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@@ -3,31 +3,33 @@ 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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  datasets:
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  - hard-hat-detection
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  model-index:
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  - name: yolos-tiny-Hard_Hat_Detection
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  results: []
 
 
 
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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  # yolos-tiny-Hard_Hat_Detection
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  This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the hard-hat-detection dataset.
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  ## Model description
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- More information needed
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  ## Intended uses & limitations
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- More information needed
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  ## Training and evaluation data
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- More information needed
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  ## Training procedure
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@@ -44,11 +46,24 @@ The following hyperparameters were used during training:
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  ### Training results
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-
 
 
 
 
 
 
 
 
 
 
 
 
 
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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 2.14.3
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- - Tokenizers 0.13.3
 
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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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+ - Workplace Safety
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+ - Safety
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  datasets:
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  - hard-hat-detection
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  model-index:
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  - name: yolos-tiny-Hard_Hat_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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  # yolos-tiny-Hard_Hat_Detection
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  This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the hard-hat-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/blob/main/Computer%20Vision/Object%20Detection/Hard%20Hat%20Detection/Hard_Hat_Object_Detection_YOLOS.ipynb
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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/hard-hat-detection
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  ## Training procedure
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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 | all | maxDets=100 | 0.346 |
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+ | Average Precision (AP)| IoU=0.50 | all | maxDets=100 | 0.747 |
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+ | Average Precision (AP)| IoU=0.75 | all | maxDets=100 | 0.275 |
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+ | Average Precision (AP)| IoU=0.50:0.95 | small | maxDets=100 | 0.128 |
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+ | Average Precision (AP)| IoU=0.50:0.95 | medium | maxDets=100 | 0.343 |
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+ | Average Precision (AP)| IoU=0.50:0.95 | large | maxDets=100 | 0.521 |
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+ | Average Recall (AR)| IoU=0.50:0.95 | all | maxDets=1 | 0.188 |
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+ | Average Recall (AR)| IoU=0.50:0.95 | all | maxDets=10 | 0.484 |
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+ | Average Recall (AR)| IoU=0.50:0.95 | all | maxDets=100 | 0.558 |
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+ | Average Recall (AR)| IoU=0.50:0.95 | small | maxDets=100 | 0.320 |
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+ | Average Recall (AR)| IoU=0.50:0.95 | medium | maxDets=100 | 0.538 |
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+ | Average Recall (AR)| IoU=0.50:0.95 | large | maxDets=100 | 0.743 |
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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 2.14.3
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+ - Tokenizers 0.13.3