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README.md
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base_model: hustvl/yolos-small
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tags:
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- generated_from_trainer
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model-index:
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- name: yolos-small-Abdomen_MRI
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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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# yolos-small-Abdomen_MRI
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This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small)
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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### Training results
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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
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base_model: hustvl/yolos-small
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tags:
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- generated_from_trainer
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- medical
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- biology
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model-index:
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- name: yolos-small-Abdomen_MRI
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results: []
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datasets:
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- Francesco/abdomen-mri
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language:
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- en
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metrics:
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- mean_iou
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pipeline_tag: object-detection
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---
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# yolos-small-Abdomen_MRI
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This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small).
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## Model description
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https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Object%20Detection/Abdomen%20MRIs%20Object%20Detection/Abdomen_MRI_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/Francesco/abdomen-mri
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## Training procedure
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### Training results
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| Metric Name | IoU | Area | maxDets | Value |
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| Average Precision (AP) | 0.50:0.95 | all | 100 | 0.453 |
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| Average Precision (AP) | 0.50 | all | 100 | 0.928 |
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| Average Precision (AP) | 0.75 | all | 100 | 0.319 |
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| Average Precision (AP) | 0.50:0.95 | small | 100 | -1.000 |
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| Average Precision (AP) | 0.50:0.95 | medium | 100 | 0.426 |
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| Average Precision (AP) | 0.50:0.95 | large | 100 | 0.457 |
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| Average Recall (AR) | 0.50:0.95 | all | 1 | 0.518 |
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| Average Recall (AR) | 0.50:0.95 | all | 10 | 0.645 |
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| Average Recall (AR) | 0.50:0.95 | all | 100 | 0.715 |
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| Average Recall (AR) | 0.50:0.95 | small | 100 | -1.000 |
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| Average Recall (AR) | 0.50:0.95 | medium | 100 | 0.633 |
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| Average Recall (AR) | 0.50:0.95 | large | 100 | 0.716 |
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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
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