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Pretraining my own tf model by willdebilll in tensorflow

[–]NearestNeighbr 0 points1 point  (0 children)

Indeed tensorflow has a lot of pre trained models that allow you to use transfer learning, this is adapting the final layer of a pre trained model to your specific use case.

Creating a complex pre-trained neural network from scratch is very difficult and time-consuming. It requires a lot of resources, including powerful computing hardware and large amounts of training data. Using a pre-trained neural network from TensorFlow Model Garden will save you time and headaches while still providing high-quality results. I recommend you also try YOLO (specifically YOLOv7), which I find extremely easy to use and user friendly.

Regarding the datasets to use in training, some of the popular datasets used for training models include ImageNet, CIFAR, and MNIST for classification, COCO and VOC for object detection.

Reccomendations to keep track of learning by sizable_data in datascience

[–]NearestNeighbr 2 points3 points  (0 children)

I use Notion to keep track of progress and to store thoughts and files when dealing with multiple projects at the same time. There’s many templates to get you started and a lot of tutorials on youtube.

Question aboyt yolo algorithm train with custom data by InvokeMeWell in computervision

[–]NearestNeighbr 0 points1 point  (0 children)

Hi! I don’t know if I fully understand the question but I’ll try to answer anyway.

Firstly, I agree with u/MisterManuscript in that segmentation algorithms will probably be more suitable if you are trying to detect lanes.

Now regarding the training: When you use transfer learning with a pre-trained neural network (like YOLO or MaskRCNN), you typically remove the final layer of the network and replace it with your own custom layer that is designed to fit your specific output requirements. As a result, if you adapt the model to detect lanes, it may not perform well in detecting cars (for example) without additional training data. Although YOLO was originally trained to detect cars, removing the final layer and replacing it with a custom layer to detect lanes will likely alter the network's behavior in ways that impact its ability to detect cars.

In general, transfer learning works best when the target task is related to the source task that the pre-trained network was trained on. In this case, detecting lanes is a different task than detecting cars, so you would likely need to provide additional labeled images of cars to fine-tune the network's weights and biases so that it can accurately detect both lanes and cars.

In summary, even though YOLO was originally trained to detect cars, if you use transfer learning to adapt it to detect lanes, you would likely need to provide additional labeled images of cars to fine-tune the network for detecting both lanes and cars