Pipeline based on - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md
- ssd_mobilenet_v2_320
- ssd_mobilenet_v1_fpn_640
- ssd_mobilenet_v2_fpnlite_320
- ssd_mobilenet_v2_fpnlite_640
- ssd_resnet50_v1_fpn_320
- ssd_resnet50_v1_fpn_640
- ssd_resnet101_v1_fpn_320
- ssd_resnet101_v1_fpn_640
- ssd_resnet152_v1_fpn_320
- ssd_resnet152_v1_fpn_640
- faster_rcnn_resnet50_v1_640
- faster_rcnn_resnet50_v1_1024
- faster_rcnn_resnet101_v1_640
- faster_rcnn_resnet101_v1_1024
- faster_rcnn_resnet152_v1_640
- faster_rcnn_resnet152_v1_1024
- faster_rcnn_inception_resnet_v2_640
- faster_rcnn_inception_resnet_v2_1024
- efficientdet_d0
- efficientdet_d1
- efficientdet_d2
- efficientdet_d3
- efficientdet_d4
- efficientdet_d5
- efficientdet_d6
- efficientdet_d7
Supports
- Python 3.6
- Cuda 10.0 (Other cuda version support is experimental)
- Tensorflow 1.15
cd installation
chmod +x install_cuda10.sh && ./install_cuda10.sh
- Load Dataset
gtf.set_train_dataset(train_img_dir, train_anno_dir, class_list_file, batch_size=2, trainval_split = 0.8)
gtf.create_tfrecord(data_output_dir="data_tfrecord")
- Set Model
gtf.set_model_params(model_name="ssd_mobilenet_v2_320")
- Set Hyper Params
gtf.set_hyper_params(num_train_steps=1000, lr=0.1)
- Train
%run Monk_Object_Detection/12_tf_obj_1/lib/train.py
- Export Model
%run Monk_Object_Detection/12_tf_obj_1/lib/export.py
- Add support for Coco-Type Annotated Datasets
- Add support for VOC-Type Annotated Dataset
- Test on Kaggle and Colab
- Add validation feature & data pipeline
- Add Optimizer selection feature
- Enable Learning-Rate Scheduler Support
- Enable Layer Freezing
- Set Verbosity Levels
- Add Project management and version control support (Similar to Monk Classification)
- Add Graph Visualization Support
- Enable batch proessing at inference
- Add feature for top-k output visualization
- Add Multi-GPU training
- Auto correct missing or corrupt images - Currently skips them
- Add Experimental Data Analysis Feature