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---
license: apache-2.0
base_model: google/vit-large-patch32-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Adam_ViTL-32_224-2e-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: 0.9482758620689655
---
<!-- 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-32_224-2e-4-batch_16_epoch_4_classes_24
This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co./google/vit-large-patch32-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2083
- Accuracy: 0.9483
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7984 | 0.07 | 100 | 0.8039 | 0.8606 |
| 0.4352 | 0.14 | 200 | 0.5735 | 0.8463 |
| 0.3651 | 0.21 | 300 | 0.3951 | 0.8937 |
| 0.3133 | 0.28 | 400 | 0.4525 | 0.8894 |
| 0.2641 | 0.35 | 500 | 0.3618 | 0.9023 |
| 0.2104 | 0.42 | 600 | 0.4240 | 0.8922 |
| 0.1787 | 0.49 | 700 | 0.4070 | 0.8879 |
| 0.1412 | 0.56 | 800 | 0.3259 | 0.9124 |
| 0.2121 | 0.63 | 900 | 0.3575 | 0.8994 |
| 0.1491 | 0.7 | 1000 | 0.2769 | 0.9152 |
| 0.268 | 0.77 | 1100 | 0.3432 | 0.9195 |
| 0.2378 | 0.84 | 1200 | 0.3622 | 0.9109 |
| 0.0812 | 0.91 | 1300 | 0.2857 | 0.9210 |
| 0.127 | 0.97 | 1400 | 0.2787 | 0.9253 |
| 0.0256 | 1.04 | 1500 | 0.3116 | 0.9267 |
| 0.027 | 1.11 | 1600 | 0.2889 | 0.9282 |
| 0.0508 | 1.18 | 1700 | 0.3048 | 0.9310 |
| 0.0932 | 1.25 | 1800 | 0.2732 | 0.9382 |
| 0.0745 | 1.32 | 1900 | 0.3275 | 0.9195 |
| 0.0675 | 1.39 | 2000 | 0.2505 | 0.9440 |
| 0.0347 | 1.46 | 2100 | 0.2686 | 0.9382 |
| 0.0121 | 1.53 | 2200 | 0.2888 | 0.9454 |
| 0.1104 | 1.6 | 2300 | 0.2375 | 0.9440 |
| 0.0778 | 1.67 | 2400 | 0.2345 | 0.9411 |
| 0.0029 | 1.74 | 2500 | 0.2924 | 0.9282 |
| 0.0063 | 1.81 | 2600 | 0.2867 | 0.9353 |
| 0.0394 | 1.88 | 2700 | 0.3384 | 0.9224 |
| 0.0043 | 1.95 | 2800 | 0.2855 | 0.9195 |
| 0.025 | 2.02 | 2900 | 0.3218 | 0.9296 |
| 0.0096 | 2.09 | 3000 | 0.2810 | 0.9368 |
| 0.0018 | 2.16 | 3100 | 0.1971 | 0.9526 |
| 0.0102 | 2.23 | 3200 | 0.2175 | 0.9497 |
| 0.0016 | 2.3 | 3300 | 0.2341 | 0.9454 |
| 0.0024 | 2.37 | 3400 | 0.2607 | 0.9425 |
| 0.0024 | 2.44 | 3500 | 0.2380 | 0.9440 |
| 0.0019 | 2.51 | 3600 | 0.2422 | 0.9382 |
| 0.0062 | 2.58 | 3700 | 0.2191 | 0.9483 |
| 0.0416 | 2.65 | 3800 | 0.2491 | 0.9483 |
| 0.002 | 2.72 | 3900 | 0.2201 | 0.9497 |
| 0.0013 | 2.79 | 4000 | 0.2242 | 0.9468 |
| 0.0012 | 2.86 | 4100 | 0.2182 | 0.9440 |
| 0.0011 | 2.92 | 4200 | 0.2079 | 0.9497 |
| 0.001 | 2.99 | 4300 | 0.2083 | 0.9483 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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