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---
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: architectural_styles_classifier
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: 0.7223300970873786
---
<!-- 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. -->
# architectural_styles_classifier
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the Architectural styles dataset, retrieved from https://www.kaggle.com/datasets/dumitrux/architectural-styles-dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9412
- Accuracy: 0.7223
## Model description
Presentation about the model: https://www.canva.com/design/DAGLBMAs1K4/d8qvLN2nchSYVmnrwYzx0w/edit?utm_content=DAGLBMAs1K4&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton
You can try the model from Huggingface Space this link: https://huggingface.co/spaces/hanslab37/technospire
## Intended uses & limitations
The model were developed as part of experiment to learn about training a model and developing Image Classification model with Gradio in Huggingface. You can use it for experiment only. Not recommended for daily use.
## Training and evaluation data
https://www.kaggle.com/datasets/dumitrux/architectural-styles-dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0003
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.8855 | 0.9960 | 110 | 1.7753 | 0.4457 |
| 1.583 | 1.9921 | 220 | 1.6208 | 0.4829 |
| 1.4343 | 2.9972 | 331 | 1.3291 | 0.5851 |
| 1.2836 | 3.9932 | 441 | 1.2550 | 0.6005 |
| 1.2885 | 4.9983 | 552 | 1.1483 | 0.6298 |
| 1.1226 | 5.9943 | 662 | 1.1245 | 0.6491 |
| 0.985 | 6.9994 | 773 | 1.1381 | 0.6397 |
| 0.9963 | 7.9955 | 883 | 1.0964 | 0.6605 |
| 0.88 | 8.9915 | 993 | 1.0407 | 0.6739 |
| 0.7688 | 9.9966 | 1104 | 1.0288 | 0.6918 |
| 0.763 | 10.9926 | 1214 | 0.9835 | 0.6898 |
| 0.6287 | 11.9977 | 1325 | 1.0049 | 0.7037 |
| 0.6229 | 12.9938 | 1435 | 1.1010 | 0.6918 |
| 0.5731 | 13.9989 | 1546 | 0.9910 | 0.7082 |
| 0.5076 | 14.9949 | 1656 | 1.0457 | 0.7112 |
| 0.554 | 16.0 | 1767 | 1.0141 | 0.7007 |
| 0.382 | 16.9960 | 1877 | 1.0606 | 0.6928 |
| 0.459 | 17.9921 | 1987 | 1.0091 | 0.7161 |
| 0.4018 | 18.9972 | 2098 | 1.0011 | 0.7072 |
| 0.3981 | 19.9208 | 2200 | 0.9821 | 0.7310 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|