File size: 3,766 Bytes
1a106ca de54407 1a106ca 7ce2be9 1a106ca 7ce2be9 1a106ca 3ec96f7 7ce2be9 3ec96f7 1a106ca 7ce2be9 1a106ca |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: beit-finetuned-pokemon
results: []
---
<!-- 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. -->
# beit-finetuned-pokemon
This model is a fine-tuned version of [ydmeira/beit-finetuned-pokemon](https://huggingface.co/ydmeira/beit-finetuned-pokemon) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0219
- Mean Iou: 0.4955
- Mean Accuracy: 0.9910
- Overall Accuracy: 0.9910
- Per Category Iou: [0.0, 0.9909617791470107]
- Per Category Accuracy: [nan, 0.9909617791470107]
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:|
| 0.0354 | 0.21 | 1000 | 0.0347 | 0.4978 | 0.9955 | 0.9955 | [0.0, 0.9955007125868244] | [nan, 0.9955007125868244] |
| 0.0273 | 0.43 | 2000 | 0.0277 | 0.4951 | 0.9903 | 0.9903 | [0.0, 0.9902709092544748] | [nan, 0.9902709092544748] |
| 0.0307 | 0.64 | 3000 | 0.0788 | 0.4875 | 0.9751 | 0.9751 | [0.0, 0.9750850921785902] | [nan, 0.9750850921785902] |
| 0.0295 | 0.85 | 4000 | 0.0412 | 0.4939 | 0.9877 | 0.9877 | [0.0, 0.9877162657609527] | [nan, 0.9877162657609527] |
| 0.0255 | 1.07 | 5000 | 0.0842 | 0.4862 | 0.9723 | 0.9723 | [0.0, 0.972304346385062] | [nan, 0.972304346385062] |
| 0.0253 | 1.28 | 6000 | 0.0325 | 0.4950 | 0.9901 | 0.9901 | [0.0, 0.9900621363084688] | [nan, 0.9900621363084688] |
| 0.0239 | 1.49 | 7000 | 0.0440 | 0.4917 | 0.9835 | 0.9835 | [0.0, 0.9834701005512881] | [nan, 0.9834701005512881] |
| 0.0238 | 1.71 | 8000 | 0.0338 | 0.4950 | 0.9900 | 0.9900 | [0.0, 0.9899977115151821] | [nan, 0.9899977115151821] |
| 0.0223 | 1.92 | 9000 | 0.0319 | 0.4950 | 0.9900 | 0.9900 | [0.0, 0.989994712810938] | [nan, 0.989994712810938] |
| 0.0231 | 2.13 | 10000 | 0.0382 | 0.4921 | 0.9841 | 0.9841 | [0.0, 0.984106425591889] | [nan, 0.984106425591889] |
| 0.0205 | 2.35 | 11000 | 0.0450 | 0.4926 | 0.9851 | 0.9851 | [0.0, 0.9851146530893756] | [nan, 0.9851146530893756] |
| 0.0201 | 2.56 | 12000 | 0.0265 | 0.4954 | 0.9908 | 0.9908 | [0.0, 0.9908277212846449] | [nan, 0.9908277212846449] |
| 0.0188 | 2.77 | 13000 | 0.0377 | 0.4933 | 0.9866 | 0.9866 | [0.0, 0.9865726862234793] | [nan, 0.9865726862234793] |
| 0.0181 | 2.99 | 14000 | 0.0219 | 0.4955 | 0.9910 | 0.9910 | [0.0, 0.9909617791470107] | [nan, 0.9909617791470107] |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|