File size: 4,809 Bytes
984e8b9 |
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: smids_1x_deit_tiny_rms_001_fold4
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: 0.7333333333333333
---
<!-- 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_tiny_rms_001_fold4
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: 0.9960
- Accuracy: 0.7333
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1598 | 1.0 | 75 | 4.0739 | 0.3033 |
| 1.1025 | 2.0 | 150 | 1.1472 | 0.335 |
| 1.0056 | 3.0 | 225 | 0.8843 | 0.5617 |
| 0.9034 | 4.0 | 300 | 0.8985 | 0.5117 |
| 0.8764 | 5.0 | 375 | 0.9613 | 0.5017 |
| 0.9617 | 6.0 | 450 | 0.9074 | 0.5317 |
| 0.8578 | 7.0 | 525 | 0.8240 | 0.5717 |
| 0.8424 | 8.0 | 600 | 0.8437 | 0.5617 |
| 0.8025 | 9.0 | 675 | 0.7942 | 0.5833 |
| 0.7777 | 10.0 | 750 | 0.7683 | 0.57 |
| 0.8053 | 11.0 | 825 | 0.7474 | 0.5983 |
| 0.818 | 12.0 | 900 | 0.7555 | 0.61 |
| 0.8018 | 13.0 | 975 | 0.7629 | 0.5833 |
| 0.8411 | 14.0 | 1050 | 0.7216 | 0.635 |
| 0.6416 | 15.0 | 1125 | 0.8742 | 0.56 |
| 0.8084 | 16.0 | 1200 | 0.7814 | 0.6083 |
| 0.7505 | 17.0 | 1275 | 0.7600 | 0.6183 |
| 0.6996 | 18.0 | 1350 | 0.7346 | 0.6283 |
| 0.7648 | 19.0 | 1425 | 0.7240 | 0.6617 |
| 0.6916 | 20.0 | 1500 | 0.6768 | 0.6767 |
| 0.7556 | 21.0 | 1575 | 0.7263 | 0.6617 |
| 0.6471 | 22.0 | 1650 | 0.7297 | 0.6583 |
| 0.752 | 23.0 | 1725 | 0.7501 | 0.635 |
| 0.7349 | 24.0 | 1800 | 0.6751 | 0.6883 |
| 0.6802 | 25.0 | 1875 | 0.6689 | 0.6817 |
| 0.6239 | 26.0 | 1950 | 0.8871 | 0.5817 |
| 0.6865 | 27.0 | 2025 | 0.6485 | 0.7033 |
| 0.6138 | 28.0 | 2100 | 0.6457 | 0.7233 |
| 0.6707 | 29.0 | 2175 | 0.6937 | 0.6833 |
| 0.6824 | 30.0 | 2250 | 0.6688 | 0.7033 |
| 0.5913 | 31.0 | 2325 | 0.6725 | 0.715 |
| 0.5797 | 32.0 | 2400 | 0.6508 | 0.7167 |
| 0.5524 | 33.0 | 2475 | 0.7048 | 0.7 |
| 0.4736 | 34.0 | 2550 | 0.6807 | 0.6933 |
| 0.5263 | 35.0 | 2625 | 0.6317 | 0.7233 |
| 0.5348 | 36.0 | 2700 | 0.6398 | 0.7367 |
| 0.5082 | 37.0 | 2775 | 0.6440 | 0.7183 |
| 0.4972 | 38.0 | 2850 | 0.6697 | 0.7167 |
| 0.4567 | 39.0 | 2925 | 0.6947 | 0.73 |
| 0.4313 | 40.0 | 3000 | 0.6527 | 0.7383 |
| 0.4762 | 41.0 | 3075 | 0.6875 | 0.74 |
| 0.4293 | 42.0 | 3150 | 0.7259 | 0.7333 |
| 0.4594 | 43.0 | 3225 | 0.7531 | 0.7367 |
| 0.379 | 44.0 | 3300 | 0.7792 | 0.7383 |
| 0.3265 | 45.0 | 3375 | 0.7882 | 0.74 |
| 0.2807 | 46.0 | 3450 | 0.8615 | 0.7367 |
| 0.2733 | 47.0 | 3525 | 0.9438 | 0.73 |
| 0.2273 | 48.0 | 3600 | 0.9312 | 0.73 |
| 0.2189 | 49.0 | 3675 | 0.9889 | 0.7383 |
| 0.1609 | 50.0 | 3750 | 0.9960 | 0.7333 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|