metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-base-patch16-224-in21k_covid_19_ct_scans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8466666666666667
- name: F1
type: f1
value: 0.8571428571428571
- name: Recall
type: recall
value: 0.8625
- name: Precision
type: precision
value: 0.8518518518518519
vit-base-patch16-224-in21k_covid_19_ct_scans
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3062
- Accuracy: 0.8467
- F1: 0.8571
- Recall: 0.8625
- Precision: 0.8519
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.6963 | 1.0 | 19 | 0.5246 | 0.76 | 0.7857 | 0.825 | 0.75 |
| 0.6963 | 2.0 | 38 | 0.3911 | 0.8333 | 0.8322 | 0.775 | 0.8986 |
| 0.6963 | 3.0 | 57 | 0.3062 | 0.8467 | 0.8571 | 0.8625 | 0.8519 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1