metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: BEiT-RD-DA
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6654545454545454
BEiT-RD-DA
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: 1.9617
- Accuracy: 0.6655
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4123 | 1.0 | 96 | 1.4099 | 0.4927 |
| 0.9503 | 2.0 | 192 | 1.8852 | 0.4927 |
| 0.8284 | 3.0 | 288 | 2.1702 | 0.5073 |
| 0.7677 | 4.0 | 384 | 2.0408 | 0.5345 |
| 0.788 | 5.0 | 480 | 2.7991 | 0.5127 |
| 0.5822 | 6.0 | 576 | 2.0951 | 0.5636 |
| 0.5172 | 7.0 | 672 | 2.5977 | 0.5364 |
| 0.4615 | 8.0 | 768 | 2.0968 | 0.58 |
| 0.3672 | 9.0 | 864 | 2.8535 | 0.5436 |
| 0.379 | 10.0 | 960 | 2.9515 | 0.5382 |
| 0.3301 | 11.0 | 1056 | 2.7200 | 0.5582 |
| 0.2786 | 12.0 | 1152 | 1.9000 | 0.6273 |
| 0.2746 | 13.0 | 1248 | 3.1768 | 0.5364 |
| 0.2298 | 14.0 | 1344 | 3.1003 | 0.5527 |
| 0.2013 | 15.0 | 1440 | 2.3441 | 0.6182 |
| 0.2225 | 16.0 | 1536 | 3.0214 | 0.5709 |
| 0.2229 | 17.0 | 1632 | 2.0676 | 0.6164 |
| 0.2024 | 18.0 | 1728 | 2.6478 | 0.5673 |
| 0.1401 | 19.0 | 1824 | 2.8952 | 0.5636 |
| 0.1984 | 20.0 | 1920 | 2.3083 | 0.6145 |
| 0.1788 | 21.0 | 2016 | 3.7702 | 0.52 |
| 0.1907 | 22.0 | 2112 | 1.9617 | 0.6655 |
| 0.1113 | 23.0 | 2208 | 2.6546 | 0.5964 |
| 0.1293 | 24.0 | 2304 | 2.6427 | 0.6036 |
| 0.1354 | 25.0 | 2400 | 3.4105 | 0.5527 |
| 0.1447 | 26.0 | 2496 | 2.5460 | 0.6127 |
| 0.0995 | 27.0 | 2592 | 2.9865 | 0.5855 |
| 0.1369 | 28.0 | 2688 | 3.5281 | 0.5545 |
| 0.1238 | 29.0 | 2784 | 2.8161 | 0.6018 |
| 0.1256 | 30.0 | 2880 | 3.4917 | 0.5491 |
| 0.1064 | 31.0 | 2976 | 3.0659 | 0.58 |
| 0.1333 | 32.0 | 3072 | 3.5972 | 0.5473 |
| 0.1134 | 33.0 | 3168 | 3.6116 | 0.54 |
| 0.0831 | 34.0 | 3264 | 3.5308 | 0.5509 |
| 0.1035 | 35.0 | 3360 | 3.4789 | 0.5582 |
| 0.0957 | 36.0 | 3456 | 3.6358 | 0.5509 |
| 0.0764 | 37.0 | 3552 | 3.3639 | 0.5709 |
| 0.072 | 38.0 | 3648 | 3.5639 | 0.5564 |
| 0.0727 | 39.0 | 3744 | 3.5193 | 0.5582 |
| 0.0619 | 40.0 | 3840 | 3.5836 | 0.5582 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0