File size: 4,817 Bytes
ae395c5 |
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-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_small_rms_00001_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: 0.870216306156406
---
<!-- 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. -->
# smids_1x_deit_small_rms_00001_fold2
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8494
- Accuracy: 0.8702
## 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: 1e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.391 | 1.0 | 75 | 0.3306 | 0.8569 |
| 0.2024 | 2.0 | 150 | 0.3078 | 0.8719 |
| 0.1659 | 3.0 | 225 | 0.3046 | 0.8636 |
| 0.1089 | 4.0 | 300 | 0.3233 | 0.8702 |
| 0.0832 | 5.0 | 375 | 0.4345 | 0.8552 |
| 0.0315 | 6.0 | 450 | 0.4227 | 0.8686 |
| 0.0247 | 7.0 | 525 | 0.5432 | 0.8652 |
| 0.0031 | 8.0 | 600 | 0.5857 | 0.8769 |
| 0.0058 | 9.0 | 675 | 0.5689 | 0.8619 |
| 0.0354 | 10.0 | 750 | 0.6368 | 0.8619 |
| 0.0193 | 11.0 | 825 | 0.5921 | 0.8752 |
| 0.0019 | 12.0 | 900 | 0.6514 | 0.8785 |
| 0.0447 | 13.0 | 975 | 0.6838 | 0.8686 |
| 0.0527 | 14.0 | 1050 | 0.6693 | 0.8735 |
| 0.0047 | 15.0 | 1125 | 0.6444 | 0.8735 |
| 0.0064 | 16.0 | 1200 | 0.7052 | 0.8719 |
| 0.0002 | 17.0 | 1275 | 0.7289 | 0.8636 |
| 0.0092 | 18.0 | 1350 | 0.7405 | 0.8669 |
| 0.0001 | 19.0 | 1425 | 0.7743 | 0.8619 |
| 0.0038 | 20.0 | 1500 | 0.7512 | 0.8686 |
| 0.0001 | 21.0 | 1575 | 0.8249 | 0.8602 |
| 0.0001 | 22.0 | 1650 | 0.7832 | 0.8686 |
| 0.0001 | 23.0 | 1725 | 0.8312 | 0.8636 |
| 0.0 | 24.0 | 1800 | 0.7877 | 0.8669 |
| 0.0 | 25.0 | 1875 | 0.7958 | 0.8719 |
| 0.0001 | 26.0 | 1950 | 0.7718 | 0.8752 |
| 0.0055 | 27.0 | 2025 | 0.7918 | 0.8686 |
| 0.0032 | 28.0 | 2100 | 0.8022 | 0.8735 |
| 0.0023 | 29.0 | 2175 | 0.8185 | 0.8735 |
| 0.0031 | 30.0 | 2250 | 0.8365 | 0.8735 |
| 0.0028 | 31.0 | 2325 | 0.7946 | 0.8686 |
| 0.0 | 32.0 | 2400 | 0.8222 | 0.8752 |
| 0.0 | 33.0 | 2475 | 0.7981 | 0.8719 |
| 0.0 | 34.0 | 2550 | 0.8313 | 0.8752 |
| 0.0084 | 35.0 | 2625 | 0.8895 | 0.8702 |
| 0.0 | 36.0 | 2700 | 0.8170 | 0.8686 |
| 0.0 | 37.0 | 2775 | 0.8344 | 0.8752 |
| 0.0 | 38.0 | 2850 | 0.8561 | 0.8735 |
| 0.0022 | 39.0 | 2925 | 0.8329 | 0.8702 |
| 0.0 | 40.0 | 3000 | 0.8473 | 0.8719 |
| 0.0026 | 41.0 | 3075 | 0.8354 | 0.8686 |
| 0.0 | 42.0 | 3150 | 0.8451 | 0.8735 |
| 0.0025 | 43.0 | 3225 | 0.8430 | 0.8735 |
| 0.0025 | 44.0 | 3300 | 0.8484 | 0.8719 |
| 0.0 | 45.0 | 3375 | 0.8461 | 0.8702 |
| 0.0 | 46.0 | 3450 | 0.8473 | 0.8735 |
| 0.0023 | 47.0 | 3525 | 0.8487 | 0.8719 |
| 0.0 | 48.0 | 3600 | 0.8492 | 0.8702 |
| 0.0022 | 49.0 | 3675 | 0.8491 | 0.8686 |
| 0.0022 | 50.0 | 3750 | 0.8494 | 0.8702 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|