---
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