metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_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: 0.8716666666666667
smids_5x_deit_tiny_adamax_00001_fold4
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.3354
- Accuracy: 0.8717
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 |
|---|---|---|---|---|
| 0.2847 | 1.0 | 375 | 0.3588 | 0.86 |
| 0.2399 | 2.0 | 750 | 0.3497 | 0.8783 |
| 0.1069 | 3.0 | 1125 | 0.3839 | 0.8683 |
| 0.1588 | 4.0 | 1500 | 0.3944 | 0.8767 |
| 0.0651 | 5.0 | 1875 | 0.4658 | 0.8717 |
| 0.0408 | 6.0 | 2250 | 0.5268 | 0.8767 |
| 0.026 | 7.0 | 2625 | 0.6145 | 0.865 |
| 0.0213 | 8.0 | 3000 | 0.6639 | 0.8717 |
| 0.0032 | 9.0 | 3375 | 0.8259 | 0.8583 |
| 0.0148 | 10.0 | 3750 | 0.8114 | 0.8717 |
| 0.015 | 11.0 | 4125 | 0.8747 | 0.87 |
| 0.0014 | 12.0 | 4500 | 0.9000 | 0.8783 |
| 0.0001 | 13.0 | 4875 | 0.9761 | 0.87 |
| 0.015 | 14.0 | 5250 | 0.9964 | 0.87 |
| 0.0001 | 15.0 | 5625 | 1.0366 | 0.87 |
| 0.0 | 16.0 | 6000 | 1.0464 | 0.8733 |
| 0.0 | 17.0 | 6375 | 1.0647 | 0.8733 |
| 0.0197 | 18.0 | 6750 | 1.1060 | 0.87 |
| 0.0 | 19.0 | 7125 | 1.1223 | 0.87 |
| 0.0 | 20.0 | 7500 | 1.1307 | 0.8717 |
| 0.0 | 21.0 | 7875 | 1.1563 | 0.8667 |
| 0.0 | 22.0 | 8250 | 1.1659 | 0.8717 |
| 0.0 | 23.0 | 8625 | 1.1873 | 0.8717 |
| 0.0 | 24.0 | 9000 | 1.1966 | 0.875 |
| 0.0 | 25.0 | 9375 | 1.2159 | 0.8667 |
| 0.0 | 26.0 | 9750 | 1.2192 | 0.8667 |
| 0.0 | 27.0 | 10125 | 1.2446 | 0.865 |
| 0.0 | 28.0 | 10500 | 1.2249 | 0.8683 |
| 0.0 | 29.0 | 10875 | 1.2382 | 0.8717 |
| 0.0 | 30.0 | 11250 | 1.2391 | 0.87 |
| 0.0 | 31.0 | 11625 | 1.2547 | 0.8733 |
| 0.0 | 32.0 | 12000 | 1.2771 | 0.87 |
| 0.0 | 33.0 | 12375 | 1.2797 | 0.8717 |
| 0.0 | 34.0 | 12750 | 1.2938 | 0.87 |
| 0.0 | 35.0 | 13125 | 1.2859 | 0.87 |
| 0.0 | 36.0 | 13500 | 1.2998 | 0.8717 |
| 0.0 | 37.0 | 13875 | 1.2941 | 0.8683 |
| 0.0015 | 38.0 | 14250 | 1.3001 | 0.8683 |
| 0.0 | 39.0 | 14625 | 1.3082 | 0.8717 |
| 0.0 | 40.0 | 15000 | 1.3151 | 0.8717 |
| 0.0 | 41.0 | 15375 | 1.3188 | 0.8717 |
| 0.0 | 42.0 | 15750 | 1.3223 | 0.8717 |
| 0.0 | 43.0 | 16125 | 1.3285 | 0.87 |
| 0.0 | 44.0 | 16500 | 1.3298 | 0.87 |
| 0.0 | 45.0 | 16875 | 1.3317 | 0.87 |
| 0.0 | 46.0 | 17250 | 1.3330 | 0.87 |
| 0.0 | 47.0 | 17625 | 1.3343 | 0.87 |
| 0.0 | 48.0 | 18000 | 1.3352 | 0.87 |
| 0.0 | 49.0 | 18375 | 1.3354 | 0.8717 |
| 0.0 | 50.0 | 18750 | 1.3354 | 0.8717 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2