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---
license: apache-2.0
base_model: google/vit-base-patch32-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: adam_ViTB-32-224-in21k-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: 0.9568965517241379
---
<!-- 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_ViTB-32-224-in21k-2e-4-batch_16_epoch_4_classes_24
This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2485
- Accuracy: 0.9569
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1079 | 0.07 | 100 | 1.0858 | 0.8506 |
| 0.5485 | 0.14 | 200 | 0.6077 | 0.8649 |
| 0.4432 | 0.21 | 300 | 0.4838 | 0.8822 |
| 0.3463 | 0.28 | 400 | 0.4479 | 0.8764 |
| 0.3089 | 0.35 | 500 | 0.3687 | 0.8908 |
| 0.2341 | 0.42 | 600 | 0.4974 | 0.8635 |
| 0.2635 | 0.49 | 700 | 0.3657 | 0.8894 |
| 0.2038 | 0.56 | 800 | 0.2892 | 0.9080 |
| 0.1374 | 0.63 | 900 | 0.3617 | 0.8865 |
| 0.2198 | 0.7 | 1000 | 0.3332 | 0.9037 |
| 0.2532 | 0.77 | 1100 | 0.3292 | 0.9037 |
| 0.1897 | 0.84 | 1200 | 0.2957 | 0.9167 |
| 0.1718 | 0.91 | 1300 | 0.2398 | 0.9339 |
| 0.1637 | 0.97 | 1400 | 0.3514 | 0.9009 |
| 0.0794 | 1.04 | 1500 | 0.2616 | 0.9224 |
| 0.0541 | 1.11 | 1600 | 0.3213 | 0.9124 |
| 0.0475 | 1.18 | 1700 | 0.3717 | 0.9124 |
| 0.1251 | 1.25 | 1800 | 0.2938 | 0.9195 |
| 0.0712 | 1.32 | 1900 | 0.2988 | 0.9181 |
| 0.1021 | 1.39 | 2000 | 0.3862 | 0.9009 |
| 0.0073 | 1.46 | 2100 | 0.2492 | 0.9310 |
| 0.0114 | 1.53 | 2200 | 0.2902 | 0.9267 |
| 0.0487 | 1.6 | 2300 | 0.2301 | 0.9411 |
| 0.0856 | 1.67 | 2400 | 0.2682 | 0.9411 |
| 0.0028 | 1.74 | 2500 | 0.2948 | 0.9325 |
| 0.0028 | 1.81 | 2600 | 0.3002 | 0.9282 |
| 0.0279 | 1.88 | 2700 | 0.2797 | 0.9353 |
| 0.0768 | 1.95 | 2800 | 0.2721 | 0.9368 |
| 0.0251 | 2.02 | 2900 | 0.2896 | 0.9325 |
| 0.0645 | 2.09 | 3000 | 0.2802 | 0.9397 |
| 0.0022 | 2.16 | 3100 | 0.2387 | 0.9468 |
| 0.0073 | 2.23 | 3200 | 0.2074 | 0.9540 |
| 0.0016 | 2.3 | 3300 | 0.2271 | 0.9440 |
| 0.0016 | 2.37 | 3400 | 0.2513 | 0.9526 |
| 0.0656 | 2.44 | 3500 | 0.2889 | 0.9411 |
| 0.0013 | 2.51 | 3600 | 0.2750 | 0.9397 |
| 0.0014 | 2.58 | 3700 | 0.2463 | 0.9526 |
| 0.0011 | 2.65 | 3800 | 0.2723 | 0.9483 |
| 0.0012 | 2.72 | 3900 | 0.2631 | 0.9511 |
| 0.0012 | 2.79 | 4000 | 0.2584 | 0.9540 |
| 0.0012 | 2.86 | 4100 | 0.2572 | 0.9540 |
| 0.001 | 2.92 | 4200 | 0.2481 | 0.9569 |
| 0.0011 | 2.99 | 4300 | 0.2485 | 0.9569 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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