|
--- |
|
license: apache-2.0 |
|
base_model: microsoft/beit-base-patch16-224 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
model-index: |
|
- name: beit-base-patch16-224 |
|
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: 0.8966666666666666 |
|
- name: Precision |
|
type: precision |
|
value: 0.891224605606628 |
|
- name: Recall |
|
type: recall |
|
value: 0.8966666666666666 |
|
--- |
|
|
|
<!-- 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 |
|
|
|
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: 0.2426 |
|
- Accuracy: 0.8967 |
|
- Precision: 0.8912 |
|
- Recall: 0.8967 |
|
- F1 Score: 0.8935 |
|
|
|
## 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: 64 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 256 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
|
| No log | 1.0 | 4 | 0.4160 | 0.8667 | 0.8037 | 0.8667 | 0.8160 | |
|
| No log | 2.0 | 8 | 0.4441 | 0.8375 | 0.7702 | 0.8375 | 0.7998 | |
|
| No log | 3.0 | 12 | 0.4451 | 0.8667 | 0.8559 | 0.8667 | 0.8605 | |
|
| 0.4959 | 4.0 | 16 | 0.3299 | 0.8792 | 0.8545 | 0.8792 | 0.8551 | |
|
| 0.4959 | 5.0 | 20 | 0.3813 | 0.8458 | 0.8776 | 0.8458 | 0.8580 | |
|
| 0.4959 | 6.0 | 24 | 0.2802 | 0.8958 | 0.8851 | 0.8958 | 0.8881 | |
|
| 0.4959 | 7.0 | 28 | 0.2991 | 0.8875 | 0.8830 | 0.8875 | 0.8850 | |
|
| 0.3696 | 8.0 | 32 | 0.2565 | 0.8917 | 0.8792 | 0.8917 | 0.8825 | |
|
| 0.3696 | 9.0 | 36 | 0.2582 | 0.9 | 0.8949 | 0.9 | 0.8970 | |
|
| 0.3696 | 10.0 | 40 | 0.2472 | 0.9 | 0.8927 | 0.9 | 0.8954 | |
|
| 0.3696 | 11.0 | 44 | 0.2463 | 0.9208 | 0.9179 | 0.9208 | 0.9191 | |
|
| 0.3299 | 12.0 | 48 | 0.2474 | 0.9167 | 0.9145 | 0.9167 | 0.9155 | |
|
| 0.3299 | 13.0 | 52 | 0.2826 | 0.8833 | 0.8971 | 0.8833 | 0.8889 | |
|
| 0.3299 | 14.0 | 56 | 0.2720 | 0.8958 | 0.9035 | 0.8958 | 0.8991 | |
|
| 0.3036 | 15.0 | 60 | 0.2629 | 0.9 | 0.9059 | 0.9 | 0.9025 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.2 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
