Detecting cells and nuclei from different statinings with Faster R-CNN and Mask R-CNN in PyTorch 1.0
This project aims at providing a pipeline for efficient nuclei and cell detection from fluorescence images. It is based on facebookresearch maskrcnn benchmark, which is implemented in PyTorch 1.0. More information can be found at https://github.com/facebookresearch/maskrcnn-benchmark.
Here the labeled image.
- PyTorch 1.0: RPN, Faster R-CNN and Mask R-CNN implementations that matches or exceeds Detectron accuracies
- Very fast: up to 2x faster than Detectron and 30% faster than mmdetection during training. See MODEL_ZOO.md for more details.
- Memory efficient: uses roughly 500MB less GPU memory than mmdetection during training
- Multi-GPU training and inference
- Batched inference: can perform inference using multiple images per batch per GPU
- CPU support for inference: runs on CPU in inference time. See our webcam demo for an example
- Provides pre-trained models for almost all reference Mask R-CNN and Faster R-CNN configurations with 1x schedule.
Check INSTALL.md for installation instructions.
The tutorial (Tutorial_training.md) explains all the features including training, inference and prediction.
For more information on some of the main abstractions in our implementation, see ABSTRACTIONS.md.