CNTK-faster-rcnn
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This project aims to help getting started with running the Faster RCNN CNTK examples, since the base CNTK images require additional setup.
A launched container will contain default images of groceries in /cntk/Examples/Image/DataSets/Grocery/grocery/ with which you can use to train and make bounding box predictions.
RUN in a bash terminal:
> ./scripts/run.sh [cpu, gpu]
Image Outputs will be available in ./Output/CustomImages when code finishes
Model will be available in ./Output/
Note: During training and testing, Unknown error and PROGRESS: 0% may be outputted to the console. This will not prevent training or testing from making progress.
git clone https://github.com/CatalystCode/CNTK-faster-rcnn.git docker build -f Dockerfile-py3-cpu .docker run -it hashfrombuild bash python train.py --tagged-images /cntk/Examples/Image/DataSets/Grocery/grocery/ --num-train 200 --num-epochs 1 python predict.py --tagged-images /cntk/Examples/Image/DataSets/Grocery/grocery/ --num-test 5 --conf-threshold 0.82
The fitted model will be stored in /cntk/Examples/Image/Detection/FasterRCNN/Output/.
Grocery prediction output should result in:
Number of rois before non-maximum suppression: 623 Number of rois after non-maximum suppression: 44 Average precision (AP) for milk = 1.0000 Average precision (AP) for joghurt = 1.0000 Average precision (AP) for tomato = 1.0000 Average precision (AP) for tabasco = 0.5000 Average precision (AP) for onion = 1.0000 Average precision (AP) for pepper = 0.8000 Average precision (AP) for orangeJuice = 1.0000 Average precision (AP) for champagne = 1.0000 Average precision (AP) for orange = 1.0000 Average precision (AP) for gerkin = 1.0000 Average precision (AP) for butter = 1.0000 Average precision (AP) for avocado = 1.0000 Average precision (AP) for eggBox = 0.7500 Average precision (AP) for mustard = 1.0000 Average precision (AP) for ketchup = 0.6667 Average precision (AP) for water = 0.5000 Mean average precision (AP) = 0.8885
The outputted test grocery images with bounding boxes will be in /cntk/Examples/Image/Detection/FasterRCNN/Output/CustomImages.
Copy the files from container to host.
docker cp <containerId>:/file/path/within/container /host/path/target
Though the model was fitted with 1 epoch, the bounding boxes that identify the grocery items are still pretty accurate.
Before using CNTK, your images must be organized in a directory structure accepted by CNTK described below.
Your custom image directory must be in the format required by CNTK. The image directory should be structured as:
.├── negative │ ├── a0vqvtsowhoubmczrq4q.jpg │ ├── avhrylgho1dg1ns6q6wb.jpg │ └── ictav2a3ahahv2pcusck.jpg ├── positive │ ├── aljrnxc0ttj07dc2riel.bboxes.labels.tsv │ ├── aljrnxc0ttj07dc2riel.bboxes.tsv │ ├── aljrnxc0ttj07dc2riel.jpg │ ├── icde4ql7u7clfv3lmani.bboxes.labels.tsv │ ├── icde4ql7u7clfv3lmani.bboxes.tsv │ └── icde4ql7u7clfv3lmani.jpg └── testImages ├── bz7mfvk1etwl0rzofewu.bboxes.labels.tsv ├── bz7mfvk1etwl0rzofewu.bboxes.tsv └── bz7mfvk1etwl0rzofewu.jpg
Make sure you have an image directory containing each of the positive, negative, and testImages directories in your docker container before running the CNTK examples.
Make sure that you have sufficient space to build your images. The 2.3-cpu-python3.5 image from microsoft/cntk is ~7 GB and the other image that results is ~6 GB.
docker build -f Dockerfile-py3-cpu .docker run --rm -it -v `pwd`:`pwd` -w `pwd` hashfrombuild bash
nvidia-docker build -f Dockerfile-py3-gpu .nvidia-docker run --rm -it -v `pwd`:`pwd` -w `pwd` hashfrombuild bash
train.py and predict.py are located in /cntk/Examples/Image/Detection. These scripts remove a lot of manual edits needed for the config files, downloading of models, and setup of annotations necessary to run the CNTK detection example.
| tag | value expected |
|---|---|
| conf-threshold | The confidence threshold used to determine when bounding boxes around detected objects are drawn. A confidence threshold of 0 will draw all bounding boxes determined by CNTK. A threshold of 1 will only draw a bounding box around the exact location you had originally drawn a bounding box, i.e. you trained and tested on the same image. Provide a float falling between 0 and 1. The default confidence theshold is 0. |
| gpu | To use gpu in either training or prediction, specify 1. Otherwise, cpu will be used. |
| model-path | The absolute path to your trained model. Defaults to /cntk/Examples/Image/Detection/FasterRCNN/Output/faster_rcnn_eval_AlexNet_e2e.model. To get a trained model, run train.py. The training script will output the directory where your trained model is stored (Usually /cntk/Examples/Image/Detection/FasterRCNN/Output/faster_rcnn_eval_AlexNet_e2e.model). |
| num-epochs | The number of epochs used to train the model. One epoch is one complete training cycle on the training set. Defaults to 20. |
| num-train | The number of training images used to train the model. |
| num-test | The number images to test. |
| tagged-images | Provide a path to the directory containing your custom images prepared for CNTK object detection. The directory should contain information formatted as described here. I recommend using the VoTT tool to create the formatted directory. |
python train.py [--gpu 1] [--num-epochs 3] --num-train 200 --tagged-images /CustomImages
After you run training, /cntk/Examples/Image/Detection/FasterRCNN/Output/ will contain one new item:
faster_rcnn_eval_AlexNet_e2e.model - trained model used as input for predictions
python predict.py [--gpu 1] --tagged-images /CustomImages --num-test 5 [--model-path /cntk/Examples/Image/Detection/FasterRCNN/Output/faster_rcnn_eval_AlexNet_e2e.model] [--conf-threshold 0.82]
After you run your predictions, /cntk/Examples/Image/Detection/FasterRCNN/Output/ will contain two new items:
CustomImages directory - contains custom images with bounding boxes drawn on detected objects
custom_images_output.json - json output of bounding boxes, confidence levels, and class names for each image
资源分类
{ "images":
{ "/Reverb/labelled-guitars/testImages/adfvzfswiuv0a1erna5k.jpg": { "class": "body", "bounding_boxes": [
{"confidence_level": "0.536132", "bounding_box": {"x1": 317, "x2": 799, "y1": 65, "y2": 493}},
{"confidence_level": "0.632784", "bounding_box": {"x1": 0, "x2": 389, "y1": 167, "y2": 507}},
{"confidence_level": "0.767789", "bounding_box": {"x1": 0, "x2": 799, "y1": 102, "y2": 595}},
{"confidence_level": "0.588904", "bounding_box": {"x1": 527, "x2": 780, "y1": 96, "y2": 579}},
{"confidence_level": "0.743675", "bounding_box": {"x1": 0, "x2": 512, "y1": 196, "y2": 718}}
]
}, "/Reverb/labelled-guitars/testImages/aayjfcuulg99o3zpctux.jpg": { "class": "body", "bounding_boxes": [
{"confidence_level": "0.872948", "bounding_box": {"x1": 79, "x2": 582, "y1": 136, "y2": 764}},
{"confidence_level": "0.629768", "bounding_box": {"x1": 158, "x2": 594, "y1": 180, "y2": 552}}
]
}, "/Reverb/labelled-guitars/testImages/caaqxk85v3izwweqvbsi.jpg": { "class": "__background__", "bounding_boxes": []
}
}
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