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open-mmlab / mmdetection

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Impossible to correctly retrieve metrics and to extend the codebase

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FrancescoSaverioZuppichini
FrancescoSaverioZuppichini commented Sep 28, 2021

Dear all,

I hope to find you well. The current codebase is totally closed and it is impossible to retrieve all the metrics computed. Allow all metrics to be returned inside dataset.evaluate. For instance, currently, we cannot get the map per class that is printed inside [eval_map](https://github.com/open-mmlab/mmdetection/blob/414c62cb12d1b0ebf6151a697a32b84adc269ca4/mmdet/core/evaluati

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good first issue
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MIC-DKFZ / medicaldetectiontoolkit

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The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

deep-neural-networks deep-learning detection inference medical-imaging segmentation object-detection medical-image-computing semantic-segmentation 3d-models medical-image-processing mask-rcnn u-net medical-image-analysis 2d-3d pytorch-cnn retina-net 3d-object-detection pytorch-implementation pytorch-deeplearning retina-unet 3d-mask-rcnn
  • Updated Sep 9, 2021
  • Python

airctic / icevision

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Get bounding boxes from Prediction

FrancescoSaverioZuppichini
FrancescoSaverioZuppichini commented Oct 12, 2021

Currently, it seems impossible to just get a list of bounding boxes from the predictions.

img = np.array(Image.open("..."))
infer_ds = Dataset.from_images([img], valid_tfms, class_map=dm.parser.class_map)

preds = model_type.predict(model, infer_ds, keep_images=True)
# I just want the bounding boxes

preds is a list of Prediction and the codebase is not easy to navi

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documentation enhancement good first issue
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