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_rms_0001_fold2
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: 0.9068219633943427
smids_5x_beit_base_rms_0001_fold2
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: 0.9869
- Accuracy: 0.9068
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 |
---|---|---|---|---|
0.3386 | 1.0 | 375 | 0.2631 | 0.8902 |
0.2769 | 2.0 | 750 | 0.2812 | 0.8852 |
0.1948 | 3.0 | 1125 | 0.4161 | 0.8686 |
0.1489 | 4.0 | 1500 | 0.3316 | 0.8852 |
0.1015 | 5.0 | 1875 | 0.3966 | 0.8835 |
0.0659 | 6.0 | 2250 | 0.5521 | 0.8686 |
0.0987 | 7.0 | 2625 | 0.4706 | 0.8852 |
0.0304 | 8.0 | 3000 | 0.6100 | 0.8835 |
0.0177 | 9.0 | 3375 | 0.5599 | 0.8835 |
0.0365 | 10.0 | 3750 | 0.5970 | 0.8902 |
0.07 | 11.0 | 4125 | 0.5587 | 0.8869 |
0.025 | 12.0 | 4500 | 0.6283 | 0.8885 |
0.013 | 13.0 | 4875 | 0.4540 | 0.9035 |
0.0155 | 14.0 | 5250 | 0.6593 | 0.8869 |
0.0612 | 15.0 | 5625 | 0.6571 | 0.8935 |
0.0058 | 16.0 | 6000 | 0.6333 | 0.8835 |
0.0564 | 17.0 | 6375 | 0.5490 | 0.8918 |
0.0204 | 18.0 | 6750 | 0.7225 | 0.8985 |
0.0128 | 19.0 | 7125 | 0.4844 | 0.9135 |
0.0241 | 20.0 | 7500 | 0.5085 | 0.9018 |
0.0042 | 21.0 | 7875 | 0.5500 | 0.9135 |
0.0209 | 22.0 | 8250 | 0.6987 | 0.8869 |
0.0277 | 23.0 | 8625 | 0.7227 | 0.8902 |
0.027 | 24.0 | 9000 | 0.8023 | 0.8769 |
0.0061 | 25.0 | 9375 | 0.7219 | 0.8985 |
0.0004 | 26.0 | 9750 | 0.7303 | 0.8935 |
0.0002 | 27.0 | 10125 | 0.6194 | 0.9118 |
0.0002 | 28.0 | 10500 | 0.7358 | 0.9085 |
0.0068 | 29.0 | 10875 | 0.7598 | 0.9002 |
0.0002 | 30.0 | 11250 | 0.7703 | 0.8935 |
0.0136 | 31.0 | 11625 | 0.7951 | 0.8902 |
0.0053 | 32.0 | 12000 | 0.8891 | 0.8918 |
0.0038 | 33.0 | 12375 | 0.7625 | 0.9018 |
0.0002 | 34.0 | 12750 | 0.8776 | 0.9052 |
0.0 | 35.0 | 13125 | 0.9210 | 0.9002 |
0.0195 | 36.0 | 13500 | 0.7510 | 0.9151 |
0.0008 | 37.0 | 13875 | 0.7794 | 0.9135 |
0.0007 | 38.0 | 14250 | 0.8315 | 0.9085 |
0.0005 | 39.0 | 14625 | 0.7854 | 0.9151 |
0.0033 | 40.0 | 15000 | 0.8459 | 0.9101 |
0.0001 | 41.0 | 15375 | 0.9023 | 0.9002 |
0.0027 | 42.0 | 15750 | 1.0108 | 0.9018 |
0.0026 | 43.0 | 16125 | 1.0264 | 0.8952 |
0.0026 | 44.0 | 16500 | 0.9790 | 0.9035 |
0.0027 | 45.0 | 16875 | 0.9445 | 0.9101 |
0.0 | 46.0 | 17250 | 0.9135 | 0.9185 |
0.0057 | 47.0 | 17625 | 0.9222 | 0.9085 |
0.0 | 48.0 | 18000 | 0.9390 | 0.9085 |
0.0052 | 49.0 | 18375 | 0.9876 | 0.9052 |
0.0025 | 50.0 | 18750 | 0.9869 | 0.9068 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2