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---
library_name: transformers
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: 0.920863309352518
---
<!-- 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: 0.3301
- Accuracy: 0.9209
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8207 | 0.6410 | 100 | 1.0336 | 0.5647 |
| 0.6506 | 1.2821 | 200 | 1.1982 | 0.5791 |
| 0.5324 | 1.9231 | 300 | 0.6060 | 0.7770 |
| 0.2486 | 2.5641 | 400 | 0.7294 | 0.7518 |
| 0.1366 | 3.2051 | 500 | 0.4832 | 0.8417 |
| 0.3124 | 3.8462 | 600 | 0.8676 | 0.7626 |
| 0.0296 | 4.4872 | 700 | 0.4233 | 0.8885 |
| 0.0723 | 5.1282 | 800 | 0.4470 | 0.8849 |
| 0.0342 | 5.7692 | 900 | 0.3406 | 0.9173 |
| 0.0055 | 6.4103 | 1000 | 0.3301 | 0.9209 |
| 0.0048 | 7.0513 | 1100 | 0.3471 | 0.9173 |
| 0.0036 | 7.6923 | 1200 | 0.3346 | 0.9137 |
| 0.003 | 8.3333 | 1300 | 0.3498 | 0.9137 |
| 0.003 | 8.9744 | 1400 | 0.3549 | 0.9101 |
| 0.0027 | 9.6154 | 1500 | 0.3569 | 0.9137 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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