File size: 4,811 Bytes
47a9ff0 |
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_sgd_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: 0.657762938230384
---
<!-- 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_sgd_0001_fold1
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: 0.8020
- Accuracy: 0.6578
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.277 | 1.0 | 76 | 1.2939 | 0.2771 |
| 1.2116 | 2.0 | 152 | 1.2225 | 0.3172 |
| 1.1682 | 3.0 | 228 | 1.1693 | 0.3740 |
| 1.1189 | 4.0 | 304 | 1.1270 | 0.4073 |
| 1.0545 | 5.0 | 380 | 1.0950 | 0.4240 |
| 1.0649 | 6.0 | 456 | 1.0693 | 0.4574 |
| 1.0629 | 7.0 | 532 | 1.0475 | 0.4858 |
| 1.0345 | 8.0 | 608 | 1.0290 | 0.5142 |
| 1.012 | 9.0 | 684 | 1.0130 | 0.5392 |
| 0.9959 | 10.0 | 760 | 0.9988 | 0.5526 |
| 0.9617 | 11.0 | 836 | 0.9857 | 0.5626 |
| 1.0119 | 12.0 | 912 | 0.9733 | 0.5710 |
| 0.951 | 13.0 | 988 | 0.9618 | 0.5810 |
| 0.8944 | 14.0 | 1064 | 0.9511 | 0.5876 |
| 0.9729 | 15.0 | 1140 | 0.9409 | 0.5910 |
| 0.9626 | 16.0 | 1216 | 0.9311 | 0.5927 |
| 0.9103 | 17.0 | 1292 | 0.9219 | 0.6043 |
| 0.9088 | 18.0 | 1368 | 0.9131 | 0.6144 |
| 0.9045 | 19.0 | 1444 | 0.9046 | 0.6160 |
| 0.9231 | 20.0 | 1520 | 0.8968 | 0.6160 |
| 0.9054 | 21.0 | 1596 | 0.8893 | 0.6177 |
| 0.854 | 22.0 | 1672 | 0.8821 | 0.6227 |
| 0.8305 | 23.0 | 1748 | 0.8753 | 0.6294 |
| 0.8621 | 24.0 | 1824 | 0.8689 | 0.6311 |
| 0.8299 | 25.0 | 1900 | 0.8629 | 0.6327 |
| 0.8471 | 26.0 | 1976 | 0.8573 | 0.6344 |
| 0.817 | 27.0 | 2052 | 0.8521 | 0.6344 |
| 0.792 | 28.0 | 2128 | 0.8472 | 0.6361 |
| 0.8136 | 29.0 | 2204 | 0.8426 | 0.6377 |
| 0.7461 | 30.0 | 2280 | 0.8383 | 0.6411 |
| 0.8135 | 31.0 | 2356 | 0.8343 | 0.6427 |
| 0.7863 | 32.0 | 2432 | 0.8305 | 0.6461 |
| 0.7659 | 33.0 | 2508 | 0.8271 | 0.6494 |
| 0.8238 | 34.0 | 2584 | 0.8240 | 0.6528 |
| 0.8196 | 35.0 | 2660 | 0.8211 | 0.6528 |
| 0.7577 | 36.0 | 2736 | 0.8184 | 0.6528 |
| 0.8136 | 37.0 | 2812 | 0.8159 | 0.6528 |
| 0.7544 | 38.0 | 2888 | 0.8137 | 0.6561 |
| 0.7769 | 39.0 | 2964 | 0.8116 | 0.6561 |
| 0.8539 | 40.0 | 3040 | 0.8098 | 0.6561 |
| 0.7796 | 41.0 | 3116 | 0.8081 | 0.6544 |
| 0.765 | 42.0 | 3192 | 0.8067 | 0.6578 |
| 0.7732 | 43.0 | 3268 | 0.8054 | 0.6578 |
| 0.7585 | 44.0 | 3344 | 0.8044 | 0.6561 |
| 0.7325 | 45.0 | 3420 | 0.8036 | 0.6561 |
| 0.7699 | 46.0 | 3496 | 0.8029 | 0.6578 |
| 0.7818 | 47.0 | 3572 | 0.8024 | 0.6578 |
| 0.7946 | 48.0 | 3648 | 0.8021 | 0.6578 |
| 0.7345 | 49.0 | 3724 | 0.8020 | 0.6578 |
| 0.833 | 50.0 | 3800 | 0.8020 | 0.6578 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|