yolos-tiny-Brain_Tumor_Detection

This model is a fine-tuned version of hustvl/yolos-tiny.

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/Computer%20Vision/Object%20Detection/Brain%20Tumors/Brain_Tumor_m2pbp_Object_Detection_YOLOS.ipynb

**If you intend on trying this project yourself, I highly recommend using (at least) the yolos-small checkpoint.

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/brain-tumor-m2pbp

Example

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: 20

Training results

Metric Name	IoU	Area	maxDets	Metric Value
Average Precision (AP)	IoU=0.50:0.95	area= all	maxDets=100	0.185
Average Precision (AP)	IoU=0.50	area= all	maxDets=100	0.448
Average Precision (AP)	IoU=0.75	area= all	maxDets=100	0.126
Average Precision (AP)	IoU=0.50:0.95	area= small	maxDets=100	0.001
Average Precision (AP)	IoU=0.50:0.95	area=medium	maxDets=100	0.080
Average Precision (AP)	IoU=0.50:0.95	area= large	maxDets=100	0.296
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets= 1	0.254
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets= 10	0.353
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets=100	0.407
Average Recall (AR)	IoU=0.50:0.95	area= small	maxDets=100	0.036
Average Recall (AR)	IoU=0.50:0.95	area=medium	maxDets=100	0.312
Average Recall (AR)	IoU=0.50:0.95	area= large	maxDets=100	0.565

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.2
• Tokenizers 0.13.3