File size: 3,149 Bytes
46be074 |
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 |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Boya1_3Class_RMSprop_1-e5_20Epoch_Beit-base-patch16_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8233062330623306
---
<!-- 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. -->
# Boya1_3Class_RMSprop_1-e5_20Epoch_Beit-base-patch16_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8083
- Accuracy: 0.8233
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3492 | 1.0 | 923 | 0.4516 | 0.8087 |
| 0.3947 | 2.0 | 1846 | 0.4372 | 0.8144 |
| 0.3321 | 3.0 | 2769 | 0.4856 | 0.8220 |
| 0.1372 | 4.0 | 3692 | 0.6093 | 0.8271 |
| 0.2202 | 5.0 | 4615 | 0.8876 | 0.8184 |
| 0.0611 | 6.0 | 5538 | 1.1112 | 0.8222 |
| 0.0654 | 7.0 | 6461 | 1.2516 | 0.8241 |
| 0.0494 | 8.0 | 7384 | 1.5011 | 0.8209 |
| 0.0614 | 9.0 | 8307 | 1.3879 | 0.8190 |
| 0.1723 | 10.0 | 9230 | 1.5852 | 0.8160 |
| 0.0314 | 11.0 | 10153 | 1.7058 | 0.8209 |
| 0.006 | 12.0 | 11076 | 1.7427 | 0.8233 |
| 0.0603 | 13.0 | 11999 | 1.6775 | 0.8206 |
| 0.0734 | 14.0 | 12922 | 1.7302 | 0.8257 |
| 0.0185 | 15.0 | 13845 | 1.7895 | 0.8236 |
| 0.0006 | 16.0 | 14768 | 1.7889 | 0.8220 |
| 0.0006 | 17.0 | 15691 | 1.8447 | 0.8198 |
| 0.0003 | 18.0 | 16614 | 1.8183 | 0.8184 |
| 0.0002 | 19.0 | 17537 | 1.8137 | 0.8176 |
| 0.0 | 20.0 | 18460 | 1.8083 | 0.8233 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|