Classification_tf2 using deepstream python app

Please provide the following information when requesting support.

• Hardware (T4/V100/Xavier/Nano/etc)
Desktop
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc)
Classification
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
• Training spec file(If have, please share here)

results_dir: '/workspace/tao-experiments/classification_tf2/output/color_dataset_split'
dataset:
  train_dataset_path: "/workspace/tao-experiments/data/color_dataset_split/split/train"
  val_dataset_path: "/workspace/tao-experiments/data/color_dataset_split/split/val"
  preprocess_mode: 'torch'
  num_classes: 9
  augmentation:
    enable_color_augmentation: True
    enable_center_crop: True
train:
  qat: False
  checkpoint: ''
  batch_size_per_gpu: 64
  num_epochs: 120
  optim_config:
    optimizer: 'sgd'
  lr_config:
    scheduler: 'cosine'
    learning_rate: 0.05
    soft_start: 0.05
  reg_config:
    type: 'L2'
    scope: ['conv2d', 'dense']
    weight_decay: 0.00005

• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
So, I faced an issue in the deployment with python. After the training I have export it into onnx format to fit into python code. GitHub - NVIDIA-AI-IOT/deepstream_python_apps: DeepStream SDK Python bindings and sample applications This is the example I took to run my classification model.

[property]
net-scale-factor = 1

onnx-file=../../models/Secondary_ColorDetection/vehicle_cctv_dataset.onnx
model-engine-file=../../models/Secondary_ColorDetection/vehicle_cctv_dataset.onnx_b64_gpu0_fp32.engine
labelfile-path=../../models/Secondary_ColorDetection/color_label.txt

# 0=FP32 and 1=INT8 mode
batch-size=64
network-mode=0

# 1=Primary 2=Secondary
process-mode=2
gie-unique-id=7
model-color-format=0
operate-on-gie-id=1
#if need detect all the object remove it
operate-on-class-ids=0
network-type=1
num-detected-classes = 9
infer-dims=3;256;256
classifier-threshold = 0.8
is-classifier=1
output-blob-names=predictions/Softmax

This is my config file for color classification.

The color detection in python app is not accurate. I used the same images in the videos and inference it using

tao model classification_tf2 inference

It shows the result I want, which it has a different result compare to the python code.

6 posts - 2 participants

Read full topic