Changes since last release:

• Fix VGG imagenet download.
• Add numpy as dependency.
• Convert generators to Keras Sequences.
• Float16 support.
• Expose learning rate parameter.
• Add validation loss as optional step.

Changes since last release

• Evaluation uses progressbar
• Correct initialization of weights for classification submodel
• Fix issue with evaluating when there are gaps in classes
• Add configuration (currently only for anchor settings)
• Refactor how annotation are generated in the generators
• Use CPU to convert model
• Update to keras 2.2.4
• Add NCHW support

Credits to
@adreo00
@borakrc
@yecharlie
@ddowling
@enricoliscio
@hgaiser
@baek-jinoo
@de-vri-es
@penguinmenac3
Morten Back Nielsen
@relh
@vcarpani

Changes since last release

• Optimizations for generators
• Improved documentation.
• OID Challenge 2018 support.
• Keras version bumped to 2.2.0.
• Add option for class specific filtering (NMS).
• Add flake8 for code testing.
• Merged COCO and non-COCO evaluation scripts.
• Correct image preprocessing for MobileNet and DenseNet.

Credits to:
@apacha
@hgaiser
@de-vri-es
@lvaleriu
@cclauss
@HolyGuacamole
@leonardvandriel
@PhilippMarquardt
@vcarpani

Changes since last release

• Implement DenseNet, VGG backbones.
• Add option to freeze backbone layers.
• Add logging of evaluation to tensorboard.
• Add pretty colors for 80 classes.
• Fix batch_size > 1 issues.
• Refactor model outputs (should hopefully stay like this now).
• Simplified training by splitting into "training model" and "inference model".
• Add structure for backbone specific functions (such as load_model).
• Encode regression as x1/y1/x2/y2 offsets (increases mAP to 0.350, previously 0.345).
• Use nearest upsampling method.

Credits to:
@vidosits
@cgratie
@DiegoAgher
@eduramiba
@GuillaumeErhard
@Muhannes
@hgaiser
@iver56
@jjiunlin
@srslynow
@de-vri-es
@Ori226
@pedroconceicao
@pderian
@rodrigo2019
@lvaleriu
@yhenon

Changes since last release

• Corrected FPN architecture as per paper.
• Set default image size to minimum of 800px.
• Change NMS to perform per-class NMS.
• Small correction for bbox transform.
• Add OID data generator.
• Change default NMS threshold to 0.5.
• Add MobileNet backbone.
• Add tensorboard callback.
• Add tool for debugging datasets.
• Improve speed of data augmentation methods.
• Add ability to resume training.
• Add evaluation tool for custom datasets (only computes mAP at the moment).
• Add skip_mismatch to weights loading, allows transfer learning from pretrained COCO model.

Credits to:
@awilliamson
@hgaiser
@de-vri-es
@mxvs
@wassname
@mkocabas
@lvaleriu