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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-beans
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: 0.90272614622057
---
<!-- 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. -->
# vit-base-beans
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: 0.3643
- Accuracy: 0.9027
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 3.5924 | 0.0620 | 100 | 3.5675 | 0.1927 |
| 3.0189 | 0.1239 | 200 | 3.0313 | 0.3047 |
| 2.5541 | 0.1859 | 300 | 2.5575 | 0.3956 |
| 2.114 | 0.2478 | 400 | 2.2332 | 0.4571 |
| 1.9624 | 0.3098 | 500 | 1.9455 | 0.5596 |
| 1.6749 | 0.3717 | 600 | 1.7370 | 0.5787 |
| 1.5852 | 0.4337 | 700 | 1.4947 | 0.6439 |
| 1.1875 | 0.4957 | 800 | 1.4151 | 0.6468 |
| 1.5114 | 0.5576 | 900 | 1.2709 | 0.6820 |
| 1.3122 | 0.6196 | 1000 | 1.1940 | 0.6939 |
| 1.0721 | 0.6815 | 1100 | 1.0757 | 0.7261 |
| 0.8249 | 0.7435 | 1200 | 0.9666 | 0.7576 |
| 0.7944 | 0.8055 | 1300 | 0.9101 | 0.7708 |
| 0.8032 | 0.8674 | 1400 | 0.9011 | 0.7691 |
| 0.7479 | 0.9294 | 1500 | 0.7409 | 0.8067 |
| 0.5997 | 0.9913 | 1600 | 0.7326 | 0.8110 |
| 0.5005 | 1.0533 | 1700 | 0.6769 | 0.8211 |
| 0.4107 | 1.1152 | 1800 | 0.6375 | 0.8374 |
| 0.4596 | 1.1772 | 1900 | 0.6302 | 0.8304 |
| 0.2544 | 1.2392 | 2000 | 0.5805 | 0.8400 |
| 0.2983 | 1.3011 | 2100 | 0.5480 | 0.8501 |
| 0.3214 | 1.3631 | 2200 | 0.5053 | 0.8683 |
| 0.2384 | 1.4250 | 2300 | 0.4929 | 0.8713 |
| 0.2397 | 1.4870 | 2400 | 0.4664 | 0.8742 |
| 0.3448 | 1.5489 | 2500 | 0.4690 | 0.8755 |
| 0.3129 | 1.6109 | 2600 | 0.4351 | 0.8843 |
| 0.1027 | 1.6729 | 2700 | 0.4311 | 0.8846 |
| 0.2086 | 1.7348 | 2800 | 0.4088 | 0.8897 |
| 0.1683 | 1.7968 | 2900 | 0.4133 | 0.8919 |
| 0.2767 | 1.8587 | 3000 | 0.3851 | 0.8964 |
| 0.1582 | 1.9207 | 3100 | 0.3703 | 0.9018 |
| 0.1421 | 1.9827 | 3200 | 0.3643 | 0.9027 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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