| | --- |
| | license: apache-2.0 |
| | base_model: hustvl/yolos-tiny |
| | tags: |
| | - generated_from_trainer |
| | - NFL |
| | - Sports |
| | - Helmets |
| | datasets: |
| | - nfl-object-detection |
| | model-index: |
| | - name: yolos-tiny-NFL_Object_Detection |
| | results: [] |
| | language: |
| | - en |
| | pipeline_tag: object-detection |
| | --- |
| | |
| | # *** This model is not completely trained!!! *** # |
| |
|
| | <hr/> |
| |
|
| | ## 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](https://huggingface.co/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 |
