|
--- |
|
license: apache-2.0 |
|
base_model: google/vit-base-patch16-224-in21k |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: google-vit-base-patch16-224-in21k-batch_16_epoch_4_classes_24 |
|
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: 0.9683908045977011 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# google-vit-base-patch16-224-in21k-batch_16_epoch_4_classes_24 |
|
|
|
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 imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1385 |
|
- Accuracy: 0.9684 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.7768 | 0.07 | 100 | 0.7113 | 0.9282 | |
|
| 0.3925 | 0.14 | 200 | 0.4597 | 0.8908 | |
|
| 0.2437 | 0.21 | 300 | 0.3130 | 0.9282 | |
|
| 0.2105 | 0.28 | 400 | 0.3497 | 0.9023 | |
|
| 0.1744 | 0.35 | 500 | 0.3150 | 0.9124 | |
|
| 0.167 | 0.42 | 600 | 0.2949 | 0.9239 | |
|
| 0.1176 | 0.49 | 700 | 0.3204 | 0.9195 | |
|
| 0.077 | 0.56 | 800 | 0.3104 | 0.9253 | |
|
| 0.1113 | 0.63 | 900 | 0.1950 | 0.9511 | |
|
| 0.1172 | 0.7 | 1000 | 0.2692 | 0.9239 | |
|
| 0.0971 | 0.77 | 1100 | 0.3097 | 0.9267 | |
|
| 0.1143 | 0.84 | 1200 | 0.2072 | 0.9454 | |
|
| 0.1545 | 0.91 | 1300 | 0.2859 | 0.9253 | |
|
| 0.0794 | 0.97 | 1400 | 0.2893 | 0.9224 | |
|
| 0.0951 | 1.04 | 1500 | 0.2094 | 0.9483 | |
|
| 0.0657 | 1.11 | 1600 | 0.2714 | 0.9353 | |
|
| 0.0068 | 1.18 | 1700 | 0.2305 | 0.9425 | |
|
| 0.0511 | 1.25 | 1800 | 0.1682 | 0.9555 | |
|
| 0.0629 | 1.32 | 1900 | 0.2328 | 0.9454 | |
|
| 0.0373 | 1.39 | 2000 | 0.3263 | 0.9310 | |
|
| 0.0885 | 1.46 | 2100 | 0.2341 | 0.9454 | |
|
| 0.0433 | 1.53 | 2200 | 0.2670 | 0.9397 | |
|
| 0.0046 | 1.6 | 2300 | 0.2308 | 0.9468 | |
|
| 0.0054 | 1.67 | 2400 | 0.3182 | 0.9296 | |
|
| 0.0952 | 1.74 | 2500 | 0.2297 | 0.9411 | |
|
| 0.1361 | 1.81 | 2600 | 0.2058 | 0.9454 | |
|
| 0.1124 | 1.88 | 2700 | 0.1656 | 0.9598 | |
|
| 0.0339 | 1.95 | 2800 | 0.1933 | 0.9526 | |
|
| 0.0021 | 2.02 | 2900 | 0.1475 | 0.9569 | |
|
| 0.0248 | 2.09 | 3000 | 0.1806 | 0.9583 | |
|
| 0.0013 | 2.16 | 3100 | 0.1899 | 0.9526 | |
|
| 0.0035 | 2.23 | 3200 | 0.1391 | 0.9641 | |
|
| 0.0358 | 2.3 | 3300 | 0.1593 | 0.9684 | |
|
| 0.0026 | 2.37 | 3400 | 0.1927 | 0.9612 | |
|
| 0.001 | 2.44 | 3500 | 0.1756 | 0.9583 | |
|
| 0.0113 | 2.51 | 3600 | 0.1512 | 0.9713 | |
|
| 0.0009 | 2.58 | 3700 | 0.1540 | 0.9698 | |
|
| 0.0498 | 2.65 | 3800 | 0.1498 | 0.9641 | |
|
| 0.0084 | 2.72 | 3900 | 0.1435 | 0.9655 | |
|
| 0.001 | 2.79 | 4000 | 0.1199 | 0.9713 | |
|
| 0.0011 | 2.86 | 4100 | 0.1301 | 0.9655 | |
|
| 0.003 | 2.92 | 4200 | 0.1350 | 0.9727 | |
|
| 0.0025 | 2.99 | 4300 | 0.1764 | 0.9583 | |
|
| 0.0006 | 3.06 | 4400 | 0.1564 | 0.9713 | |
|
| 0.0006 | 3.13 | 4500 | 0.1524 | 0.9713 | |
|
| 0.0006 | 3.2 | 4600 | 0.1515 | 0.9727 | |
|
| 0.0006 | 3.27 | 4700 | 0.1633 | 0.9741 | |
|
| 0.0005 | 3.34 | 4800 | 0.1404 | 0.9713 | |
|
| 0.0005 | 3.41 | 4900 | 0.1586 | 0.9684 | |
|
| 0.0005 | 3.48 | 5000 | 0.1576 | 0.9655 | |
|
| 0.0005 | 3.55 | 5100 | 0.1505 | 0.9684 | |
|
| 0.0153 | 3.62 | 5200 | 0.1369 | 0.9684 | |
|
| 0.0005 | 3.69 | 5300 | 0.1579 | 0.9670 | |
|
| 0.0005 | 3.76 | 5400 | 0.1451 | 0.9698 | |
|
| 0.0005 | 3.83 | 5500 | 0.1417 | 0.9698 | |
|
| 0.0005 | 3.9 | 5600 | 0.1380 | 0.9698 | |
|
| 0.0004 | 3.97 | 5700 | 0.1385 | 0.9684 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|