|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- preprocessed1024_config |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: vit-cc-512-birads |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: preprocessed1024_config |
|
type: preprocessed1024_config |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: |
|
accuracy: 0.4943467336683417 |
|
- name: F1 |
|
type: f1 |
|
value: |
|
f1: 0.3929699341372617 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# vit-cc-512-birads |
|
|
|
This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.1133 |
|
- Accuracy: {'accuracy': 0.4943467336683417} |
|
- F1: {'f1': 0.3929699341372617} |
|
|
|
## 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: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:| |
|
| 1.1037 | 1.0 | 796 | 1.0357 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} | |
|
| 1.0588 | 2.0 | 1592 | 1.0446 | {'accuracy': 0.4623115577889447} | {'f1': 0.33094476503399495} | |
|
| 1.0486 | 3.0 | 2388 | 1.0408 | {'accuracy': 0.47361809045226133} | {'f1': 0.3313643442345453} | |
|
| 1.0288 | 4.0 | 3184 | 1.0186 | {'accuracy': 0.5050251256281407} | {'f1': 0.3404676010455165} | |
|
| 1.0284 | 5.0 | 3980 | 1.0288 | {'accuracy': 0.5037688442211056} | {'f1': 0.3406391773730375} | |
|
| 0.997 | 6.0 | 4776 | 1.0183 | {'accuracy': 0.5087939698492462} | {'f1': 0.3539488153998284} | |
|
| 0.9682 | 7.0 | 5572 | 1.0965 | {'accuracy': 0.4566582914572864} | {'f1': 0.3695106771946128} | |
|
| 0.9313 | 8.0 | 6368 | 1.0554 | {'accuracy': 0.4962311557788945} | {'f1': 0.38158088397057704} | |
|
| 0.8938 | 9.0 | 7164 | 1.0930 | {'accuracy': 0.4943467336683417} | {'f1': 0.38196414933207573} | |
|
| 0.8697 | 10.0 | 7960 | 1.1133 | {'accuracy': 0.4943467336683417} | {'f1': 0.3929699341372617} | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.12.0 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.12.1 |
|
|
