I have an image dataset that has a bounding box for the objects being identified. Some images have multiple bounding boxes with different classifications.
Perceptilabs does not seem to handle/utilize csv files with bounding boxes. Can the bounding boxes be imported and used for the classification?
Regards,
Cecil
Hi @Kingseso,
Welcome to the forum!
We unfortunately don’t handle bounding boxes yet as you said, we are currently limited to image classification, segmentation and some regression tasks.
Object Detection is something we are looking at adding in sooner rather than later though to complete the Computer Vision trifecta.
Is there any specific Object Detection model/method you were interested in running with?
All the best,
Robert
Hello Robert,
I ultimately used Yolov5 for object detection, but I do have a second question.
I was able to use PL for image recognition using some of the examples from the blog.
I am running into an issue of generating probabilities from the exported model. I used the following code in python:
model = keras.models.load_model(path_to_model)
im = Image.open(test_image_path + test_df[‘File Name’][i])
im = np.array(im, dtype=np.float32)
im= np.expand_dims(im, axis=0)
prediction1 = model.predict(im)
model.predict only provides the class. I would like the probability of the selected identified class. Maybe you have some recommendations.
Regards,
Cecil
Hello Robert,
Never mind about the probability. I realized I needed the confidence from my bounding box, so I was able to get that else where.
Keep up the good work. PL made model making quick and clean.
Cecil
Hi @Kingseso
If it’s no too much trouble, you would mind explaining how you get the confidence? The statistical bits of ML are - for me - the hardest to get to grips with, so I find new examples very helpful.
Thx 