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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: computer_parts_classifier-model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train[:722]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8068965517241379
---
<!-- 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. -->
# computer_parts_classifier-model
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: 0.5140
- Accuracy: 0.8069
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.97 | 9 | 1.0689 | 0.5379 |
| 1.1042 | 1.95 | 18 | 0.9123 | 0.6897 |
| 0.9605 | 2.92 | 27 | 0.7676 | 0.7379 |
| 0.7855 | 4.0 | 37 | 0.6722 | 0.7586 |
| 0.626 | 4.97 | 46 | 0.5915 | 0.8069 |
| 0.5102 | 5.95 | 55 | 0.5672 | 0.8138 |
| 0.4266 | 6.92 | 64 | 0.5106 | 0.8483 |
| 0.3561 | 8.0 | 74 | 0.5587 | 0.8138 |
| 0.3126 | 8.97 | 83 | 0.5492 | 0.8069 |
| 0.294 | 9.95 | 92 | 0.5589 | 0.7862 |
| 0.2287 | 10.92 | 101 | 0.5579 | 0.8069 |
| 0.2282 | 12.0 | 111 | 0.5193 | 0.8138 |
| 0.2261 | 12.97 | 120 | 0.4383 | 0.8552 |
| 0.2261 | 13.95 | 129 | 0.5205 | 0.7931 |
| 0.1996 | 14.92 | 138 | 0.5037 | 0.8138 |
| 0.1796 | 16.0 | 148 | 0.4986 | 0.8138 |
| 0.1583 | 16.97 | 157 | 0.5583 | 0.7931 |
| 0.1692 | 17.95 | 166 | 0.4743 | 0.8276 |
| 0.1577 | 18.92 | 175 | 0.4867 | 0.8345 |
| 0.1706 | 19.46 | 180 | 0.5140 | 0.8069 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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