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module.yml
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name: nvidia_car_classification
parameters:
output_frame:
codec: ${oc.env:CODEC, 'raw-rgba'}
draw_func:
module: samples.nvidia_car_classification.overlay
# specify the drawfunc's python class from the module
class_name: Overlay
rendered_objects:
Primary_Detector:
Car:
bbox:
border_color: '00FF00FF' # Green
background_color: '00000000' # transparent
thickness: 2
label:
font_color: 'FFFFFFFF' # White
border_color: '00000000' # transparent
background_color: '000000FF' # Black
font_scale: 0.5
thickness: 1
# Note that the label format for Primary_Detector.Car objects
# is overriden on a per object basis:
# every car has the classifier result labels added on separate lines
format: [ '{label} #{track_id}' ]
position:
# position options are TopLeftInside / TopLeftOutside / Center
position: TopLeftInside
margin_x: 0
margin_y: 0
Person:
bbox:
border_color: '0000FFFF' # Blue
thickness: 2
batch_size: 1
pipeline:
elements:
# detector
- element: nvinfer@detector
name: Primary_Detector
model:
format: caffe
remote:
url: s3://savant-data/models/Primary_Detector/Primary_Detector.zip
checksum_url: s3://savant-data/models/Primary_Detector/Primary_Detector.md5
parameters:
endpoint: https://eu-central-1.linodeobjects.com
model_file: resnet10.caffemodel
batch_size: ${parameters.batch_size}
precision: int8
int8_calib_file: cal_trt.bin
label_file: labels.txt
scaling_compute_hw: AUTO
scaling_filter: AUTO
input:
scale_factor: 0.0039215697906911373
output:
num_detected_classes: 4
layer_names: [ conv2d_bbox, conv2d_cov/Sigmoid ]
objects:
- class_id: 0
label: Car
- class_id: 2
label: Person
# tracker
- element: nvtracker
properties:
ll-lib-file: /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so
ll-config-file: ${oc.env:PROJECT_PATH}/samples/assets/tracker/config_tracker_NvDCF_perf.yml
tracker-width: 640
tracker-height: 384
display-tracking-id: 0
# Car Color classifier
- element: nvinfer@classifier
name: Secondary_CarColor
model:
format: caffe
remote:
url: s3://savant-data/models/Secondary_CarColor/Secondary_CarColor.zip
checksum_url: s3://savant-data/models/Secondary_CarColor/Secondary_CarColor.md5
parameters:
endpoint: https://eu-central-1.linodeobjects.com
model_file: resnet18.caffemodel
mean_file: mean.ppm
label_file: labels.txt
precision: int8
int8_calib_file: cal_trt.bin
batch_size: 16
input:
object: Primary_Detector.Car
object_min_width: 64
object_min_height: 64
color_format: bgr
output:
layer_names: [ predictions/Softmax ]
attributes:
- name: car_color
threshold: 0.51
# Car Make classifier
- element: nvinfer@classifier
name: Secondary_CarMake
model:
format: caffe
remote:
url: s3://savant-data/models/Secondary_CarMake/Secondary_CarMake.zip
checksum_url: s3://savant-data/models/Secondary_CarMake/Secondary_CarMake.md5
parameters:
endpoint: https://eu-central-1.linodeobjects.com
model_file: resnet18.caffemodel
mean_file: mean.ppm
label_file: labels.txt
precision: int8
int8_calib_file: cal_trt.bin
batch_size: 16
input:
object: Primary_Detector.Car
object_min_width: 64
object_min_height: 64
color_format: bgr
output:
layer_names: [ predictions/Softmax ]
attributes:
- name: car_make
threshold: 0.51
# Car Type classifier
- element: nvinfer@classifier
name: Secondary_VehicleTypes
model:
format: caffe
remote:
url: s3://savant-data/models/Secondary_VehicleTypes/Secondary_VehicleTypes.zip
checksum_url: s3://savant-data/models/Secondary_VehicleTypes/Secondary_VehicleTypes.md5
parameters:
endpoint: https://eu-central-1.linodeobjects.com
model_file: resnet18.caffemodel
mean_file: mean.ppm
label_file: labels.txt
precision: int8
int8_calib_file: cal_trt.bin
batch_size: 16
input:
object: Primary_Detector.Car
object_min_width: 64
object_min_height: 64
color_format: bgr
output:
layer_names: [ predictions/Softmax ]
attributes:
- name: car_type
threshold: 0.51