File size: 2,937 Bytes
f36cb90 |
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 |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_conflu_deneme_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: 0.5333333333333333
---
<!-- 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. -->
# hushem_conflu_deneme_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9900
- Accuracy: 0.5333
## 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: 0.001
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.5124 | 0.2444 |
| 2.1014 | 2.0 | 12 | 1.4172 | 0.2667 |
| 2.1014 | 3.0 | 18 | 1.3682 | 0.2667 |
| 1.3494 | 4.0 | 24 | 1.5568 | 0.3333 |
| 1.1794 | 5.0 | 30 | 1.1703 | 0.3778 |
| 1.1794 | 6.0 | 36 | 1.1853 | 0.5333 |
| 0.9962 | 7.0 | 42 | 0.9960 | 0.5778 |
| 0.9962 | 8.0 | 48 | 0.9911 | 0.5778 |
| 0.7941 | 9.0 | 54 | 1.7710 | 0.4444 |
| 0.6504 | 10.0 | 60 | 1.0188 | 0.5111 |
| 0.6504 | 11.0 | 66 | 1.3899 | 0.4889 |
| 0.3424 | 12.0 | 72 | 1.3633 | 0.5333 |
| 0.3424 | 13.0 | 78 | 1.6911 | 0.4667 |
| 0.1576 | 14.0 | 84 | 1.8405 | 0.5556 |
| 0.0563 | 15.0 | 90 | 1.8925 | 0.5333 |
| 0.0563 | 16.0 | 96 | 2.0167 | 0.5333 |
| 0.0162 | 17.0 | 102 | 1.9900 | 0.5333 |
| 0.0162 | 18.0 | 108 | 1.9900 | 0.5333 |
| 0.009 | 19.0 | 114 | 1.9900 | 0.5333 |
| 0.0088 | 20.0 | 120 | 1.9900 | 0.5333 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|