File size: 5,876 Bytes
aa277b6 1265a6b aa277b6 1265a6b aa277b6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
---
license: mit
tags:
- generated_from_trainer
- vision
- image-to-text
- image-captioning
datasets:
- imagefolder
model-index:
- name: git-base-pokemon
results: []
pipeline_tag: image-to-text
---
<!-- 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. -->
# git-base-pokemon
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1817
- Wer Score: 9.0938
## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.3974 | 0.7 | 50 | 4.5248 | 4.5234 |
| 2.2794 | 1.4 | 100 | 0.4021 | 5.1680 |
| 0.1697 | 2.1 | 150 | 0.1398 | 1.5039 |
| 0.0816 | 2.8 | 200 | 0.1458 | 9.9570 |
| 0.0556 | 3.5 | 250 | 0.1417 | 2.5234 |
| 0.043 | 4.2 | 300 | 0.1448 | 12.8086 |
| 0.0285 | 4.9 | 350 | 0.1469 | 7.3867 |
| 0.021 | 5.59 | 400 | 0.1505 | 13.0312 |
| 0.0205 | 6.29 | 450 | 0.1499 | 6.3281 |
| 0.0179 | 6.99 | 500 | 0.1527 | 13.0234 |
| 0.0157 | 7.69 | 550 | 0.1552 | 6.3047 |
| 0.015 | 8.39 | 600 | 0.1571 | 6.7656 |
| 0.015 | 9.09 | 650 | 0.1579 | 10.2305 |
| 0.0137 | 9.79 | 700 | 0.1585 | 11.4219 |
| 0.0132 | 10.49 | 750 | 0.1598 | 5.8320 |
| 0.0132 | 11.19 | 800 | 0.1591 | 12.0508 |
| 0.013 | 11.89 | 850 | 0.1612 | 7.9492 |
| 0.0117 | 12.59 | 900 | 0.1621 | 8.1758 |
| 0.0123 | 13.29 | 950 | 0.1632 | 12.9961 |
| 0.0125 | 13.99 | 1000 | 0.1613 | 10.2031 |
| 0.0116 | 14.69 | 1050 | 0.1642 | 5.7930 |
| 0.0112 | 15.38 | 1100 | 0.1636 | 6.1719 |
| 0.0112 | 16.08 | 1150 | 0.1652 | 7.2422 |
| 0.0107 | 16.78 | 1200 | 0.1644 | 12.9961 |
| 0.0108 | 17.48 | 1250 | 0.1661 | 5.0117 |
| 0.0109 | 18.18 | 1300 | 0.1658 | 7.3242 |
| 0.0108 | 18.88 | 1350 | 0.1691 | 6.0547 |
| 0.0101 | 19.58 | 1400 | 0.1690 | 6.9141 |
| 0.0103 | 20.28 | 1450 | 0.1692 | 7.1680 |
| 0.0107 | 20.98 | 1500 | 0.1702 | 12.3281 |
| 0.0099 | 21.68 | 1550 | 0.1708 | 10.75 |
| 0.0103 | 22.38 | 1600 | 0.1714 | 9.5586 |
| 0.0101 | 23.08 | 1650 | 0.1713 | 12.9805 |
| 0.0098 | 23.78 | 1700 | 0.1712 | 11.4883 |
| 0.0095 | 24.48 | 1750 | 0.1711 | 9.3320 |
| 0.0096 | 25.17 | 1800 | 0.1738 | 8.6523 |
| 0.0097 | 25.87 | 1850 | 0.1717 | 11.5078 |
| 0.0091 | 26.57 | 1900 | 0.1735 | 7.9570 |
| 0.0092 | 27.27 | 1950 | 0.1729 | 9.8242 |
| 0.0093 | 27.97 | 2000 | 0.1721 | 10.5078 |
| 0.0087 | 28.67 | 2050 | 0.1732 | 9.3906 |
| 0.009 | 29.37 | 2100 | 0.1760 | 8.0664 |
| 0.009 | 30.07 | 2150 | 0.1769 | 10.5312 |
| 0.0086 | 30.77 | 2200 | 0.1743 | 10.8555 |
| 0.0087 | 31.47 | 2250 | 0.1772 | 10.2188 |
| 0.0089 | 32.17 | 2300 | 0.1757 | 11.6016 |
| 0.0088 | 32.87 | 2350 | 0.1765 | 8.9297 |
| 0.0082 | 33.57 | 2400 | 0.1754 | 9.6484 |
| 0.0082 | 34.27 | 2450 | 0.1770 | 12.3711 |
| 0.0084 | 34.97 | 2500 | 0.1761 | 10.1523 |
| 0.0076 | 35.66 | 2550 | 0.1774 | 9.1055 |
| 0.0077 | 36.36 | 2600 | 0.1788 | 8.7852 |
| 0.0079 | 37.06 | 2650 | 0.1782 | 11.8086 |
| 0.0071 | 37.76 | 2700 | 0.1784 | 10.5234 |
| 0.0075 | 38.46 | 2750 | 0.1789 | 8.8828 |
| 0.0072 | 39.16 | 2800 | 0.1796 | 8.5664 |
| 0.0071 | 39.86 | 2850 | 0.1804 | 9.5391 |
| 0.0069 | 40.56 | 2900 | 0.1796 | 9.4062 |
| 0.0068 | 41.26 | 2950 | 0.1797 | 8.9883 |
| 0.0067 | 41.96 | 3000 | 0.1809 | 10.5273 |
| 0.0062 | 42.66 | 3050 | 0.1801 | 10.4531 |
| 0.0062 | 43.36 | 3100 | 0.1803 | 7.2188 |
| 0.0063 | 44.06 | 3150 | 0.1808 | 8.7930 |
| 0.0058 | 44.76 | 3200 | 0.1804 | 10.5156 |
| 0.0057 | 45.45 | 3250 | 0.1807 | 11.1328 |
| 0.0059 | 46.15 | 3300 | 0.1812 | 8.6875 |
| 0.0055 | 46.85 | 3350 | 0.1811 | 10.2773 |
| 0.0053 | 47.55 | 3400 | 0.1814 | 10.0391 |
| 0.0054 | 48.25 | 3450 | 0.1817 | 8.5391 |
| 0.0053 | 48.95 | 3500 | 0.1818 | 8.9688 |
| 0.005 | 49.65 | 3550 | 0.1817 | 9.0938 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|