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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-weldclassifyv4
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: 0.8093525179856115
---
<!-- 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-weldclassifyv4
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: 0.5265
- Accuracy: 0.8094
## 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: 13
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1126 | 0.6410 | 100 | 1.0171 | 0.5504 |
| 0.8229 | 1.2821 | 200 | 0.7307 | 0.6942 |
| 0.7224 | 1.9231 | 300 | 0.6399 | 0.7122 |
| 0.3909 | 2.5641 | 400 | 0.5400 | 0.7734 |
| 0.237 | 3.2051 | 500 | 0.6716 | 0.7626 |
| 0.4056 | 3.8462 | 600 | 0.5265 | 0.8094 |
| 0.1764 | 4.4872 | 700 | 0.9174 | 0.7446 |
| 0.0546 | 5.1282 | 800 | 0.6644 | 0.8237 |
| 0.0436 | 5.7692 | 900 | 0.6923 | 0.8345 |
| 0.0661 | 6.4103 | 1000 | 0.6784 | 0.8345 |
| 0.0167 | 7.0513 | 1100 | 0.7115 | 0.8309 |
| 0.0744 | 7.6923 | 1200 | 0.6341 | 0.8525 |
| 0.0047 | 8.3333 | 1300 | 0.6402 | 0.8597 |
| 0.0039 | 8.9744 | 1400 | 0.5958 | 0.8849 |
| 0.0029 | 9.6154 | 1500 | 0.6158 | 0.8885 |
| 0.0027 | 10.2564 | 1600 | 0.6189 | 0.8885 |
| 0.0025 | 10.8974 | 1700 | 0.6309 | 0.8885 |
| 0.0024 | 11.5385 | 1800 | 0.6356 | 0.8885 |
| 0.0023 | 12.1795 | 1900 | 0.6382 | 0.8885 |
| 0.0023 | 12.8205 | 2000 | 0.6399 | 0.8885 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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