metadata
license: apache-2.0
base_model: google/vit-large-patch32-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-brain-xray
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: sartajbhuvaji/Brain-Tumor-Classification
type: imagefolder
config: default
split: Testing
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7081218274111675
vit-large-brain-xray
This model is a fine-tuned version of google/vit-large-patch32-224-in21k on the sartajbhuvaji/Brain-Tumor-Classification dataset. It achieves the following results on the evaluation set:
- Loss: 1.0935
- Accuracy: 0.7081
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2144 | 0.5556 | 100 | 1.2679 | 0.6269 |
| 0.1091 | 1.1111 | 200 | 1.0935 | 0.7081 |
| 0.1078 | 1.6667 | 300 | 1.1237 | 0.7589 |
| 0.016 | 2.2222 | 400 | 1.2356 | 0.7563 |
| 0.0095 | 2.7778 | 500 | 1.2316 | 0.7589 |
| 0.0066 | 3.3333 | 600 | 1.3165 | 0.7589 |
| 0.0161 | 3.8889 | 700 | 1.3412 | 0.7614 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1