metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-18
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: font-identifier
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: 0.9454545454545454
font-identifier
This model is a fine-tuned version of microsoft/resnet-18 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1706
- Accuracy: 0.9455
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.2323 | 1.0 | 14 | 3.1711 | 0.05 |
| 3.1019 | 2.0 | 28 | 2.9173 | 0.1227 |
| 2.779 | 3.0 | 42 | 2.5695 | 0.2409 |
| 2.5 | 4.0 | 56 | 2.1142 | 0.4091 |
| 1.8064 | 5.0 | 70 | 1.6804 | 0.4864 |
| 1.575 | 6.0 | 84 | 1.2757 | 0.5773 |
| 1.348 | 7.0 | 98 | 1.0973 | 0.6864 |
| 1.0483 | 8.0 | 112 | 0.8965 | 0.7273 |
| 0.9753 | 9.0 | 126 | 0.7025 | 0.7682 |
| 0.7763 | 10.0 | 140 | 0.6220 | 0.8091 |
| 0.7392 | 11.0 | 154 | 0.5169 | 0.8636 |
| 0.7077 | 12.0 | 168 | 0.4815 | 0.8682 |
| 0.5433 | 13.0 | 182 | 0.4650 | 0.8455 |
| 0.565 | 14.0 | 196 | 0.3828 | 0.8773 |
| 0.4204 | 15.0 | 210 | 0.3493 | 0.8864 |
| 0.4798 | 16.0 | 224 | 0.2847 | 0.9045 |
| 0.4353 | 17.0 | 238 | 0.3370 | 0.8773 |
| 0.3871 | 18.0 | 252 | 0.2797 | 0.9045 |
| 0.3779 | 19.0 | 266 | 0.2671 | 0.9045 |
| 0.3819 | 20.0 | 280 | 0.2575 | 0.9 |
| 0.3216 | 21.0 | 294 | 0.2516 | 0.9227 |
| 0.3461 | 22.0 | 308 | 0.2368 | 0.9045 |
| 0.3116 | 23.0 | 322 | 0.2651 | 0.9136 |
| 0.3244 | 24.0 | 336 | 0.2820 | 0.9 |
| 0.2725 | 25.0 | 350 | 0.2320 | 0.9045 |
| 0.3377 | 26.0 | 364 | 0.2309 | 0.9318 |
| 0.2556 | 27.0 | 378 | 0.2361 | 0.9136 |
| 0.2654 | 28.0 | 392 | 0.1988 | 0.9364 |
| 0.2578 | 29.0 | 406 | 0.2322 | 0.9227 |
| 0.2262 | 30.0 | 420 | 0.1686 | 0.9409 |
| 0.2298 | 31.0 | 434 | 0.2148 | 0.9091 |
| 0.2259 | 32.0 | 448 | 0.1982 | 0.9318 |
| 0.2155 | 33.0 | 462 | 0.2340 | 0.9227 |
| 0.213 | 34.0 | 476 | 0.1359 | 0.9545 |
| 0.1812 | 35.0 | 490 | 0.1522 | 0.9409 |
| 0.1793 | 36.0 | 504 | 0.1553 | 0.9409 |
| 0.2391 | 37.0 | 518 | 0.1149 | 0.9636 |
| 0.1755 | 38.0 | 532 | 0.1627 | 0.9273 |
| 0.1907 | 39.0 | 546 | 0.1555 | 0.95 |
| 0.1814 | 40.0 | 560 | 0.1832 | 0.9409 |
| 0.1879 | 41.0 | 574 | 0.2046 | 0.9318 |
| 0.1953 | 42.0 | 588 | 0.1722 | 0.9364 |
| 0.1814 | 43.0 | 602 | 0.2270 | 0.9455 |
| 0.1932 | 44.0 | 616 | 0.1651 | 0.9318 |
| 0.1813 | 45.0 | 630 | 0.1752 | 0.9318 |
| 0.1691 | 46.0 | 644 | 0.1681 | 0.9636 |
| 0.1396 | 47.0 | 658 | 0.1604 | 0.9545 |
| 0.1647 | 48.0 | 672 | 0.1575 | 0.95 |
| 0.1501 | 49.0 | 686 | 0.1360 | 0.9545 |
| 0.1534 | 50.0 | 700 | 0.1706 | 0.9455 |
Framework versions
- Transformers 4.54.1
- Pytorch 2.7.1
- Datasets 4.0.0
- Tokenizers 0.21.4