|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: microsoft/resnet-50 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- oxford102_flower_dataset |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: resnet-50-finetuned-oxfordflowers |
|
results: |
|
- task: |
|
name: Image Classification |
|
type: image-classification |
|
dataset: |
|
name: oxford102_flower_dataset |
|
type: oxford102_flower_dataset |
|
config: default |
|
split: validation |
|
args: default |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.8329809725158562 |
|
- name: Precision |
|
type: precision |
|
value: 0.8530722962152707 |
|
- name: Recall |
|
type: recall |
|
value: 0.8329809725158562 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8319188207666911 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# resnet-50-finetuned-oxfordflowers |
|
|
|
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the oxford102_flower_dataset dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6561 |
|
- Accuracy: 0.8330 |
|
- Precision: 0.8531 |
|
- Recall: 0.8330 |
|
- F1: 0.8319 |
|
|
|
## 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: 0.001 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 4.4813 | 1.0 | 32 | 4.1934 | 0.3176 | 0.3522 | 0.3176 | 0.2599 | |
|
| 2.6507 | 2.0 | 64 | 1.8716 | 0.5382 | 0.5792 | 0.5382 | 0.4930 | |
|
| 1.257 | 3.0 | 96 | 1.0998 | 0.7216 | 0.7663 | 0.7216 | 0.7085 | |
|
| 0.5333 | 4.0 | 128 | 0.9724 | 0.7422 | 0.7875 | 0.7422 | 0.7296 | |
|
| 0.2506 | 5.0 | 160 | 0.8243 | 0.7627 | 0.7975 | 0.7627 | 0.7566 | |
|
| 0.0689 | 6.0 | 192 | 0.7067 | 0.8147 | 0.8482 | 0.8147 | 0.8111 | |
|
| 0.0325 | 7.0 | 224 | 0.6370 | 0.8206 | 0.8428 | 0.8206 | 0.8175 | |
|
| 0.0132 | 8.0 | 256 | 0.5774 | 0.8412 | 0.8617 | 0.8412 | 0.8389 | |
|
| 0.0117 | 9.0 | 288 | 0.5469 | 0.8559 | 0.8726 | 0.8559 | 0.8542 | |
|
| 0.0066 | 10.0 | 320 | 0.5384 | 0.8608 | 0.8722 | 0.8608 | 0.8575 | |
|
| 0.0072 | 11.0 | 352 | 0.5246 | 0.8686 | 0.8783 | 0.8686 | 0.8650 | |
|
| 0.0068 | 12.0 | 384 | 0.5130 | 0.8716 | 0.8790 | 0.8716 | 0.8679 | |
|
| 0.0045 | 13.0 | 416 | 0.5038 | 0.8716 | 0.8814 | 0.8716 | 0.8691 | |
|
| 0.0025 | 14.0 | 448 | 0.5486 | 0.85 | 0.8627 | 0.85 | 0.8448 | |
|
| 0.0029 | 15.0 | 480 | 0.4992 | 0.8637 | 0.8736 | 0.8637 | 0.8619 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.47.1 |
|
- Pytorch 2.5.1+cu121 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
