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_fold5
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: 0.9066666666666666
smids_5x_beit_base_adamax_0001_fold5
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: 1.0130
- Accuracy: 0.9067
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 |
---|---|---|---|---|
0.2609 | 1.0 | 375 | 0.4392 | 0.83 |
0.2435 | 2.0 | 750 | 0.3135 | 0.87 |
0.1555 | 3.0 | 1125 | 0.3719 | 0.885 |
0.1553 | 4.0 | 1500 | 0.4465 | 0.89 |
0.1 | 5.0 | 1875 | 0.4697 | 0.88 |
0.0953 | 6.0 | 2250 | 0.5131 | 0.895 |
0.1058 | 7.0 | 2625 | 0.5042 | 0.9033 |
0.0095 | 8.0 | 3000 | 0.6171 | 0.9033 |
0.0607 | 9.0 | 3375 | 0.5787 | 0.89 |
0.0225 | 10.0 | 3750 | 0.6004 | 0.8983 |
0.0522 | 11.0 | 4125 | 0.6713 | 0.89 |
0.0354 | 12.0 | 4500 | 0.8122 | 0.9 |
0.0281 | 13.0 | 4875 | 0.7547 | 0.8833 |
0.0538 | 14.0 | 5250 | 0.6866 | 0.88 |
0.0498 | 15.0 | 5625 | 0.7040 | 0.8783 |
0.0034 | 16.0 | 6000 | 0.6946 | 0.8883 |
0.0375 | 17.0 | 6375 | 0.7067 | 0.88 |
0.0372 | 18.0 | 6750 | 0.8461 | 0.875 |
0.0081 | 19.0 | 7125 | 0.5733 | 0.9 |
0.025 | 20.0 | 7500 | 0.6029 | 0.9167 |
0.0105 | 21.0 | 7875 | 0.6183 | 0.885 |
0.0342 | 22.0 | 8250 | 0.5174 | 0.9217 |
0.0291 | 23.0 | 8625 | 0.5708 | 0.9083 |
0.0316 | 24.0 | 9000 | 0.7866 | 0.8833 |
0.0002 | 25.0 | 9375 | 0.8031 | 0.895 |
0.0548 | 26.0 | 9750 | 0.7954 | 0.8933 |
0.0233 | 27.0 | 10125 | 0.8188 | 0.895 |
0.0003 | 28.0 | 10500 | 0.7997 | 0.9 |
0.0063 | 29.0 | 10875 | 0.8708 | 0.89 |
0.0025 | 30.0 | 11250 | 0.8386 | 0.8967 |
0.0008 | 31.0 | 11625 | 0.8998 | 0.8833 |
0.0 | 32.0 | 12000 | 0.9085 | 0.8967 |
0.0005 | 33.0 | 12375 | 0.7875 | 0.905 |
0.0 | 34.0 | 12750 | 0.9329 | 0.8983 |
0.0001 | 35.0 | 13125 | 0.7985 | 0.9017 |
0.0 | 36.0 | 13500 | 0.8234 | 0.8983 |
0.0 | 37.0 | 13875 | 0.8947 | 0.9033 |
0.005 | 38.0 | 14250 | 0.9096 | 0.9067 |
0.0291 | 39.0 | 14625 | 0.9293 | 0.9117 |
0.0006 | 40.0 | 15000 | 0.8881 | 0.9117 |
0.0 | 41.0 | 15375 | 1.0854 | 0.8967 |
0.0003 | 42.0 | 15750 | 0.9486 | 0.8983 |
0.0 | 43.0 | 16125 | 0.9324 | 0.91 |
0.0 | 44.0 | 16500 | 0.9408 | 0.9083 |
0.0 | 45.0 | 16875 | 1.0069 | 0.9067 |
0.0 | 46.0 | 17250 | 1.0803 | 0.9 |
0.013 | 47.0 | 17625 | 1.0261 | 0.905 |
0.0 | 48.0 | 18000 | 1.0163 | 0.9067 |
0.0 | 49.0 | 18375 | 1.0208 | 0.9083 |
0.0021 | 50.0 | 18750 | 1.0130 | 0.9067 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2