yolos-small-Liver_Disease

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

Model description

https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Object%20Detection/Liver%20Disease%20Object%20Detection/Liver_Disease_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/liver-disease

Example Image

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: 10

Training results

Metric Name	IoU	Area	maxDets	Metric Value
Average Precision (AP)	IoU=0.50:0.95	area= all	maxDets=100	0.254
Average Precision (AP)	IoU=0.50	area= all	maxDets=100	0.399
Average Precision (AP)	IoU=0.75	area= all	maxDets=100	0.291
Average Precision (AP)	IoU=0.50:0.95	area= small	maxDets=100	0.000
Average Precision (AP)	IoU=0.50:0.95	area=medium	maxDets=100	0.154
Average Precision (AP)	IoU=0.50:0.95	area= large	maxDets=100	0.283
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets= 1	0.147
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets= 10	0.451
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets=100	0.552
Average Recall (AR)	IoU=0.50:0.95	area= small	maxDets=100	0.000
Average Recall (AR)	IoU=0.50:0.95	area=medium	maxDets=100	0.444
Average Recall (AR)	IoU=0.50:0.95	area= large	maxDets=100	0.572

Framework versions

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