metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_tiny_adamax_0001_fold2
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: 0.8885191347753744
smids_10x_deit_tiny_adamax_0001_fold2
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: 1.0252
- Accuracy: 0.8885
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: 0.0001
- 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 |
|---|---|---|---|---|
| 0.2345 | 1.0 | 750 | 0.2862 | 0.8852 |
| 0.1226 | 2.0 | 1500 | 0.3492 | 0.8819 |
| 0.0786 | 3.0 | 2250 | 0.4125 | 0.8785 |
| 0.0821 | 4.0 | 3000 | 0.6690 | 0.8569 |
| 0.0058 | 5.0 | 3750 | 0.6467 | 0.8869 |
| 0.0246 | 6.0 | 4500 | 0.7107 | 0.8819 |
| 0.0249 | 7.0 | 5250 | 0.7670 | 0.8852 |
| 0.0023 | 8.0 | 6000 | 0.8535 | 0.8952 |
| 0.0094 | 9.0 | 6750 | 0.9434 | 0.8819 |
| 0.0202 | 10.0 | 7500 | 1.0037 | 0.8835 |
| 0.0073 | 11.0 | 8250 | 1.0395 | 0.8802 |
| 0.0 | 12.0 | 9000 | 0.9154 | 0.8935 |
| 0.0001 | 13.0 | 9750 | 1.0585 | 0.8785 |
| 0.0048 | 14.0 | 10500 | 0.9392 | 0.8952 |
| 0.0002 | 15.0 | 11250 | 0.9865 | 0.8885 |
| 0.0 | 16.0 | 12000 | 1.1010 | 0.8885 |
| 0.0 | 17.0 | 12750 | 1.0500 | 0.8968 |
| 0.0 | 18.0 | 13500 | 1.0366 | 0.8968 |
| 0.0 | 19.0 | 14250 | 0.9974 | 0.8735 |
| 0.0 | 20.0 | 15000 | 1.0266 | 0.8935 |
| 0.0 | 21.0 | 15750 | 0.9740 | 0.9018 |
| 0.0135 | 22.0 | 16500 | 1.0163 | 0.8935 |
| 0.0062 | 23.0 | 17250 | 1.0796 | 0.8835 |
| 0.0 | 24.0 | 18000 | 1.0547 | 0.8852 |
| 0.0 | 25.0 | 18750 | 1.0544 | 0.8918 |
| 0.0 | 26.0 | 19500 | 0.9809 | 0.8952 |
| 0.0089 | 27.0 | 20250 | 1.0367 | 0.8918 |
| 0.0 | 28.0 | 21000 | 1.0326 | 0.8835 |
| 0.0 | 29.0 | 21750 | 1.0069 | 0.8935 |
| 0.0 | 30.0 | 22500 | 1.0290 | 0.8968 |
| 0.0 | 31.0 | 23250 | 1.0034 | 0.8935 |
| 0.0 | 32.0 | 24000 | 0.9398 | 0.8985 |
| 0.0 | 33.0 | 24750 | 1.0178 | 0.8935 |
| 0.0 | 34.0 | 25500 | 1.0300 | 0.8902 |
| 0.0 | 35.0 | 26250 | 1.0140 | 0.8918 |
| 0.0 | 36.0 | 27000 | 1.0115 | 0.8902 |
| 0.0074 | 37.0 | 27750 | 1.0130 | 0.8918 |
| 0.0 | 38.0 | 28500 | 1.0096 | 0.8902 |
| 0.0 | 39.0 | 29250 | 1.0259 | 0.8935 |
| 0.0 | 40.0 | 30000 | 1.0330 | 0.8918 |
| 0.0 | 41.0 | 30750 | 1.0283 | 0.8902 |
| 0.0 | 42.0 | 31500 | 1.0254 | 0.8902 |
| 0.0 | 43.0 | 32250 | 1.0245 | 0.8869 |
| 0.0 | 44.0 | 33000 | 1.0228 | 0.8885 |
| 0.0024 | 45.0 | 33750 | 1.0249 | 0.8885 |
| 0.0 | 46.0 | 34500 | 1.0240 | 0.8885 |
| 0.0 | 47.0 | 35250 | 1.0247 | 0.8885 |
| 0.0 | 48.0 | 36000 | 1.0252 | 0.8885 |
| 0.0 | 49.0 | 36750 | 1.0256 | 0.8885 |
| 0.0 | 50.0 | 37500 | 1.0252 | 0.8885 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2