|
--- |
|
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 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# test-trainer |
|
|
|
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 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 |
|
|
