TNC_FRCNN

Faster-RCNN for The Nature Conservancy Wildlife protection project

Setup

1. Python 3.5 under Windows and Python 3.5/3.6 under Linux

2. CNTK 2.6 Python environment CPU-Only:

pip install https://cntk.ai/PythonWheel/CPU-Only/cntk-2.6-cp35-cp35m-win_amd64.whl

or, GPU:

pip install https://cntk.ai/PythonWheel/GPU/cntk_gpu-2.6-cp35-cp35m-win_amd64.whl

3. Install the following additional packages:

pip install opencv-python easydict pyyaml future

Quick start

1. If you just want to play with single image prediction Download trained (with 10 classes) model from \shpeng440\TNC_RawData\BU\FasterRCNN_Model Copy the model (faster_rcnn_eval_AlexNet_e2e.model) to TNC_FRCNN\Detection\FasterRCNN\Output folder

2. If you want to train your own model Go to PretrainedModels folder to download AlexNet_ImageNet_Caffe.model Install VoTT - https://github.com/Microsoft/VoTT#installation Go to DataPrep 2.1 create json for VoTT 2.2 review in VoTT 2.3 export to Faster_RCNN 2.4 create training/test data Go to \Detection\FasterRCNN to train your model Rename Output\faster_rcnn_eval_AlexNet_e2e.model or move to other folder Delete Output\faster_rcnn_eval_AlexNet_e2e.model run 'python run_faster_rcnn.py'

3. Detect single image Go to \Detection

   python DetectImg.py <image_full_path.jpg>
   

Sub-folder content

Detection

Faster-RCNN training, evaluation and demo.

DataPrep

Scripts for data preparation.

Pretrained Model

The base model for F-RCNN training

DataSets

Contains groups of test/training set. default - BU (data from Beijing University)

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