toniz / deeplab-on-ios Goto Github PK

@toniz
Hi, I have trained deep lab on my custom dataset(50*50,3400 images) for iris eyes object with 257 as crop size and during the test, it detects for crop with crop size 257 .
i tested the pb model with code python it detects ok

now what I need is to integrate my model on ios application, i was able to successfully convert the model to tflite .but it gives me a slow segmentation
tflite_convert ----output_format=TFLITE --inference_type=FLOAT --inference_input_type=FlOAT --input_arrays=sub_2 --input_shapes=1,257,257,3 --output_arrays=ResizeBilinear_2 --output_file=/Users/hak/Downloads/deeplabv3_mnv2_pascal_trainvall/mobilenet.tflite --graph_def=/Users/hak/Downloads/deeplabv3_mnv2_pascal_trainvall/mobilenet.pb --mean_values=128 --std_dev_values=127 --allow_custom_ops --post_training_quantize
tflite file size=2,2mo
however the original deeplab tflite file works fine on the appliacation
i have changed fps to 60 and to 240 but nothing change
i use iphone6+

@toniz
Hi ,
if you don't mind I want to ask you for the source code of your github repository

@toniz
thnx for the project, it was really helpful for me, i have tried to put my retrained model on the code but I got an error if you don't mind can you check it with me??

error > EXC_BAD_ACCESS

i have trained deeplab model on new object and its work with detection
i was able to successfully convert the model with the following command:
toco --output_file=model.tflite --input_file=prozen_inference_graph.pb --input_arrays=ImageTensor --output_arrays=SemanticPredictions --input_shapes=1,513,513,3 --inference_input_type=QUANTIZED_UINT8 --inference_type=FLOAT --mean_values=128 --std_dev_values=127 --allow_custom_ops --post_training_quantize

@toniz
Hi, I have trained deep lab on my custom dataset(400*300,14000 images) for iris eyes object with 513 as crop size and during the test, it detects for crop with crop size 513 .
i tested the pb model with code python it detects ok(not precise very much but it ok)
now what I need i integrate my model on ios application, i was able to successfully convert the model to tflite .but it gives me a wrong and bad segmentation
python train.py \ --logtostderr \ --train_split="trainval" \ --model_variant="mobilenet_v2" \ --output_stride=16 \ --train_crop_size="513,513" \ --train_batch_size=4 \ --training_number_of_steps=20000 \ --fine_tune_batch_norm=true \ --tf_initial_checkpoint="${INIT_FOLDER}/${CKPT_NAME}/model.ckpt-30000" \ --train_logdir="${TRAIN_LOGDIR}" \ --dataset_dir="${my_DATASET}"
####convert to tflite
tflite_convert ----output_format=TFLITE --inference_type=QUANTIZED_UINT8 --inference_input_type=FlOAT --input_arrays=sub_2 --input_shapes=1,513,513,3 --output_arrays=ResizeBilinear_2 --output_file=/Users/hak/Downloads/deeplabv3_mnv2_pascal_trainvall/mobilenet1-20000.tflite --graph_def=/Users/hak/Downloads/deeplabv3_mnv2_pascal_trainvall/mobilenet-20000.pb --mean_values=128 --std_dev_values=127 --allow_custom_ops --post_training_quantize
did you have any idea why mobilenet gives me such bad result is't the training steps no enough for 14000 images or what ??

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