File size: 4,241 Bytes
361a93e 9eedb9a 361a93e 9eedb9a 361a93e 9eedb9a 361a93e 9eedb9a 361a93e 9eedb9a 361a93e 9eedb9a 361a93e |
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 |
---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-finetuned-magic-cards-230117
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. -->
# segformer-b5-finetuned-magic-cards-230117
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the andrewljohnson/magic_cards dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2096
- Mean Iou: 0.6629
- Mean Accuracy: 0.9944
- Overall Accuracy: 0.9944
- Accuracy Unlabeled: nan
- Accuracy Front: 0.9997
- Accuracy Back: 0.9891
- Iou Unlabeled: 0.0
- Iou Front: 0.9997
- Iou Back: 0.9891
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Front | Accuracy Back | Iou Unlabeled | Iou Front | Iou Back |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:-------------:|:-------------:|:---------:|:--------:|
| 0.496 | 0.74 | 20 | 0.4441 | 0.6552 | 0.9838 | 0.9838 | nan | 0.9786 | 0.9890 | 0.0 | 0.9786 | 0.9869 |
| 0.1693 | 1.48 | 40 | 0.4098 | 0.6597 | 0.9897 | 0.9897 | nan | 0.9943 | 0.9851 | 0.0 | 0.9943 | 0.9849 |
| 0.1172 | 2.22 | 60 | 0.2734 | 0.6582 | 0.9874 | 0.9874 | nan | 0.9977 | 0.9770 | 0.0 | 0.9977 | 0.9770 |
| 0.1335 | 2.96 | 80 | 0.2637 | 0.6609 | 0.9914 | 0.9914 | nan | 0.9959 | 0.9869 | 0.0 | 0.9959 | 0.9869 |
| 0.0781 | 3.7 | 100 | 0.5178 | 0.6644 | 0.9966 | 0.9966 | nan | 0.9998 | 0.9933 | 0.0 | 0.9998 | 0.9933 |
| 0.1302 | 4.44 | 120 | 0.2753 | 0.6652 | 0.9978 | 0.9978 | nan | 0.9993 | 0.9962 | 0.0 | 0.9993 | 0.9962 |
| 0.0688 | 5.19 | 140 | 0.1458 | 0.6618 | 0.9926 | 0.9926 | nan | 0.9950 | 0.9903 | 0.0 | 0.9950 | 0.9903 |
| 0.0866 | 5.93 | 160 | 0.1763 | 0.6636 | 0.9954 | 0.9954 | nan | 0.9962 | 0.9946 | 0.0 | 0.9962 | 0.9946 |
| 0.0525 | 6.67 | 180 | 0.1812 | 0.6627 | 0.9941 | 0.9941 | nan | 0.9988 | 0.9895 | 0.0 | 0.9988 | 0.9895 |
| 0.0679 | 7.41 | 200 | 0.2246 | 0.6625 | 0.9937 | 0.9937 | nan | 0.9990 | 0.9884 | 0.0 | 0.9990 | 0.9884 |
| 0.0424 | 8.15 | 220 | 0.2079 | 0.6623 | 0.9934 | 0.9935 | nan | 0.9996 | 0.9873 | 0.0 | 0.9996 | 0.9873 |
| 0.0349 | 8.89 | 240 | 0.1559 | 0.6626 | 0.9939 | 0.9940 | nan | 0.9987 | 0.9892 | 0.0 | 0.9987 | 0.9892 |
| 0.0357 | 9.63 | 260 | 0.2096 | 0.6629 | 0.9944 | 0.9944 | nan | 0.9997 | 0.9891 | 0.0 | 0.9997 | 0.9891 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.0.dev0
|