File size: 2,943 Bytes
92b19a3 3b3f7c0 92b19a3 3b3f7c0 6ff0951 92b19a3 3b3f7c0 5dd5081 3b3f7c0 5dd5081 3b3f7c0 6ff0951 95ccd40 3b3f7c0 5d8063e 3b3f7c0 5d8063e 3b3f7c0 6ff0951 3b3f7c0 6ff0951 3b3f7c0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
---
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: 0.9040816326530612
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# font-identifier
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3626
- Accuracy: 0.9041
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.929 | 0.98 | 30 | 3.8215 | 0.0429 |
| 3.2162 | 1.98 | 61 | 2.9144 | 0.2816 |
| 2.4387 | 2.99 | 92 | 2.1019 | 0.4776 |
| 1.9404 | 4.0 | 123 | 1.5607 | 0.6041 |
| 1.5756 | 4.98 | 153 | 1.3012 | 0.6449 |
| 1.3374 | 5.98 | 184 | 1.0699 | 0.7102 |
| 1.1912 | 6.99 | 215 | 0.9145 | 0.7633 |
| 1.0716 | 8.0 | 246 | 0.7864 | 0.7898 |
| 0.9751 | 8.98 | 276 | 0.6894 | 0.8204 |
| 0.8211 | 9.98 | 307 | 0.6256 | 0.8510 |
| 0.8254 | 10.99 | 338 | 0.5563 | 0.8633 |
| 0.742 | 12.0 | 369 | 0.5149 | 0.8694 |
| 0.6949 | 12.98 | 399 | 0.4625 | 0.8878 |
| 0.6401 | 13.98 | 430 | 0.4799 | 0.8857 |
| 0.6304 | 14.99 | 461 | 0.3970 | 0.8980 |
| 0.6239 | 16.0 | 492 | 0.4016 | 0.9 |
| 0.5911 | 16.98 | 522 | 0.4271 | 0.8755 |
| 0.5764 | 17.98 | 553 | 0.3922 | 0.9 |
| 0.5461 | 18.99 | 584 | 0.3750 | 0.9 |
| 0.6236 | 19.51 | 600 | 0.3626 | 0.9041 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.14.1
|