|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: dit-base-Tobacco_Dataset_v3 |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: imagefolder |
|
type: imagefolder |
|
config: data |
|
split: train |
|
args: data |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9418521177315147 |
|
language: |
|
- en |
|
pipeline_tag: image-classification |
|
--- |
|
|
|
# dit-base-Tobacco_Dataset_v3 |
|
|
|
This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1958 |
|
- Accuracy: 0.9419 |
|
- F1 |
|
- Weighted: 0.9403 |
|
- Micro: 0.9419 |
|
- Macro: 0.9278 |
|
|
|
## Model description |
|
|
|
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Document%20AI/Multiclass%20Classification/Tobacco-Related%20Documents/Tobacco%20Dataset%20%26%20DiT%20Transformer%20Project_v3.ipynb |
|
|
|
## Intended uses & limitations |
|
|
|
This model is intended to demonstrate my ability to solve a complex problem using technology. |
|
|
|
## Training and evaluation data |
|
|
|
Dataset Source: https://www.kaggle.com/datasets/patrickaudriaz/tobacco3482jpg |
|
|
|
## 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: 8 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:| |
|
| 1.9273 | 0.98 | 43 | 1.1368 | 0.5987 | 0.5462 | 0.5987 | 0.5175 | |
|
| 1.0685 | 1.98 | 86 | 0.5244 | 0.8248 | 0.7939 | 0.8248 | 0.7670 | |
|
| 0.7373 | 2.98 | 129 | 0.3631 | 0.8808 | 0.8610 | 0.8808 | 0.8318 | |
|
| 0.641 | 3.98 | 172 | 0.2884 | 0.9045 | 0.8967 | 0.9045 | 0.8732 | |
|
| 0.5579 | 4.98 | 215 | 0.2192 | 0.9361 | 0.9338 | 0.9361 | 0.9214 | |
|
| 0.5279 | 5.98 | 258 | 0.2292 | 0.9289 | 0.9263 | 0.9289 | 0.9137 | |
|
| 0.4918 | 6.98 | 301 | 0.2052 | 0.9368 | 0.9348 | 0.9368 | 0.9218 | |
|
| 0.4723 | 7.98 | 344 | 0.1958 | 0.9419 | 0.9403 | 0.9419 | 0.9278 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.1 |
|
- Pytorch 1.12.1 |
|
- Datasets 2.9.0 |
|
- Tokenizers 0.12.1 |
