More modular!

• PyTorch 1.0: Support PyTorch 1.0 or higher.
• Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly.
• Modular: Add your own modules without pain. We abstract backbone,Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without change the code base. For example, You can add EfficientNet as backbone, just add efficient_net.py (ALREADY ADDED) and register it, specific it in the config file, It's done!
• CPU support for inference: runs on CPU in inference time.
• Smooth and enjoyable training procedure: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training CMD.
• Batched inference: can perform inference using multiple images per batch per GPU.
• Evaluating during training: eval you model every eval_step to check performance improving or not.
• Metrics Visualization: visualize metrics details in tensorboard, like AP, APl, APm and APs for COCO dataset or mAP and 20 categories' AP for VOC dataset.
• Auto download: load pre-trained weights from URL and cache it.

Bug fix release.

1.1

change torch.IntTensor to torch.LongTensor

Second Release

• PyTorch 1.0
• GPU/CPU NMS
• Multi-GPU training and inference
• Modular
• Visualization(Support Tensorboard)
• CPU support for inference
• Evaluating during training