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Faster-RCNN finetuning with PyTorch. Object detection using PyTorch. Custom dataset. Wheat detection

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In this video, we are going to see how can we fine tune a pretrained faster-rcnn model using PyTorch. The dataset we will be using is the wheat detection dataset.
The video has three objectives:
1. Explain the main portion of the code for fine tuning faster-rcnn.
2. How does the faster-rcnn finetuning look like and what layers are affected as a result?
3. See the whole code for fine tuning faster-rcnn.
We will see how the last layer of the faster-rcnn module is affected due to fine tuning. We also see how the Non Max Suppression (NMS) also works in the context of fine tuning faster-rcnn.
The main code for faster-rcnn is:
num_classes = 2
The video has three objectives:
1. Explain the main portion of the code for fine tuning faster-rcnn.
2. How does the faster-rcnn finetuning look like and what layers are affected as a result?
3. See the whole code for fine tuning faster-rcnn.
We will see how the last layer of the faster-rcnn module is affected due to fine tuning. We also see how the Non Max Suppression (NMS) also works in the context of fine tuning faster-rcnn.
The main code for faster-rcnn is:
num_classes = 2
Faster-RCNN finetuning with PyTorch. Object detection using PyTorch. Custom dataset. Wheat detection
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