File size: 5,319 Bytes
663066c beeb1fe 663066c beeb1fe 663066c beeb1fe 663066c beeb1fe 663066c beeb1fe 663066c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: google-vit-base-patch16-384-batch_16_epoch_4_classes_24
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: bengali_food_images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9899425287356322
---
<!-- 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. -->
# google-vit-base-patch16-384-batch_16_epoch_4_classes_24
This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the bengali_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0635
- Accuracy: 0.9899
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2947 | 0.07 | 100 | 0.2491 | 0.9353 |
| 0.1703 | 0.14 | 200 | 0.2377 | 0.9339 |
| 0.0797 | 0.21 | 300 | 0.1413 | 0.9641 |
| 0.1035 | 0.28 | 400 | 0.1057 | 0.9641 |
| 0.0532 | 0.35 | 500 | 0.1711 | 0.9483 |
| 0.1004 | 0.42 | 600 | 0.1746 | 0.9526 |
| 0.0962 | 0.49 | 700 | 0.1598 | 0.9555 |
| 0.1579 | 0.56 | 800 | 0.1741 | 0.9440 |
| 0.0532 | 0.63 | 900 | 0.0974 | 0.9670 |
| 0.1594 | 0.7 | 1000 | 0.2842 | 0.9181 |
| 0.0488 | 0.77 | 1100 | 0.2928 | 0.9224 |
| 0.1122 | 0.84 | 1200 | 0.3095 | 0.9138 |
| 0.1252 | 0.91 | 1300 | 0.1411 | 0.9569 |
| 0.0517 | 0.97 | 1400 | 0.1378 | 0.9684 |
| 0.047 | 1.04 | 1500 | 0.2595 | 0.9483 |
| 0.0478 | 1.11 | 1600 | 0.1425 | 0.9583 |
| 0.0107 | 1.18 | 1700 | 0.1135 | 0.9684 |
| 0.0021 | 1.25 | 1800 | 0.1428 | 0.9598 |
| 0.036 | 1.32 | 1900 | 0.1851 | 0.9583 |
| 0.0733 | 1.39 | 2000 | 0.1801 | 0.9583 |
| 0.0549 | 1.46 | 2100 | 0.1917 | 0.9598 |
| 0.0442 | 1.53 | 2200 | 0.1538 | 0.9655 |
| 0.0196 | 1.6 | 2300 | 0.1411 | 0.9698 |
| 0.0809 | 1.67 | 2400 | 0.1862 | 0.9540 |
| 0.0004 | 1.74 | 2500 | 0.1325 | 0.9698 |
| 0.0404 | 1.81 | 2600 | 0.1246 | 0.9713 |
| 0.0691 | 1.88 | 2700 | 0.1961 | 0.9598 |
| 0.0088 | 1.95 | 2800 | 0.1841 | 0.9684 |
| 0.0029 | 2.02 | 2900 | 0.1057 | 0.9813 |
| 0.0005 | 2.09 | 3000 | 0.1131 | 0.9741 |
| 0.0001 | 2.16 | 3100 | 0.0892 | 0.9813 |
| 0.0002 | 2.23 | 3200 | 0.0757 | 0.9828 |
| 0.0186 | 2.3 | 3300 | 0.0794 | 0.9784 |
| 0.0127 | 2.37 | 3400 | 0.1100 | 0.9770 |
| 0.0048 | 2.44 | 3500 | 0.1386 | 0.9799 |
| 0.0048 | 2.51 | 3600 | 0.0635 | 0.9899 |
| 0.001 | 2.58 | 3700 | 0.0997 | 0.9799 |
| 0.0005 | 2.65 | 3800 | 0.1119 | 0.9756 |
| 0.0006 | 2.72 | 3900 | 0.1292 | 0.9713 |
| 0.0003 | 2.79 | 4000 | 0.1186 | 0.9770 |
| 0.0137 | 2.86 | 4100 | 0.0969 | 0.9770 |
| 0.0001 | 2.92 | 4200 | 0.0738 | 0.9842 |
| 0.0001 | 2.99 | 4300 | 0.1236 | 0.9828 |
| 0.0001 | 3.06 | 4400 | 0.0932 | 0.9856 |
| 0.0001 | 3.13 | 4500 | 0.0992 | 0.9799 |
| 0.0001 | 3.2 | 4600 | 0.0960 | 0.9828 |
| 0.0001 | 3.27 | 4700 | 0.1123 | 0.9799 |
| 0.0001 | 3.34 | 4800 | 0.1107 | 0.9813 |
| 0.0029 | 3.41 | 4900 | 0.1041 | 0.9842 |
| 0.0001 | 3.48 | 5000 | 0.1074 | 0.9828 |
| 0.0001 | 3.55 | 5100 | 0.1111 | 0.9799 |
| 0.0001 | 3.62 | 5200 | 0.1088 | 0.9784 |
| 0.0001 | 3.69 | 5300 | 0.0936 | 0.9813 |
| 0.0001 | 3.76 | 5400 | 0.0915 | 0.9799 |
| 0.0001 | 3.83 | 5500 | 0.0897 | 0.9799 |
| 0.0001 | 3.9 | 5600 | 0.0875 | 0.9770 |
| 0.0 | 3.97 | 5700 | 0.0856 | 0.9784 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|