File size: 4,864 Bytes
1f7c76a |
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-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_base_rms_0001_fold1
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: 0.9048414023372288
---
<!-- 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_deit_base_rms_0001_fold1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8578
- Accuracy: 0.9048
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3767 | 1.0 | 226 | 0.4015 | 0.8047 |
| 0.1856 | 2.0 | 452 | 0.2907 | 0.8915 |
| 0.122 | 3.0 | 678 | 0.3819 | 0.8397 |
| 0.0716 | 4.0 | 904 | 0.6439 | 0.8598 |
| 0.0597 | 5.0 | 1130 | 0.4947 | 0.8831 |
| 0.0636 | 6.0 | 1356 | 0.4627 | 0.8965 |
| 0.0123 | 7.0 | 1582 | 0.5193 | 0.8798 |
| 0.0384 | 8.0 | 1808 | 0.5328 | 0.8965 |
| 0.0347 | 9.0 | 2034 | 0.5230 | 0.8865 |
| 0.0555 | 10.0 | 2260 | 0.4625 | 0.8915 |
| 0.0105 | 11.0 | 2486 | 0.4967 | 0.9032 |
| 0.0151 | 12.0 | 2712 | 0.5936 | 0.8798 |
| 0.0374 | 13.0 | 2938 | 0.5700 | 0.8881 |
| 0.0272 | 14.0 | 3164 | 0.5683 | 0.8915 |
| 0.0029 | 15.0 | 3390 | 0.8104 | 0.8815 |
| 0.0276 | 16.0 | 3616 | 0.6803 | 0.8932 |
| 0.006 | 17.0 | 3842 | 0.6793 | 0.8781 |
| 0.0461 | 18.0 | 4068 | 0.6650 | 0.8815 |
| 0.006 | 19.0 | 4294 | 0.8601 | 0.8831 |
| 0.0039 | 20.0 | 4520 | 0.5720 | 0.8948 |
| 0.0002 | 21.0 | 4746 | 0.6983 | 0.8948 |
| 0.0089 | 22.0 | 4972 | 0.6968 | 0.8865 |
| 0.0025 | 23.0 | 5198 | 0.7765 | 0.9032 |
| 0.0037 | 24.0 | 5424 | 0.7330 | 0.8965 |
| 0.0105 | 25.0 | 5650 | 0.5590 | 0.8932 |
| 0.0002 | 26.0 | 5876 | 0.6884 | 0.9048 |
| 0.0001 | 27.0 | 6102 | 0.6695 | 0.9015 |
| 0.0048 | 28.0 | 6328 | 0.7561 | 0.8848 |
| 0.0001 | 29.0 | 6554 | 0.8455 | 0.8831 |
| 0.0168 | 30.0 | 6780 | 0.6624 | 0.8932 |
| 0.013 | 31.0 | 7006 | 0.7840 | 0.8932 |
| 0.0 | 32.0 | 7232 | 0.6961 | 0.8982 |
| 0.0033 | 33.0 | 7458 | 0.8341 | 0.8915 |
| 0.0 | 34.0 | 7684 | 0.7715 | 0.9048 |
| 0.0 | 35.0 | 7910 | 0.8192 | 0.9015 |
| 0.0 | 36.0 | 8136 | 0.7732 | 0.9048 |
| 0.0 | 37.0 | 8362 | 0.7832 | 0.9098 |
| 0.0 | 38.0 | 8588 | 0.7728 | 0.9065 |
| 0.0 | 39.0 | 8814 | 0.8176 | 0.9065 |
| 0.0 | 40.0 | 9040 | 0.8093 | 0.9048 |
| 0.0026 | 41.0 | 9266 | 0.7762 | 0.9132 |
| 0.0021 | 42.0 | 9492 | 0.7747 | 0.9065 |
| 0.0 | 43.0 | 9718 | 0.7876 | 0.9048 |
| 0.0 | 44.0 | 9944 | 0.7913 | 0.9015 |
| 0.0 | 45.0 | 10170 | 0.8068 | 0.9015 |
| 0.0 | 46.0 | 10396 | 0.8218 | 0.9015 |
| 0.0 | 47.0 | 10622 | 0.8475 | 0.9048 |
| 0.0 | 48.0 | 10848 | 0.8522 | 0.9048 |
| 0.0 | 49.0 | 11074 | 0.8566 | 0.9048 |
| 0.0 | 50.0 | 11300 | 0.8578 | 0.9048 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|