File size: 4,865 Bytes
120fbfe |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_deit_tiny_rms_001_fold3
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: 0.7906976744186046
---
<!-- 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_40x_deit_tiny_rms_001_fold3
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: 2.2392
- Accuracy: 0.7907
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.246 | 1.0 | 217 | 1.4100 | 0.2558 |
| 0.9627 | 2.0 | 434 | 1.1709 | 0.5116 |
| 0.8333 | 3.0 | 651 | 1.1793 | 0.4186 |
| 0.7844 | 4.0 | 868 | 0.8077 | 0.6279 |
| 0.7035 | 5.0 | 1085 | 1.3327 | 0.5116 |
| 0.7976 | 6.0 | 1302 | 0.7941 | 0.6977 |
| 0.7352 | 7.0 | 1519 | 0.9909 | 0.6279 |
| 0.6247 | 8.0 | 1736 | 0.7281 | 0.6977 |
| 0.6212 | 9.0 | 1953 | 1.1902 | 0.6279 |
| 0.6647 | 10.0 | 2170 | 1.0897 | 0.5581 |
| 0.4763 | 11.0 | 2387 | 0.9383 | 0.6047 |
| 0.5076 | 12.0 | 2604 | 0.5861 | 0.7907 |
| 0.4573 | 13.0 | 2821 | 0.9438 | 0.5349 |
| 0.3857 | 14.0 | 3038 | 0.7991 | 0.6977 |
| 0.3919 | 15.0 | 3255 | 0.9377 | 0.6047 |
| 0.352 | 16.0 | 3472 | 1.0859 | 0.5814 |
| 0.3551 | 17.0 | 3689 | 1.2113 | 0.6744 |
| 0.3196 | 18.0 | 3906 | 1.2889 | 0.6279 |
| 0.2405 | 19.0 | 4123 | 0.9915 | 0.6512 |
| 0.2367 | 20.0 | 4340 | 1.6136 | 0.6279 |
| 0.2222 | 21.0 | 4557 | 1.4836 | 0.5814 |
| 0.1901 | 22.0 | 4774 | 1.0739 | 0.7209 |
| 0.173 | 23.0 | 4991 | 1.3956 | 0.6512 |
| 0.1711 | 24.0 | 5208 | 1.7072 | 0.6279 |
| 0.1027 | 25.0 | 5425 | 1.4657 | 0.6512 |
| 0.0952 | 26.0 | 5642 | 1.6372 | 0.6744 |
| 0.1462 | 27.0 | 5859 | 2.2566 | 0.5814 |
| 0.1003 | 28.0 | 6076 | 1.5093 | 0.6512 |
| 0.0764 | 29.0 | 6293 | 1.9318 | 0.6512 |
| 0.1025 | 30.0 | 6510 | 1.9630 | 0.6047 |
| 0.0702 | 31.0 | 6727 | 2.1273 | 0.6512 |
| 0.0313 | 32.0 | 6944 | 1.6171 | 0.7209 |
| 0.0732 | 33.0 | 7161 | 1.2147 | 0.7209 |
| 0.0384 | 34.0 | 7378 | 1.9804 | 0.7209 |
| 0.0177 | 35.0 | 7595 | 1.8221 | 0.6512 |
| 0.0098 | 36.0 | 7812 | 2.4941 | 0.6744 |
| 0.0407 | 37.0 | 8029 | 2.6063 | 0.6512 |
| 0.0798 | 38.0 | 8246 | 3.5391 | 0.5581 |
| 0.0022 | 39.0 | 8463 | 2.7971 | 0.6512 |
| 0.0004 | 40.0 | 8680 | 1.8602 | 0.7209 |
| 0.0547 | 41.0 | 8897 | 2.4427 | 0.6744 |
| 0.0003 | 42.0 | 9114 | 2.1061 | 0.6977 |
| 0.0003 | 43.0 | 9331 | 2.0283 | 0.5814 |
| 0.0017 | 44.0 | 9548 | 2.1926 | 0.6744 |
| 0.0001 | 45.0 | 9765 | 1.9704 | 0.7674 |
| 0.0 | 46.0 | 9982 | 2.2645 | 0.7442 |
| 0.0001 | 47.0 | 10199 | 2.3408 | 0.7674 |
| 0.0 | 48.0 | 10416 | 2.2312 | 0.7674 |
| 0.0 | 49.0 | 10633 | 2.2302 | 0.7907 |
| 0.0 | 50.0 | 10850 | 2.2392 | 0.7907 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|