metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k_GI_diagnosis
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: 0.88125
vit-base-patch16-224-in21k_GI_diagnosis
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: 0.5797
- Accuracy: 0.8812
- Weighted f1: 0.8740
- Micro f1: 0.8812
- Macro f1: 0.8740
- Weighted recall: 0.8812
- Micro recall: 0.8812
- Macro recall: 0.8813
- Weighted precision: 0.9157
- Micro precision: 0.8812
- Macro precision: 0.9157
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.3805 | 1.0 | 200 | 0.5006 | 0.8638 | 0.8531 | 0.8638 | 0.8531 | 0.8638 | 0.8638 | 0.8638 | 0.9111 | 0.8638 | 0.9111 |
| 1.3805 | 2.0 | 400 | 0.2538 | 0.9375 | 0.9365 | 0.9375 | 0.9365 | 0.9375 | 0.9375 | 0.9375 | 0.9455 | 0.9375 | 0.9455 |
| 0.0628 | 3.0 | 600 | 0.5797 | 0.8812 | 0.8740 | 0.8812 | 0.8740 | 0.8812 | 0.8812 | 0.8813 | 0.9157 | 0.8812 | 0.9157 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.12.1