metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_sgd_00001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.41333333333333333
smids_5x_deit_tiny_sgd_00001_fold4
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0682
- Accuracy: 0.4133
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3789 | 1.0 | 375 | 1.3304 | 0.3467 |
| 1.3522 | 2.0 | 750 | 1.2980 | 0.3417 |
| 1.2851 | 3.0 | 1125 | 1.2700 | 0.3467 |
| 1.3268 | 4.0 | 1500 | 1.2462 | 0.35 |
| 1.2083 | 5.0 | 1875 | 1.2264 | 0.3533 |
| 1.2564 | 6.0 | 2250 | 1.2096 | 0.3667 |
| 1.2076 | 7.0 | 2625 | 1.1953 | 0.375 |
| 1.1738 | 8.0 | 3000 | 1.1833 | 0.375 |
| 1.1964 | 9.0 | 3375 | 1.1730 | 0.3767 |
| 1.1824 | 10.0 | 3750 | 1.1642 | 0.375 |
| 1.1746 | 11.0 | 4125 | 1.1567 | 0.375 |
| 1.0941 | 12.0 | 4500 | 1.1499 | 0.3783 |
| 1.1561 | 13.0 | 4875 | 1.1439 | 0.3817 |
| 1.1702 | 14.0 | 5250 | 1.1384 | 0.3817 |
| 1.1181 | 15.0 | 5625 | 1.1334 | 0.3867 |
| 1.149 | 16.0 | 6000 | 1.1288 | 0.3833 |
| 1.1131 | 17.0 | 6375 | 1.1244 | 0.3867 |
| 1.1335 | 18.0 | 6750 | 1.1203 | 0.39 |
| 1.105 | 19.0 | 7125 | 1.1164 | 0.3933 |
| 1.0655 | 20.0 | 7500 | 1.1128 | 0.3933 |
| 1.1098 | 21.0 | 7875 | 1.1094 | 0.395 |
| 1.0972 | 22.0 | 8250 | 1.1061 | 0.3933 |
| 1.112 | 23.0 | 8625 | 1.1030 | 0.3917 |
| 1.0932 | 24.0 | 9000 | 1.1001 | 0.395 |
| 1.0801 | 25.0 | 9375 | 1.0974 | 0.3933 |
| 1.1085 | 26.0 | 9750 | 1.0947 | 0.4 |
| 1.1153 | 27.0 | 10125 | 1.0922 | 0.4 |
| 1.0883 | 28.0 | 10500 | 1.0899 | 0.4 |
| 1.0621 | 29.0 | 10875 | 1.0877 | 0.4017 |
| 1.0559 | 30.0 | 11250 | 1.0856 | 0.4017 |
| 1.0795 | 31.0 | 11625 | 1.0837 | 0.4 |
| 1.1076 | 32.0 | 12000 | 1.0819 | 0.4017 |
| 1.1027 | 33.0 | 12375 | 1.0802 | 0.405 |
| 1.0471 | 34.0 | 12750 | 1.0787 | 0.41 |
| 1.032 | 35.0 | 13125 | 1.0772 | 0.4117 |
| 1.0529 | 36.0 | 13500 | 1.0759 | 0.4083 |
| 1.0365 | 37.0 | 13875 | 1.0747 | 0.4067 |
| 1.0659 | 38.0 | 14250 | 1.0736 | 0.4067 |
| 1.073 | 39.0 | 14625 | 1.0726 | 0.4117 |
| 1.1034 | 40.0 | 15000 | 1.0717 | 0.4117 |
| 1.0918 | 41.0 | 15375 | 1.0710 | 0.4117 |
| 1.0873 | 42.0 | 15750 | 1.0703 | 0.4133 |
| 1.0582 | 43.0 | 16125 | 1.0697 | 0.4133 |
| 1.0527 | 44.0 | 16500 | 1.0693 | 0.4133 |
| 1.0394 | 45.0 | 16875 | 1.0689 | 0.4133 |
| 1.0718 | 46.0 | 17250 | 1.0686 | 0.4133 |
| 1.0719 | 47.0 | 17625 | 1.0684 | 0.4133 |
| 1.0655 | 48.0 | 18000 | 1.0683 | 0.4133 |
| 1.0516 | 49.0 | 18375 | 1.0682 | 0.4133 |
| 1.0396 | 50.0 | 18750 | 1.0682 | 0.4133 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2