Interested in parsing any custom object detection dataset format?
I will show you how easy to parse your dataset, and train one of many IceVision models using your own data.
In this post, I walk you through the steps to parse data stored in CSV files by using the Chess dataset
I will show you how easy to parse your dataset, and train one of many IceVision models using your own data.
In this post, I walk you through the steps to parse data stored in CSV files by using the Chess dataset
You can apply the same logic to any other format.
• First, both COCO and VOC formats are transparently supported in IceVision.
• The CSV format is different than the COCO and VOC formats. Hence, the custom parsing.
• IceVision auto-generates your parser class skeleton.
• First, both COCO and VOC formats are transparently supported in IceVision.
• The CSV format is different than the COCO and VOC formats. Hence, the custom parsing.
• IceVision auto-generates your parser class skeleton.
• You only need to connect some attributes to their corresponding ones in your CSV file
• Once done, you instantiate your class, and parse your data
• From there, all the subsequent code is common to any other parsed data.
• Parsed data are a collection of record objects
• Once done, you instantiate your class, and parse your data
• From there, all the subsequent code is common to any other parsed data.
• Parsed data are a collection of record objects
🧊 To train your model, walk through the following steps.
1- Create both train and validation Datasets: IceVision uses a random split (80% and 20%). You can use other splitters
1- Create both train and validation Datasets: IceVision uses a random split (80% and 20%). You can use other splitters
2- Pick any IceVision model
3- Create both train and validation DataLoaders
4- Find an optimal Learning Rate (optional)
5- Train your model using either Fastai or Pytorch-Lightning
6- Do a batch inference (prediction) to visually check your model performance
3- Create both train and validation DataLoaders
4- Find an optimal Learning Rate (optional)
5- Train your model using either Fastai or Pytorch-Lightning
6- Do a batch inference (prediction) to visually check your model performance
Those are the common steps: 1 to 6
Choosing one of the IceVision models
Notebook: lnkd.in/em8JGgtt
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⭐️ If you are interested in mastering Object Detection (OD), follow @ai_fast_track, and get highly curated content in your feed.
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