metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: computer_partsclassifier-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: 0.8137931034482758
computer_partsclassifier-model
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5569
- Accuracy: 0.8138
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 | 1.0514 | 0.5172 |
1.0783 | 1.95 | 18 | 0.9347 | 0.6828 |
0.9676 | 2.92 | 27 | 0.7734 | 0.7517 |
0.7674 | 4.0 | 37 | 0.6470 | 0.7931 |
0.6162 | 4.97 | 46 | 0.5806 | 0.8 |
0.4838 | 5.95 | 55 | 0.5836 | 0.7931 |
0.4034 | 6.92 | 64 | 0.5778 | 0.8 |
0.325 | 8.0 | 74 | 0.5584 | 0.8069 |
0.2824 | 8.97 | 83 | 0.4549 | 0.8207 |
0.2252 | 9.95 | 92 | 0.5479 | 0.8 |
0.2017 | 10.92 | 101 | 0.5885 | 0.7724 |
0.183 | 12.0 | 111 | 0.5698 | 0.8 |
0.1709 | 12.97 | 120 | 0.5687 | 0.8 |
0.1709 | 13.95 | 129 | 0.6270 | 0.7793 |
0.1647 | 14.92 | 138 | 0.5652 | 0.8 |
0.1543 | 16.0 | 148 | 0.5965 | 0.8138 |
0.1676 | 16.97 | 157 | 0.5710 | 0.8 |
0.1562 | 17.95 | 166 | 0.6193 | 0.7724 |
0.1402 | 18.92 | 175 | 0.6086 | 0.7862 |
0.1313 | 19.46 | 180 | 0.5569 | 0.8138 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2