File size: 4,864 Bytes
d27fdea |
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_5x_deit_tiny_rms_0001_fold3
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: 0.9016666666666666
---
<!-- 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_5x_deit_tiny_rms_0001_fold3
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: 1.0235
- Accuracy: 0.9017
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3072 | 1.0 | 375 | 0.3497 | 0.8733 |
| 0.1839 | 2.0 | 750 | 0.4255 | 0.87 |
| 0.1528 | 3.0 | 1125 | 0.4557 | 0.8567 |
| 0.1267 | 4.0 | 1500 | 0.3726 | 0.89 |
| 0.1353 | 5.0 | 1875 | 0.4467 | 0.8917 |
| 0.0943 | 6.0 | 2250 | 0.4927 | 0.91 |
| 0.1102 | 7.0 | 2625 | 0.6801 | 0.8833 |
| 0.1057 | 8.0 | 3000 | 0.6555 | 0.88 |
| 0.032 | 9.0 | 3375 | 0.7410 | 0.8783 |
| 0.0843 | 10.0 | 3750 | 0.8478 | 0.8667 |
| 0.0459 | 11.0 | 4125 | 0.6987 | 0.8917 |
| 0.0092 | 12.0 | 4500 | 0.7040 | 0.8917 |
| 0.0349 | 13.0 | 4875 | 0.7908 | 0.885 |
| 0.0111 | 14.0 | 5250 | 0.7260 | 0.8983 |
| 0.0286 | 15.0 | 5625 | 0.7556 | 0.89 |
| 0.0202 | 16.0 | 6000 | 0.7922 | 0.885 |
| 0.0017 | 17.0 | 6375 | 0.7780 | 0.89 |
| 0.0426 | 18.0 | 6750 | 0.7356 | 0.9033 |
| 0.0036 | 19.0 | 7125 | 0.7906 | 0.88 |
| 0.0088 | 20.0 | 7500 | 0.8591 | 0.8883 |
| 0.014 | 21.0 | 7875 | 0.9590 | 0.8867 |
| 0.0 | 22.0 | 8250 | 0.9929 | 0.8783 |
| 0.0363 | 23.0 | 8625 | 0.9559 | 0.89 |
| 0.0156 | 24.0 | 9000 | 0.9344 | 0.88 |
| 0.0345 | 25.0 | 9375 | 0.8898 | 0.8917 |
| 0.0005 | 26.0 | 9750 | 0.9066 | 0.9 |
| 0.0104 | 27.0 | 10125 | 0.9018 | 0.8983 |
| 0.0026 | 28.0 | 10500 | 0.8354 | 0.89 |
| 0.0098 | 29.0 | 10875 | 1.0679 | 0.885 |
| 0.0077 | 30.0 | 11250 | 0.8084 | 0.8933 |
| 0.007 | 31.0 | 11625 | 0.9761 | 0.8833 |
| 0.0079 | 32.0 | 12000 | 0.8798 | 0.8867 |
| 0.0211 | 33.0 | 12375 | 0.9152 | 0.8967 |
| 0.0205 | 34.0 | 12750 | 0.8595 | 0.8967 |
| 0.0 | 35.0 | 13125 | 0.9123 | 0.8983 |
| 0.0 | 36.0 | 13500 | 1.0918 | 0.8817 |
| 0.0001 | 37.0 | 13875 | 0.9598 | 0.8917 |
| 0.0 | 38.0 | 14250 | 0.9005 | 0.8933 |
| 0.0 | 39.0 | 14625 | 0.9817 | 0.895 |
| 0.003 | 40.0 | 15000 | 1.0214 | 0.8933 |
| 0.0 | 41.0 | 15375 | 1.0132 | 0.895 |
| 0.0012 | 42.0 | 15750 | 1.0443 | 0.8933 |
| 0.0 | 43.0 | 16125 | 1.0086 | 0.895 |
| 0.0 | 44.0 | 16500 | 1.0148 | 0.895 |
| 0.0 | 45.0 | 16875 | 1.0171 | 0.895 |
| 0.0 | 46.0 | 17250 | 1.0091 | 0.8967 |
| 0.0 | 47.0 | 17625 | 1.0118 | 0.8983 |
| 0.0 | 48.0 | 18000 | 1.0184 | 0.9017 |
| 0.0 | 49.0 | 18375 | 1.0213 | 0.9017 |
| 0.0 | 50.0 | 18750 | 1.0235 | 0.9017 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|