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---
license: apache-2.0
base_model: google/vit-large-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Adam_ViTL-16-224-1e-4-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. -->
# Adam_ViTL-16-224-1e-4-batch_16_epoch_4_classes_24
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1561
- 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7459 | 0.03 | 100 | 0.6501 | 0.8190 |
| 0.5929 | 0.07 | 200 | 0.4409 | 0.8836 |
| 0.2493 | 0.1 | 300 | 0.3525 | 0.9009 |
| 0.2142 | 0.14 | 400 | 0.3999 | 0.8779 |
| 0.3381 | 0.17 | 500 | 0.4229 | 0.8851 |
| 0.3445 | 0.21 | 600 | 0.2836 | 0.9195 |
| 0.2239 | 0.24 | 700 | 0.3989 | 0.8836 |
| 0.3475 | 0.28 | 800 | 0.2761 | 0.9210 |
| 0.0307 | 0.31 | 900 | 0.2963 | 0.9080 |
| 0.2957 | 0.35 | 1000 | 0.4865 | 0.8793 |
| 0.2431 | 0.38 | 1100 | 0.2740 | 0.9325 |
| 0.0729 | 0.42 | 1200 | 0.2630 | 0.9224 |
| 0.2757 | 0.45 | 1300 | 0.2515 | 0.9339 |
| 0.1763 | 0.49 | 1400 | 0.3826 | 0.9037 |
| 0.1481 | 0.52 | 1500 | 0.2282 | 0.9411 |
| 0.21 | 0.56 | 1600 | 0.2288 | 0.9454 |
| 0.2224 | 0.59 | 1700 | 0.3142 | 0.9296 |
| 0.0815 | 0.63 | 1800 | 0.2412 | 0.9411 |
| 0.0687 | 0.66 | 1900 | 0.2835 | 0.9353 |
| 0.3321 | 0.7 | 2000 | 0.3000 | 0.9282 |
| 0.1174 | 0.73 | 2100 | 0.2154 | 0.9440 |
| 0.0694 | 0.77 | 2200 | 0.2062 | 0.9497 |
| 0.0351 | 0.8 | 2300 | 0.1716 | 0.9511 |
| 0.088 | 0.84 | 2400 | 0.1410 | 0.9511 |
| 0.0856 | 0.87 | 2500 | 0.2342 | 0.9411 |
| 0.2248 | 0.91 | 2600 | 0.1954 | 0.9497 |
| 0.1188 | 0.94 | 2700 | 0.2655 | 0.9425 |
| 0.0322 | 0.98 | 2800 | 0.2535 | 0.9440 |
| 0.0739 | 1.01 | 2900 | 0.1640 | 0.9526 |
| 0.0352 | 1.04 | 3000 | 0.1760 | 0.9612 |
| 0.0007 | 1.08 | 3100 | 0.1593 | 0.9641 |
| 0.0107 | 1.11 | 3200 | 0.1970 | 0.9569 |
| 0.0027 | 1.15 | 3300 | 0.1603 | 0.9583 |
| 0.0005 | 1.18 | 3400 | 0.1550 | 0.9583 |
| 0.0637 | 1.22 | 3500 | 0.1874 | 0.9583 |
| 0.0006 | 1.25 | 3600 | 0.1829 | 0.9583 |
| 0.0626 | 1.29 | 3700 | 0.2311 | 0.9526 |
| 0.1023 | 1.32 | 3800 | 0.2325 | 0.9483 |
| 0.0014 | 1.36 | 3900 | 0.1556 | 0.9698 |
| 0.0186 | 1.39 | 4000 | 0.2151 | 0.9483 |
| 0.0005 | 1.43 | 4100 | 0.1369 | 0.9670 |
| 0.0005 | 1.46 | 4200 | 0.1240 | 0.9727 |
| 0.0004 | 1.5 | 4300 | 0.2019 | 0.9612 |
| 0.0008 | 1.53 | 4400 | 0.1361 | 0.9713 |
| 0.013 | 1.57 | 4500 | 0.1343 | 0.9684 |
| 0.014 | 1.6 | 4600 | 0.1553 | 0.9670 |
| 0.0005 | 1.64 | 4700 | 0.1528 | 0.9655 |
| 0.0003 | 1.67 | 4800 | 0.1586 | 0.9641 |
| 0.0009 | 1.71 | 4900 | 0.1598 | 0.9655 |
| 0.0003 | 1.74 | 5000 | 0.1727 | 0.9641 |
| 0.0003 | 1.78 | 5100 | 0.1521 | 0.9727 |
| 0.0076 | 1.81 | 5200 | 0.1534 | 0.9698 |
| 0.0003 | 1.85 | 5300 | 0.1656 | 0.9655 |
| 0.0003 | 1.88 | 5400 | 0.1833 | 0.9641 |
| 0.0003 | 1.92 | 5500 | 0.1719 | 0.9670 |
| 0.0003 | 1.95 | 5600 | 0.1565 | 0.9684 |
| 0.0003 | 1.99 | 5700 | 0.1561 | 0.9684 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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