metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: test-trainer
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Chess
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9107142857142857
- name: F1
type: f1
value: 0.9121670865142396
- name: Precision
type: precision
value: 0.9171626984126985
- name: Recall
type: recall
value: 0.9107142857142857
test-trainer
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Chess dataset. It achieves the following results on the evaluation set:
- Loss: 0.7291
- Accuracy: 0.9107
- F1: 0.9122
- Precision: 0.9172
- Recall: 0.9107
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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 50 | 1.6720 | 0.4821 | 0.4134 | 0.3870 | 0.4821 |
| No log | 2.0 | 100 | 1.4652 | 0.6429 | 0.6126 | 0.7414 | 0.6429 |
| No log | 3.0 | 150 | 1.1742 | 0.7321 | 0.7210 | 0.7792 | 0.7321 |
| No log | 4.0 | 200 | 0.9813 | 0.8393 | 0.8433 | 0.8589 | 0.8393 |
| No log | 5.0 | 250 | 0.8312 | 0.8214 | 0.8164 | 0.8516 | 0.8214 |
| No log | 6.0 | 300 | 0.7291 | 0.9107 | 0.9122 | 0.9172 | 0.9107 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.2.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3