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---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-85-fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9772727272727273
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beit-base-patch16-224-85-fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1430
- Accuracy: 0.9773
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.7308 | 0.5455 |
| No log | 2.0 | 4 | 0.7927 | 0.7045 |
| No log | 3.0 | 6 | 0.9672 | 0.7045 |
| No log | 4.0 | 8 | 0.6257 | 0.7045 |
| 0.6404 | 5.0 | 10 | 0.4646 | 0.7955 |
| 0.6404 | 6.0 | 12 | 0.5648 | 0.7045 |
| 0.6404 | 7.0 | 14 | 0.4389 | 0.7727 |
| 0.6404 | 8.0 | 16 | 0.4523 | 0.75 |
| 0.6404 | 9.0 | 18 | 0.4698 | 0.75 |
| 0.455 | 10.0 | 20 | 0.3707 | 0.8409 |
| 0.455 | 11.0 | 22 | 0.3594 | 0.8182 |
| 0.455 | 12.0 | 24 | 0.6136 | 0.7273 |
| 0.455 | 13.0 | 26 | 0.3022 | 0.8864 |
| 0.455 | 14.0 | 28 | 0.2919 | 0.8409 |
| 0.3981 | 15.0 | 30 | 0.3612 | 0.8182 |
| 0.3981 | 16.0 | 32 | 0.2492 | 0.8864 |
| 0.3981 | 17.0 | 34 | 0.2460 | 0.9091 |
| 0.3981 | 18.0 | 36 | 0.2931 | 0.8636 |
| 0.3981 | 19.0 | 38 | 0.1822 | 0.9091 |
| 0.3257 | 20.0 | 40 | 0.2060 | 0.9091 |
| 0.3257 | 21.0 | 42 | 0.2195 | 0.8864 |
| 0.3257 | 22.0 | 44 | 0.2624 | 0.9091 |
| 0.3257 | 23.0 | 46 | 0.2384 | 0.9091 |
| 0.3257 | 24.0 | 48 | 0.1767 | 0.9318 |
| 0.2553 | 25.0 | 50 | 0.2040 | 0.9318 |
| 0.2553 | 26.0 | 52 | 0.1981 | 0.9091 |
| 0.2553 | 27.0 | 54 | 0.1835 | 0.9318 |
| 0.2553 | 28.0 | 56 | 0.1820 | 0.9318 |
| 0.2553 | 29.0 | 58 | 0.1466 | 0.9545 |
| 0.2083 | 30.0 | 60 | 0.1668 | 0.9318 |
| 0.2083 | 31.0 | 62 | 0.2229 | 0.9318 |
| 0.2083 | 32.0 | 64 | 0.1783 | 0.9545 |
| 0.2083 | 33.0 | 66 | 0.1944 | 0.8864 |
| 0.2083 | 34.0 | 68 | 0.3025 | 0.9091 |
| 0.2353 | 35.0 | 70 | 0.4457 | 0.8409 |
| 0.2353 | 36.0 | 72 | 0.2759 | 0.9318 |
| 0.2353 | 37.0 | 74 | 0.2179 | 0.9318 |
| 0.2353 | 38.0 | 76 | 0.3911 | 0.9091 |
| 0.2353 | 39.0 | 78 | 0.5785 | 0.8409 |
| 0.1782 | 40.0 | 80 | 0.2339 | 0.9318 |
| 0.1782 | 41.0 | 82 | 0.2302 | 0.9091 |
| 0.1782 | 42.0 | 84 | 0.3967 | 0.8864 |
| 0.1782 | 43.0 | 86 | 0.4447 | 0.8636 |
| 0.1782 | 44.0 | 88 | 0.2020 | 0.9091 |
| 0.2059 | 45.0 | 90 | 0.1911 | 0.9318 |
| 0.2059 | 46.0 | 92 | 0.2609 | 0.9091 |
| 0.2059 | 47.0 | 94 | 0.2925 | 0.9091 |
| 0.2059 | 48.0 | 96 | 0.2079 | 0.9318 |
| 0.2059 | 49.0 | 98 | 0.1853 | 0.9318 |
| 0.1706 | 50.0 | 100 | 0.2860 | 0.9318 |
| 0.1706 | 51.0 | 102 | 0.3735 | 0.8636 |
| 0.1706 | 52.0 | 104 | 0.1968 | 0.9318 |
| 0.1706 | 53.0 | 106 | 0.1722 | 0.9318 |
| 0.1706 | 54.0 | 108 | 0.3123 | 0.8636 |
| 0.1429 | 55.0 | 110 | 0.3297 | 0.8864 |
| 0.1429 | 56.0 | 112 | 0.1430 | 0.9773 |
| 0.1429 | 57.0 | 114 | 0.1134 | 0.9773 |
| 0.1429 | 58.0 | 116 | 0.2312 | 0.9091 |
| 0.1429 | 59.0 | 118 | 0.2826 | 0.9091 |
| 0.1325 | 60.0 | 120 | 0.2417 | 0.9091 |
| 0.1325 | 61.0 | 122 | 0.1393 | 0.9318 |
| 0.1325 | 62.0 | 124 | 0.2178 | 0.9318 |
| 0.1325 | 63.0 | 126 | 0.3991 | 0.9091 |
| 0.1325 | 64.0 | 128 | 0.3325 | 0.9091 |
| 0.1481 | 65.0 | 130 | 0.2327 | 0.9091 |
| 0.1481 | 66.0 | 132 | 0.2885 | 0.9091 |
| 0.1481 | 67.0 | 134 | 0.3576 | 0.9091 |
| 0.1481 | 68.0 | 136 | 0.2686 | 0.9318 |
| 0.1481 | 69.0 | 138 | 0.1717 | 0.9545 |
| 0.1237 | 70.0 | 140 | 0.1493 | 0.9545 |
| 0.1237 | 71.0 | 142 | 0.1429 | 0.9318 |
| 0.1237 | 72.0 | 144 | 0.1790 | 0.9318 |
| 0.1237 | 73.0 | 146 | 0.1590 | 0.9318 |
| 0.1237 | 74.0 | 148 | 0.1971 | 0.8864 |
| 0.105 | 75.0 | 150 | 0.2229 | 0.9318 |
| 0.105 | 76.0 | 152 | 0.1789 | 0.8864 |
| 0.105 | 77.0 | 154 | 0.1671 | 0.9545 |
| 0.105 | 78.0 | 156 | 0.2435 | 0.9318 |
| 0.105 | 79.0 | 158 | 0.2658 | 0.9318 |
| 0.0923 | 80.0 | 160 | 0.2092 | 0.9318 |
| 0.0923 | 81.0 | 162 | 0.1748 | 0.9318 |
| 0.0923 | 82.0 | 164 | 0.1727 | 0.9318 |
| 0.0923 | 83.0 | 166 | 0.1945 | 0.9091 |
| 0.0923 | 84.0 | 168 | 0.2429 | 0.9318 |
| 0.1033 | 85.0 | 170 | 0.2796 | 0.9318 |
| 0.1033 | 86.0 | 172 | 0.2548 | 0.9318 |
| 0.1033 | 87.0 | 174 | 0.2379 | 0.9091 |
| 0.1033 | 88.0 | 176 | 0.2409 | 0.9091 |
| 0.1033 | 89.0 | 178 | 0.2421 | 0.9091 |
| 0.1073 | 90.0 | 180 | 0.2332 | 0.9091 |
| 0.1073 | 91.0 | 182 | 0.2231 | 0.9091 |
| 0.1073 | 92.0 | 184 | 0.2153 | 0.9318 |
| 0.1073 | 93.0 | 186 | 0.2088 | 0.9318 |
| 0.1073 | 94.0 | 188 | 0.2058 | 0.9318 |
| 0.104 | 95.0 | 190 | 0.2040 | 0.9318 |
| 0.104 | 96.0 | 192 | 0.2046 | 0.9318 |
| 0.104 | 97.0 | 194 | 0.2043 | 0.9318 |
| 0.104 | 98.0 | 196 | 0.2056 | 0.9318 |
| 0.104 | 99.0 | 198 | 0.2081 | 0.9318 |
| 0.0896 | 100.0 | 200 | 0.2097 | 0.9318 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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