6034 | 1.0 | 132 | 0.5659 | 0.8315 |
| 0.4903 | 2.0 | 265 | 0.4868 | 0.8472 |
| 0.5305 | 3.0 | 397 | 0.4742 | 0.8538 |
| 0.5424 | 4.0 | 530 | 0.4650 | 0.8552 |
| 0.4289 | 5.0 | 662 | 0.4508 | 0.8552 |
| 0.4275 | 6.0 | 795 | 0.4394 | 0.8590 |
| 0.4075 | 7.0 | 927 | 0.4767 | 0.8434 |
| 0.3649 | 8.0 | 1060 | 0.4462 | 0.8595 |
| 0.3934 | 9.0 | 1192 | 0.4323 | 0.8605 |
| 0.3436 | 9.96 | 1320 | 0.4344 | 0.8586 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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