|
--- |
|
license: apache-2.0 |
|
base_model: hustvl/yolos-small |
|
tags: |
|
- generated_from_trainer |
|
- medical |
|
- biology |
|
model-index: |
|
- name: yolos-small-Abdomen_MRI |
|
results: [] |
|
datasets: |
|
- Francesco/abdomen-mri |
|
language: |
|
- en |
|
metrics: |
|
- mean_iou |
|
pipeline_tag: object-detection |
|
--- |
|
|
|
# yolos-small-Abdomen_MRI |
|
|
|
This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small). |
|
|
|
## Model description |
|
|
|
https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Object%20Detection/Abdomen%20MRIs%20Object%20Detection/Abdomen_MRI_Object_Detection_YOLOS.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://huggingface.co/datasets/Francesco/abdomen-mri |
|
|
|
## 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: 15 |
|
|
|
### Training results |
|
|
|
|
|
| Metric Name | IoU | Area | maxDets | Value | |
|
|:-----:|:-----:|:-----:|:-----:|:-----:| |
|
| Average Precision (AP) | 0.50:0.95 | all | 100 | 0.453 | |
|
| Average Precision (AP) | 0.50 | all | 100 | 0.928 | |
|
| Average Precision (AP) | 0.75 | all | 100 | 0.319 | |
|
| Average Precision (AP) | 0.50:0.95 | small | 100 | -1.000 | |
|
| Average Precision (AP) | 0.50:0.95 | medium | 100 | 0.426 | |
|
| Average Precision (AP) | 0.50:0.95 | large | 100 | 0.457 | |
|
| Average Recall (AR) | 0.50:0.95 | all | 1 | 0.518 | |
|
| Average Recall (AR) | 0.50:0.95 | all | 10 | 0.645 | |
|
| Average Recall (AR) | 0.50:0.95 | all | 100 | 0.715 | |
|
| Average Recall (AR) | 0.50:0.95 | small | 100 | -1.000 | |
|
| Average Recall (AR) | 0.50:0.95 | medium | 100 | 0.633 | |
|
| Average Recall (AR) | 0.50:0.95 | large | 100 | 0.716 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.31.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.1 |
|
- Tokenizers 0.13.3 |
