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_sgd_001_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: 0.8851913477537438
smids_5x_beit_base_sgd_001_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: 0.2987
- Accuracy: 0.8852
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: 0.001
- 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 |
---|---|---|---|---|
0.6816 | 1.0 | 375 | 0.6833 | 0.7338 |
0.5586 | 2.0 | 750 | 0.5192 | 0.7987 |
0.4343 | 3.0 | 1125 | 0.4591 | 0.8037 |
0.3314 | 4.0 | 1500 | 0.4185 | 0.8270 |
0.3478 | 5.0 | 1875 | 0.4048 | 0.8369 |
0.3776 | 6.0 | 2250 | 0.3805 | 0.8336 |
0.3391 | 7.0 | 2625 | 0.3595 | 0.8403 |
0.3304 | 8.0 | 3000 | 0.3500 | 0.8469 |
0.2847 | 9.0 | 3375 | 0.3354 | 0.8602 |
0.2987 | 10.0 | 3750 | 0.3303 | 0.8519 |
0.2929 | 11.0 | 4125 | 0.3216 | 0.8669 |
0.3369 | 12.0 | 4500 | 0.3243 | 0.8569 |
0.2344 | 13.0 | 4875 | 0.3140 | 0.8619 |
0.2532 | 14.0 | 5250 | 0.3121 | 0.8669 |
0.2563 | 15.0 | 5625 | 0.3059 | 0.8769 |
0.2186 | 16.0 | 6000 | 0.3073 | 0.8652 |
0.3036 | 17.0 | 6375 | 0.3090 | 0.8669 |
0.2499 | 18.0 | 6750 | 0.3061 | 0.8719 |
0.2571 | 19.0 | 7125 | 0.2980 | 0.8752 |
0.287 | 20.0 | 7500 | 0.3028 | 0.8719 |
0.2417 | 21.0 | 7875 | 0.2951 | 0.8819 |
0.2578 | 22.0 | 8250 | 0.2968 | 0.8769 |
0.2485 | 23.0 | 8625 | 0.3021 | 0.8752 |
0.2288 | 24.0 | 9000 | 0.2899 | 0.8852 |
0.1978 | 25.0 | 9375 | 0.3001 | 0.8785 |
0.2856 | 26.0 | 9750 | 0.2985 | 0.8819 |
0.2426 | 27.0 | 10125 | 0.2936 | 0.8852 |
0.222 | 28.0 | 10500 | 0.2944 | 0.8852 |
0.2193 | 29.0 | 10875 | 0.2940 | 0.8802 |
0.2438 | 30.0 | 11250 | 0.2935 | 0.8802 |
0.1916 | 31.0 | 11625 | 0.2931 | 0.8735 |
0.2423 | 32.0 | 12000 | 0.2890 | 0.8869 |
0.207 | 33.0 | 12375 | 0.2979 | 0.8769 |
0.1912 | 34.0 | 12750 | 0.3002 | 0.8885 |
0.2247 | 35.0 | 13125 | 0.2940 | 0.8819 |
0.1554 | 36.0 | 13500 | 0.2936 | 0.8819 |
0.1927 | 37.0 | 13875 | 0.2998 | 0.8769 |
0.1704 | 38.0 | 14250 | 0.2978 | 0.8835 |
0.2119 | 39.0 | 14625 | 0.3017 | 0.8769 |
0.1926 | 40.0 | 15000 | 0.2970 | 0.8819 |
0.1898 | 41.0 | 15375 | 0.3000 | 0.8785 |
0.2087 | 42.0 | 15750 | 0.2990 | 0.8819 |
0.1865 | 43.0 | 16125 | 0.2976 | 0.8835 |
0.2027 | 44.0 | 16500 | 0.3000 | 0.8819 |
0.1873 | 45.0 | 16875 | 0.2993 | 0.8835 |
0.2551 | 46.0 | 17250 | 0.2983 | 0.8819 |
0.227 | 47.0 | 17625 | 0.2985 | 0.8852 |
0.2135 | 48.0 | 18000 | 0.3011 | 0.8785 |
0.2131 | 49.0 | 18375 | 0.2986 | 0.8852 |
0.2054 | 50.0 | 18750 | 0.2987 | 0.8852 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2