DunnBC22/yolos-tiny-NFL_Object_Detection

*** This model is not completely trained!!! ***

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This model requires more training than what the resouces I have can offer!!!

yolos-tiny-NFL_Object_Detection

This model is a fine-tuned version of hustvl/yolos-tiny on the nfl-object-detection dataset.

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/tree/main/Computer%20Vision/Object%20Detection/Trained%2C%20But%20to%20Standard/NFL%20Object%20Detection/Successful%20Attempt

• Fine-tuning and evaluation of this model are in separate files.

** If you plan on fine-tuning an Object Detection model on the NFL Helmet detection dataset, I would 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/keremberke/nfl-object-detection

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: 18

Training results

Metric Name	IoU	Area	maxDets	Metric Value
Average Precision (AP)	IoU=0.50:0.95	area= all	maxDets=100	0.003
Average Precision (AP)	IoU=0.50	area= all	maxDets=100	0.010
Average Precision (AP)	IoU=0.75	area= all	maxDets=100	0.000
Average Precision (AP)	IoU=0.50:0.95	area= small	maxDets=100	0.002
Average Precision (AP)	IoU=0.50:0.95	area=medium	maxDets=100	0.014
Average Precision (AP)	IoU=0.50:0.95	area= large	maxDets=100	0.000
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets= 1	0.002
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets= 10	0.014
Average Recall (AR)	IoU=0.50:0.95	area= all	maxDets=100	0.029
Average Recall (AR)	IoU=0.50:0.95	area= small	maxDets=100	0.026
Average Recall (AR)	IoU=0.50:0.95	area=medium	maxDets=100	0.105
Average Recall (AR)	IoU=0.50:0.95	area= large	maxDets=100	0.000

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.1
• Tokenizers 0.13.3