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I am training a pre built tensorflow based model for custom object detection. I want to detect only 1 type of object. I have taken lot of images from different angles and in different light conditions. I am training on K80 Nvidia GPU. Everything is working and when I train I can see the loss function falling to 0.3. But the loss values drops very quickly to under 1 when I start training. I am using SSD mobile Net as the base configuration for the model. When I try to test the model, it just draws a big square on the input image, rather than detecting the desired object in the image. Basically, it fails to detect the object.

I tried to train the model with a different set of images of mac n chesse which had lot of variations. Then the model worked fine and detected images of mac n chesse in the input image. But when I have pictures of single object then the model fails to detect. Please help me understand what I am doing wrong here

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The issue was with my training dataset. I was not properly cropping the object from the original image. Also I needed around 300 images to properly train the model. SSD worked well after giving a well cropped images.

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