metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_0001_fold3
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: 0.88
smids_5x_beit_base_adamax_0001_fold3
This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2034
- Accuracy: 0.88
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.6864 | 1.0 | 375 | 0.6860 | 0.7217 |
0.4252 | 2.0 | 750 | 0.6416 | 0.7633 |
0.3187 | 3.0 | 1125 | 0.4319 | 0.845 |
0.2894 | 4.0 | 1500 | 0.4449 | 0.84 |
0.2471 | 5.0 | 1875 | 0.4205 | 0.8483 |
0.1902 | 6.0 | 2250 | 0.3865 | 0.8633 |
0.2206 | 7.0 | 2625 | 0.4140 | 0.86 |
0.1469 | 8.0 | 3000 | 0.4250 | 0.875 |
0.0865 | 9.0 | 3375 | 0.5396 | 0.8717 |
0.0868 | 10.0 | 3750 | 0.6873 | 0.8583 |
0.0906 | 11.0 | 4125 | 0.4484 | 0.8883 |
0.0585 | 12.0 | 4500 | 0.6803 | 0.88 |
0.1093 | 13.0 | 4875 | 0.5271 | 0.8833 |
0.0782 | 14.0 | 5250 | 0.6855 | 0.8633 |
0.0163 | 15.0 | 5625 | 0.6773 | 0.865 |
0.0996 | 16.0 | 6000 | 0.6530 | 0.8733 |
0.0415 | 17.0 | 6375 | 0.8051 | 0.88 |
0.0673 | 18.0 | 6750 | 0.7716 | 0.88 |
0.1076 | 19.0 | 7125 | 0.7362 | 0.8783 |
0.0103 | 20.0 | 7500 | 0.7231 | 0.875 |
0.0579 | 21.0 | 7875 | 0.7618 | 0.88 |
0.0461 | 22.0 | 8250 | 0.9110 | 0.8717 |
0.0544 | 23.0 | 8625 | 0.7656 | 0.8767 |
0.0075 | 24.0 | 9000 | 0.8172 | 0.8933 |
0.0242 | 25.0 | 9375 | 1.0276 | 0.865 |
0.001 | 26.0 | 9750 | 1.1126 | 0.8717 |
0.0272 | 27.0 | 10125 | 0.9522 | 0.8833 |
0.0034 | 28.0 | 10500 | 0.9353 | 0.87 |
0.0223 | 29.0 | 10875 | 0.8506 | 0.8833 |
0.0058 | 30.0 | 11250 | 1.1236 | 0.855 |
0.0185 | 31.0 | 11625 | 1.0029 | 0.8733 |
0.0001 | 32.0 | 12000 | 0.8488 | 0.88 |
0.0006 | 33.0 | 12375 | 0.8729 | 0.8767 |
0.0005 | 34.0 | 12750 | 1.0735 | 0.875 |
0.0003 | 35.0 | 13125 | 1.0286 | 0.885 |
0.0 | 36.0 | 13500 | 0.8268 | 0.89 |
0.0001 | 37.0 | 13875 | 1.0063 | 0.875 |
0.0002 | 38.0 | 14250 | 0.9833 | 0.885 |
0.0009 | 39.0 | 14625 | 0.9292 | 0.8917 |
0.0034 | 40.0 | 15000 | 0.9953 | 0.8883 |
0.0 | 41.0 | 15375 | 1.0555 | 0.885 |
0.0 | 42.0 | 15750 | 1.0377 | 0.89 |
0.0 | 43.0 | 16125 | 1.1991 | 0.8717 |
0.0 | 44.0 | 16500 | 1.1156 | 0.8783 |
0.0 | 45.0 | 16875 | 1.1077 | 0.8717 |
0.0001 | 46.0 | 17250 | 1.0635 | 0.8817 |
0.0 | 47.0 | 17625 | 1.1588 | 0.8817 |
0.0 | 48.0 | 18000 | 1.1268 | 0.8867 |
0.0 | 49.0 | 18375 | 1.2011 | 0.88 |
0.0 | 50.0 | 18750 | 1.2034 | 0.88 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2