File size: 7,170 Bytes
8a998d5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: resnet-18-feature-extraction
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.95
- name: Precision
type: precision
value: 0.9652777777777778
- name: Recall
type: recall
value: 0.9788732394366197
- name: F1
type: f1
value: 0.972027972027972
---
<!-- 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. -->
# resnet-18-feature-extraction
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1485
- Accuracy: 0.95
- Precision: 0.9653
- Recall: 0.9789
- F1: 0.9720
- Roc Auc: 0.8505
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 0.8 | 2 | 0.6232 | 0.75 | 0.9636 | 0.7465 | 0.8413 | 0.7621 |
| No log | 1.8 | 4 | 0.6971 | 0.4875 | 1.0 | 0.4225 | 0.5941 | 0.7113 |
| No log | 2.8 | 6 | 0.7915 | 0.2875 | 1.0 | 0.1972 | 0.3294 | 0.5986 |
| No log | 3.8 | 8 | 0.8480 | 0.2875 | 1.0 | 0.1972 | 0.3294 | 0.5986 |
| 0.8651 | 4.8 | 10 | 0.9094 | 0.2562 | 1.0 | 0.1620 | 0.2788 | 0.5810 |
| 0.8651 | 5.8 | 12 | 0.7470 | 0.5625 | 1.0 | 0.5070 | 0.6729 | 0.7535 |
| 0.8651 | 6.8 | 14 | 0.5915 | 0.85 | 1.0 | 0.8310 | 0.9077 | 0.9155 |
| 0.8651 | 7.8 | 16 | 0.4817 | 0.8875 | 0.9844 | 0.8873 | 0.9333 | 0.8881 |
| 0.8651 | 8.8 | 18 | 0.3455 | 0.9187 | 0.9778 | 0.9296 | 0.9531 | 0.8815 |
| 0.5349 | 9.8 | 20 | 0.2966 | 0.9187 | 0.9708 | 0.9366 | 0.9534 | 0.8572 |
| 0.5349 | 10.8 | 22 | 0.2347 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.5349 | 11.8 | 24 | 0.2468 | 0.9313 | 0.9645 | 0.9577 | 0.9611 | 0.8400 |
| 0.5349 | 12.8 | 26 | 0.2310 | 0.9563 | 0.9720 | 0.9789 | 0.9754 | 0.8783 |
| 0.5349 | 13.8 | 28 | 0.2083 | 0.9313 | 0.9580 | 0.9648 | 0.9614 | 0.8157 |
| 0.3593 | 14.8 | 30 | 0.1840 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
| 0.3593 | 15.8 | 32 | 0.1947 | 0.9375 | 0.9648 | 0.9648 | 0.9648 | 0.8435 |
| 0.3593 | 16.8 | 34 | 0.1837 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
| 0.3593 | 17.8 | 36 | 0.1819 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
| 0.3593 | 18.8 | 38 | 0.1924 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
| 0.2737 | 19.8 | 40 | 0.1990 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.2737 | 20.8 | 42 | 0.1759 | 0.95 | 0.9718 | 0.9718 | 0.9718 | 0.8748 |
| 0.2737 | 21.8 | 44 | 0.1804 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
| 0.2737 | 22.8 | 46 | 0.1666 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
| 0.2737 | 23.8 | 48 | 0.1534 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
| 0.2278 | 24.8 | 50 | 0.1612 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
| 0.2278 | 25.8 | 52 | 0.1535 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
| 0.2278 | 26.8 | 54 | 0.1568 | 0.9437 | 0.9716 | 0.9648 | 0.9682 | 0.8713 |
| 0.2278 | 27.8 | 56 | 0.2107 | 0.9375 | 0.9714 | 0.9577 | 0.9645 | 0.8678 |
| 0.2278 | 28.8 | 58 | 0.1592 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
| 0.2057 | 29.8 | 60 | 0.1557 | 0.9375 | 0.9648 | 0.9648 | 0.9648 | 0.8435 |
| 0.2057 | 30.8 | 62 | 0.1714 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
| 0.2057 | 31.8 | 64 | 0.1571 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.2057 | 32.8 | 66 | 0.1574 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
| 0.2057 | 33.8 | 68 | 0.1423 | 0.9563 | 0.9720 | 0.9789 | 0.9754 | 0.8783 |
| 0.2 | 34.8 | 70 | 0.1677 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
| 0.2 | 35.8 | 72 | 0.1560 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
| 0.2 | 36.8 | 74 | 0.1594 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
| 0.2 | 37.8 | 76 | 0.1512 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
| 0.2 | 38.8 | 78 | 0.1396 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
| 0.1838 | 39.8 | 80 | 0.1509 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
| 0.1838 | 40.8 | 82 | 0.1529 | 0.95 | 0.9718 | 0.9718 | 0.9718 | 0.8748 |
| 0.1838 | 41.8 | 84 | 0.1506 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.1838 | 42.8 | 86 | 0.1549 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.1838 | 43.8 | 88 | 0.1331 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
| 0.1872 | 44.8 | 90 | 0.1409 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
| 0.1872 | 45.8 | 92 | 0.1639 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
| 0.1872 | 46.8 | 94 | 0.1391 | 0.95 | 0.9589 | 0.9859 | 0.9722 | 0.8263 |
| 0.1872 | 47.8 | 96 | 0.1436 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
| 0.1872 | 48.8 | 98 | 0.1442 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
| 0.185 | 49.8 | 100 | 0.1485 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|