File size: 4,867 Bytes
63349be |
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: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_sgd_0001_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: 0.7886855241264559
---
<!-- 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_3x_beit_base_sgd_0001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5470
- Accuracy: 0.7887
## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1643 | 1.0 | 225 | 1.2557 | 0.3494 |
| 1.1336 | 2.0 | 450 | 1.1964 | 0.3727 |
| 1.0702 | 3.0 | 675 | 1.1415 | 0.3960 |
| 1.0744 | 4.0 | 900 | 1.0897 | 0.4226 |
| 0.9272 | 5.0 | 1125 | 1.0392 | 0.4526 |
| 0.9348 | 6.0 | 1350 | 0.9924 | 0.4908 |
| 0.9221 | 7.0 | 1575 | 0.9474 | 0.5374 |
| 0.8806 | 8.0 | 1800 | 0.9069 | 0.5890 |
| 0.8541 | 9.0 | 2025 | 0.8693 | 0.6206 |
| 0.8102 | 10.0 | 2250 | 0.8367 | 0.6439 |
| 0.7893 | 11.0 | 2475 | 0.8072 | 0.6672 |
| 0.7786 | 12.0 | 2700 | 0.7812 | 0.6872 |
| 0.7601 | 13.0 | 2925 | 0.7581 | 0.7038 |
| 0.7654 | 14.0 | 3150 | 0.7376 | 0.7105 |
| 0.7556 | 15.0 | 3375 | 0.7195 | 0.7171 |
| 0.7319 | 16.0 | 3600 | 0.7031 | 0.7321 |
| 0.6868 | 17.0 | 3825 | 0.6881 | 0.7354 |
| 0.7278 | 18.0 | 4050 | 0.6745 | 0.7421 |
| 0.6222 | 19.0 | 4275 | 0.6623 | 0.7454 |
| 0.6905 | 20.0 | 4500 | 0.6515 | 0.7471 |
| 0.6715 | 21.0 | 4725 | 0.6419 | 0.7554 |
| 0.7342 | 22.0 | 4950 | 0.6326 | 0.7554 |
| 0.6844 | 23.0 | 5175 | 0.6245 | 0.7621 |
| 0.6577 | 24.0 | 5400 | 0.6173 | 0.7654 |
| 0.6177 | 25.0 | 5625 | 0.6101 | 0.7687 |
| 0.647 | 26.0 | 5850 | 0.6037 | 0.7671 |
| 0.6355 | 27.0 | 6075 | 0.5976 | 0.7704 |
| 0.6059 | 28.0 | 6300 | 0.5926 | 0.7704 |
| 0.5954 | 29.0 | 6525 | 0.5873 | 0.7770 |
| 0.6256 | 30.0 | 6750 | 0.5829 | 0.7787 |
| 0.6261 | 31.0 | 6975 | 0.5789 | 0.7820 |
| 0.5804 | 32.0 | 7200 | 0.5748 | 0.7820 |
| 0.5936 | 33.0 | 7425 | 0.5711 | 0.7854 |
| 0.5647 | 34.0 | 7650 | 0.5682 | 0.7854 |
| 0.6238 | 35.0 | 7875 | 0.5657 | 0.7854 |
| 0.5976 | 36.0 | 8100 | 0.5630 | 0.7854 |
| 0.5852 | 37.0 | 8325 | 0.5605 | 0.7870 |
| 0.5826 | 38.0 | 8550 | 0.5584 | 0.7854 |
| 0.5619 | 39.0 | 8775 | 0.5564 | 0.7854 |
| 0.5946 | 40.0 | 9000 | 0.5547 | 0.7870 |
| 0.5381 | 41.0 | 9225 | 0.5529 | 0.7870 |
| 0.5966 | 42.0 | 9450 | 0.5514 | 0.7870 |
| 0.588 | 43.0 | 9675 | 0.5504 | 0.7870 |
| 0.5705 | 44.0 | 9900 | 0.5494 | 0.7854 |
| 0.6073 | 45.0 | 10125 | 0.5486 | 0.7870 |
| 0.5915 | 46.0 | 10350 | 0.5480 | 0.7887 |
| 0.5988 | 47.0 | 10575 | 0.5476 | 0.7887 |
| 0.542 | 48.0 | 10800 | 0.5472 | 0.7887 |
| 0.5885 | 49.0 | 11025 | 0.5471 | 0.7887 |
| 0.5585 | 50.0 | 11250 | 0.5470 | 0.7887 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|