đ yolos-tiny-NFL_Object_Detection
This model is a fine - tuned version of hustvl/yolos-tiny on the nfl - object - detection dataset. It aims to demonstrate the ability to solve complex problems using technology.
đ Quick Start
This model requires more training than the resources the author has can offer. And this model is not completely trained!!!
⨠Features
- It is a fine - tuned version of hustvl/yolos-tiny on the nfl - object - detection dataset.
- Fine - tuning and evaluation of this model are in separate files.
đ Documentation
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
** If you plan on fine - tuning an Object Detection model on the NFL Helmet detection dataset, it is recommended to use (at least) the Yolos - small checkpoint.
Intended uses & limitations
This model is intended to demonstrate the author's 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
| Property |
Details |
| 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
đ License
This model is licensed under the Apache - 2.0 license.
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