小编ASM*_*SMS的帖子

我正在尝试在 Android 上运行 tflite 模型以进行对象检测。对于相同的，

1. 我已经成功地用我的图像集训练了模型，如下所示：

(a) 培训：

!python3 object_detection/model_main.py \
--pipeline_config_path=/content/drive/My\ Drive/Detecto\ Tutorial/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config \
--model_dir=training/

（修改配置文件以指向提到我的特定 TFrecords 的位置）

(b) 导出推理图

!python /content/drive/'My Drive'/'Detecto Tutorial'/models/research/object_detection/export_inference_graph.py \
--input_type=image_tensor \
--pipeline_config_path=/content/drive/My\ Drive/Detecto\ Tutorial/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config \
--output_directory={output_directory} \
--trained_checkpoint_prefix={last_model_path}

(c) 创建 tflite 就绪图

!python /content/drive/'My Drive'/'Detecto Tutorial'/models/research/object_detection/export_tflite_ssd_graph.py \
  --pipeline_config_path=/content/drive/My\ Drive/Detecto\ Tutorial/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config \
  --output_directory={output_directory} \
  --trained_checkpoint_prefix={last_model_path} \
  --add_postprocessing_op=true

2. 我使用图形文件中的 tflite_convert 创建了一个 tflite 模型，如下所示

   !tflite_convert
   --graph_def_file=/content/drive/My\ Drive/Detecto\ Tutorial/models/research/fine_tuned_model/tflite_graph.pb
   --output_file=/content/drive/My\ Drive/Detecto\ Tutorial/models/research/fine_tuned_model /detect3.tflite
   --output_format = TFLITE
   --input_shapes = 1,300,300,3
   --input_arrays = normalized_input_image_tensor
   --output_arrays = 'TFLite_Detection_PostProcess'， 'TFLite_Detection_PostProcess：1'， 'TFLite_Detection_PostProcess：2'， …