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--- |
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license: apache-2.0 |
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base_model: hustvl/yolos-tiny |
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tags: |
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- generated_from_trainer |
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- NFL |
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- Sports |
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- Helmets |
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datasets: |
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- nfl-object-detection |
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model-index: |
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- name: yolos-tiny-NFL_Object_Detection |
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results: [] |
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language: |
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- en |
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pipeline_tag: object-detection |
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--- |
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# *** This model is not completely trained!!! *** # |
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<hr/> |
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## This model requires more training than what the resouces I have can offer!!! # |
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# yolos-tiny-NFL_Object_Detection |
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This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the nfl-object-detection dataset. |
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## Model description |
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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 |
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* Fine-tuning and evaluation of this model are in separate files. |
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** 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. |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://huggingface.co/datasets/keremberke/nfl-object-detection |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 18 |
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### Training results |
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| Metric Name | IoU | Area | maxDets | Metric Value | |
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|:-----:|:-----:|:-----:|:-----:|:-----:| |
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| Average Precision (AP) | IoU=0.50:0.95 | area= all | maxDets=100 | 0.003 | |
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| Average Precision (AP) | IoU=0.50 | area= all | maxDets=100 | 0.010 | |
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| Average Precision (AP) | IoU=0.75 | area= all | maxDets=100 | 0.000 | |
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| Average Precision (AP) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.002 | |
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| Average Precision (AP) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.014 | |
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| Average Precision (AP) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.000 | |
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| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets= 1 | 0.002 | |
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| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets= 10 | 0.014 | |
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| Average Recall (AR) | IoU=0.50:0.95 | area= all | maxDets=100 | 0.029 | |
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| Average Recall (AR) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.026 | |
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| Average Recall (AR) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.105 | |
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| Average Recall (AR) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.000 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.1 |
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- Tokenizers 0.13.3 |
