Hi,
I trained resnet_18 two class classifier. I get accuracy around 98%, when I infer using tlt-infer, classification are perfect with 90% confidence. But when I converted it to etlt->engine and used it in deepstream, for same frame I get different output in deepstream which incorrectly classifies, also I tried using etlt file in deepstream, still get same incorrect result.
Hi sathiez,
Which data type did you tlt-export, int8 or fp16?
More, could you please paste the config file and the command line along with logs when you run deepsteam? Thanks.
Hi,
I am using fp32
[property] gpu-id=0 net-scale-factor=1 model-engine-file=classify.engine batch-size=2 # 0=FP32 and 1=INT8 mode network-mode=0 process-mode=2 model-color-format=0 gpu-id=0 gie-unique-id=3 operate-on-gie-id=1 operate-on-class-ids=0 output-blob-names=predictions/Softmax #offsets = 104.0;177.0;123.0 ## 0=Detector, 1=Classifier, 2=Segmentation, 100=Other network-type=1 # Enable tensor metadata output output-tensor-meta=1 Deepstream log
With tracker Now playing:video.h264 Creating LL OSD context new Deserialize yoloLayerV3 plugin: yolo_17 Deserialize yoloLayerV3 plugin: yolo_24 Running... Creating LL OSD context new There is nothing to debug using deepstream log.
Can you paste your command line too? Thanks.
Can you refer to “Integrating a Classification model” part of tlt user guide Integrating TAO Models into DeepStream — TAO Toolkit 3.22.05 documentation ?
In your config file, there is not “labelfile-path”, “input-dims”, “uff-input-blob-name”.
Hi,
I am using deepstream-infer-tensor-meta-app so I will mention labels inside .cpp file. Even after I try using “input-dims”, “uff-input-blob-name” my output remains same.
my command line
./deepstream-infer-tensor-meta-app video.h264 Can you use deepstream_test1_app to check too? Thanks.
I already tried with deepstream-test4 app I get same results.
How many frames in your h264 files? What’s the accuracy rate when you run deepstream?Do you mean you generate the h264 file with the same images as using in tlt-infer?
Hi sathiez,
Have you managed to get issue resolved? Any result can be shared?
I encountered the same problem
@neos2008,
Could you please elaborate your problem?
I follow the example notebook to train the resnet_10 two class classifier. when I infer using tlt-infer, I get accuracy around 92%. But when I converted it to etlt->trt engine using tlt-converter and used it in deepstream, for same frame I get different output in deepstream which incorrectly classifies. And I tried using etlt file in deepstream, the model can’t be converted to engine.
the ouput error log:
gstnvtracker: Loading low-level lib at /opt/nvidia/deepstream/deepstream-4.0/lib/libnvds_mot_iou.so
gstnvtracker: Optional NvMOT_RemoveStreams not implemented
gstnvtracker: Batch processing is OFF
Device Number: 0
Device name: GeForce RTX 2080 Ti
Device Version 7.5
Device Supports Optical Flow Functionality
0:00:02.658735384 11321 0x55f8fcb61100 WARN nvinfer gstnvinfer.cpp:515:gst_nvinfer_logger:<secondary_gie_0> NvDsInferContext[UID 2]:useEngineFile(): Failed to read from model engine file
0:00:02.658763326 11321 0x55f8fcb61100 INFO nvinfer gstnvinfer.cpp:519:gst_nvinfer_logger:<secondary_gie_0> NvDsInferContext[UID 2]:initialize(): Trying to create engine from model files
0:00:02.980378146 11321 0x55f8fcb61100 ERROR nvinfer gstnvinfer.cpp:511:gst_nvinfer_logger:<secondary_gie_0> NvDsInferContext[UID 2]:log(): UffParser: Output error: Output predictions/Softmax #output node name for classification not found
NvDsInferCudaEngineGetFromTltModel: Failed to parse UFF model
0:00:02.983341364 11321 0x55f8fcb61100 ERROR nvinfer gstnvinfer.cpp:511:gst_nvinfer_logger:<secondary_gie_0> NvDsInferContext[UID 2]:generateTRTModel(): Failed to create network using custom network creation function
0:00:02.983369856 11321 0x55f8fcb61100 ERROR nvinfer gstnvinfer.cpp:511:gst_nvinfer_logger:<secondary_gie_0> NvDsInferContext[UID 2]:initialize(): Failed to create engine from model files
0:00:02.983433550 11321 0x55f8fcb61100 WARN nvinfer gstnvinfer.cpp:692:gst_nvinfer_start:<secondary_gie_0> error: Failed to create NvDsInferContext instance
0:00:02.983444342 11321 0x55f8fcb61100 WARN nvinfer gstnvinfer.cpp:692:gst_nvinfer_start:<secondary_gie_0> error: Config file path: /deepstream/deepstream_sdk_v4.0.1_x86_64/sources/objectDetector_DetectNet_v2/resnet10_cls/config_cls_file.txt, NvDsInfer Error: NVDSINFER_CUSTOM_LIB_FAILED
** ERROR: main:1294: Failed to set pipeline to PAUSED
Quitting
model config:
[property]
gpu-id=0
# preprocessing parameters: These are the same for all classification models generated by TLT.
net-scale-factor=1.0
offsets=123.67;116.28;103.53
model-color-format=1
batch-size=4
# Model specific paths. These need to be updated for every classfication model.
int8-calib-file=resnet10_class/final_model_int8_cache.bin
labelfile-path=resnet10_class/labels.txt
tlt-encoded-model=resnet10_class/final_model.etlt
tlt-model-key=######### hidden
input-dims=3;224;224;0 # where c = number of channels, h = height of the model input, w = width of model input, 0: implies CHW format.
uff-input-blob-name=input_1
output-blob-names=predictions/Softmax #output node name for classification
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
# process-mode: 2 - inferences on crops from primary detector, 1 - inferences on whole frame
process-mode=2
interval=0
network-type=1 # defines that the model is a classifier.
gie-unique-id=2
operate-on-gie-id=1
operate-on-class-ids=0
classifier-threshold=0.2
is-classifier=1
classifier-async-mode=1
gie-mode=2
Please modify below
output-blob-names=predictions/Softmax #output node name for classification
to
output-blob-names=predictions/Softmax
and retry.
After I modify the model config file, the engine is successfully generated by deepstearm. But the result still is wrong.
What’s your latest problem? Can you elaborate?
I follow the example notebook to train the resnet_10 two class classifier. when I infer using tlt-infer, I get accuracy around 92%. But when I converted it to etlt->engine and used it in deepstream, for same frame I get different output in deepstream which incorrectly classifies, also I tried using etlt file in deepstream, still get same incorrect result.
@neos2008
Please paste your
- training spec
- training log
- resnet10_class/labels.txt
trainging spec
model_config { arch: "resnet", n_layers: 10 # Setting these parameters to true to match the template downloaded from NGC. use_bias: true use_batch_norm: true all_projections: true freeze_blocks: 0 freeze_blocks: 1 input_image_size: "3,224,224" } train_config { train_dataset_path: "/data/jiajia_warehouse_dataset/jt_project/split/train" val_dataset_path: "/data/jiajia_warehouse_dataset/jt_project/split/val" pretrained_model_path: "/data/tlt-streamanalytics/pretrain_model/pretrained_resnet10/resnet10.hdf5" optimizer: "sgd" batch_size_per_gpu: 64 n_epochs: 80 n_workers: 16 # regularizer reg_config { type: "L2" scope: "Conv2D,Dense" weight_decay: 0.00005 } # learning_rate lr_config { scheduler: "step" learning_rate: 0.006 #soft_start: 0.056 #annealing_points: "0.3, 0.6, 0.8" #annealing_divider: 10 step_size: 10 gamma: 0.1 } } eval_config { eval_dataset_path: "/data/jiajia_warehouse_dataset/jt_project/split/test" model_path: "/data/tlt-streamanalytics/config/jt_resnet10_cls/output/weights/resnet_080.tlt" top_k: 3 batch_size: 256 n_workers: 8 } training log
Using TensorFlow backend. 2020-06-16 17:18:52.391924: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2020-06-16 17:18:55.016624: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x616ad40 executing computations on platform CUDA. Devices: 2020-06-16 17:18:55.016666: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5 2020-06-16 17:18:55.016676: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (1): GeForce RTX 2080 Ti, Compute Capability 7.5 2020-06-16 17:18:55.016683: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (2): GeForce RTX 2080 Ti, Compute Capability 7.5 2020-06-16 17:18:55.042645: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2500020000 Hz 2020-06-16 17:18:55.047296: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x6360980 executing computations on platform Host. Devices: 2020-06-16 17:18:55.047335: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined> 2020-06-16 17:18:55.047632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545 pciBusID: 0000:05:00.0 totalMemory: 10.76GiB freeMemory: 10.60GiB 2020-06-16 17:18:55.047666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 2020-06-16 17:18:55.053634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-06-16 17:18:55.053664: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 2020-06-16 17:18:55.053675: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 2020-06-16 17:18:55.053809: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10312 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:05:00.0, compute capability: 7.5) 2020-06-16 17:18:55,063 [INFO] iva.makenet.scripts.train: Loading experiment spec at /data/tlt-streamanalytics/config/jt_resnet10_cls/specs/classification_spec.cfg. 2020-06-16 17:18:55,065 [INFO] iva.makenet.spec_handling.spec_loader: Merging specification from /data/tlt-streamanalytics/config/jt_resnet10_cls/specs/classification_spec.cfg 2020-06-16 17:18:55,197 [INFO] iva.makenet.scripts.train: Processing dataset (train): /data/jiajia_warehouse_dataset/jt_project/split/train 2020-06-16 17:18:55,318 [INFO] iva.makenet.scripts.train: Processing dataset (validation): /data/jiajia_warehouse_dataset/jt_project/split/val WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. 2020-06-16 17:18:55,329 [WARNING] tensorflow: From /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. Found 2481 images belonging to 2 classes. Found 355 images belonging to 2 classes. __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) (None, 3, 224, 224) 0 __________________________________________________________________________________________________ conv1 (Conv2D) (None, 64, 112, 112) 9472 input_1[0][0] __________________________________________________________________________________________________ bn_conv1 (BatchNormalization) (None, 64, 112, 112) 256 conv1[0][0] __________________________________________________________________________________________________ activation_1 (Activation) (None, 64, 112, 112) 0 bn_conv1[0][0] __________________________________________________________________________________________________ block_1a_conv_1 (Conv2D) (None, 64, 56, 56) 36928 activation_1[0][0] __________________________________________________________________________________________________ block_1a_bn_1 (BatchNormalizati (None, 64, 56, 56) 256 block_1a_conv_1[0][0] __________________________________________________________________________________________________ activation_2 (Activation) (None, 64, 56, 56) 0 block_1a_bn_1[0][0] __________________________________________________________________________________________________ block_1a_conv_2 (Conv2D) (None, 64, 56, 56) 36928 activation_2[0][0] __________________________________________________________________________________________________ block_1a_conv_shortcut (Conv2D) (None, 64, 56, 56) 4160 activation_1[0][0] __________________________________________________________________________________________________ block_1a_bn_2 (BatchNormalizati (None, 64, 56, 56) 256 block_1a_conv_2[0][0] __________________________________________________________________________________________________ block_1a_bn_shortcut (BatchNorm (None, 64, 56, 56) 256 block_1a_conv_shortcut[0][0] __________________________________________________________________________________________________ add_1 (Add) (None, 64, 56, 56) 0 block_1a_bn_2[0][0] block_1a_bn_shortcut[0][0] __________________________________________________________________________________________________ activation_3 (Activation) (None, 64, 56, 56) 0 add_1[0][0] __________________________________________________________________________________________________ block_2a_conv_1 (Conv2D) (None, 128, 28, 28) 73856 activation_3[0][0] __________________________________________________________________________________________________ block_2a_bn_1 (BatchNormalizati (None, 128, 28, 28) 512 block_2a_conv_1[0][0] __________________________________________________________________________________________________ activation_4 (Activation) (None, 128, 28, 28) 0 block_2a_bn_1[0][0] __________________________________________________________________________________________________ block_2a_conv_2 (Conv2D) (None, 128, 28, 28) 147584 activation_4[0][0] __________________________________________________________________________________________________ block_2a_conv_shortcut (Conv2D) (None, 128, 28, 28) 8320 activation_3[0][0] __________________________________________________________________________________________________ block_2a_bn_2 (BatchNormalizati (None, 128, 28, 28) 512 block_2a_conv_2[0][0] __________________________________________________________________________________________________ block_2a_bn_shortcut (BatchNorm (None, 128, 28, 28) 512 block_2a_conv_shortcut[0][0] __________________________________________________________________________________________________ add_2 (Add) (None, 128, 28, 28) 0 block_2a_bn_2[0][0] block_2a_bn_shortcut[0][0] __________________________________________________________________________________________________ activation_5 (Activation) (None, 128, 28, 28) 0 add_2[0][0] __________________________________________________________________________________________________ block_3a_conv_1 (Conv2D) (None, 256, 14, 14) 295168 activation_5[0][0] __________________________________________________________________________________________________ block_3a_bn_1 (BatchNormalizati (None, 256, 14, 14) 1024 block_3a_conv_1[0][0] __________________________________________________________________________________________________ activation_6 (Activation) (None, 256, 14, 14) 0 block_3a_bn_1[0][0] __________________________________________________________________________________________________ block_3a_conv_2 (Conv2D) (None, 256, 14, 14) 590080 activation_6[0][0] __________________________________________________________________________________________________ block_3a_conv_shortcut (Conv2D) (None, 256, 14, 14) 33024 activation_5[0][0] __________________________________________________________________________________________________ block_3a_bn_2 (BatchNormalizati (None, 256, 14, 14) 1024 block_3a_conv_2[0][0] __________________________________________________________________________________________________ block_3a_bn_shortcut (BatchNorm (None, 256, 14, 14) 1024 block_3a_conv_shortcut[0][0] __________________________________________________________________________________________________ add_3 (Add) (None, 256, 14, 14) 0 block_3a_bn_2[0][0] block_3a_bn_shortcut[0][0] __________________________________________________________________________________________________ activation_7 (Activation) (None, 256, 14, 14) 0 add_3[0][0] __________________________________________________________________________________________________ block_4a_conv_1 (Conv2D) (None, 512, 14, 14) 1180160 activation_7[0][0] __________________________________________________________________________________________________ block_4a_bn_1 (BatchNormalizati (None, 512, 14, 14) 2048 block_4a_conv_1[0][0] __________________________________________________________________________________________________ activation_8 (Activation) (None, 512, 14, 14) 0 block_4a_bn_1[0][0] __________________________________________________________________________________________________ block_4a_conv_2 (Conv2D) (None, 512, 14, 14) 2359808 activation_8[0][0] __________________________________________________________________________________________________ block_4a_conv_shortcut (Conv2D) (None, 512, 14, 14) 131584 activation_7[0][0] __________________________________________________________________________________________________ block_4a_bn_2 (BatchNormalizati (None, 512, 14, 14) 2048 block_4a_conv_2[0][0] __________________________________________________________________________________________________ block_4a_bn_shortcut (BatchNorm (None, 512, 14, 14) 2048 block_4a_conv_shortcut[0][0] __________________________________________________________________________________________WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. 2020-06-16 17:19:02,131 [WARNING] tensorflow: From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. 2020-06-16 17:19:08.189790: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally ________ add_4 (Add) (None, 512, 14, 14) 0 block_4a_bn_2[0][0] block_4a_bn_shortcut[0][0] __________________________________________________________________________________________________ activation_9 (Activation) (None, 512, 14, 14) 0 add_4[0][0] __________________________________________________________________________________________________ avg_pool (AveragePooling2D) (None, 512, 1, 1) 0 activation_9[0][0] __________________________________________________________________________________________________ flatten (Flatten) (None, 512) 0 avg_pool[0][0] __________________________________________________________________________________________________ predictions (Dense) (None, 2) 1026 flatten[0][0] ================================================================================================== Total params: 4,919,874 Trainable params: 4,826,498 Non-trainable params: 93,376 __________________________________________________________________________________________________ Epoch 1/80 39/39 [==============================] - 12s 300ms/step - loss: 0.7114 - acc: 0.7462 - val_loss: 0.6007 - val_acc: 0.8366 WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/horovod/tensorflow/__init__.py:91: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide. 2020-06-16 17:19:17,619 [WARNING] tensorflow: From /usr/local/lib/python2.7/dist-packages/horovod/tensorflow/__init__.py:91: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide. Epoch 2/80 39/39 [==============================] - 8s 202ms/step - loss: 0.4755 - acc: 0.8573 - val_loss: 0.5061 - val_acc: 0.8310 Epoch 3/80 39/39 [==============================] - 7s 192ms/step - loss: 0.4594 - acc: 0.8700 - val_loss: 0.4980 - val_acc: 0.8423 Epoch 4/80 39/39 [==============================] - 8s 201ms/step - loss: 0.4227 - acc: 0.8844 - val_loss: 0.5126 - val_acc: 0.8366 Epoch 5/80 39/39 [==============================] - 8s 200ms/step - loss: 0.4295 - acc: 0.8810 - val_loss: 0.4705 - val_acc: 0.8535 Epoch 6/80 39/39 [==============================] - 8s 199ms/step - loss: 0.4146 - acc: 0.8872 - val_loss: 0.4840 - val_acc: 0.8423 Epoch 7/80 39/39 [==============================] - 8s 196ms/step - loss: 0.4080 - acc: 0.8911 - val_loss: 0.5009 - val_acc: 0.8479 Epoch 8/80 39/39 [==============================] - 8s 195ms/step - loss: 0.4019 - acc: 0.8983 - val_loss: 0.4733 - val_acc: 0.8479 Epoch 9/80 39/39 [==============================] - 7s 186ms/step - loss: 0.3960 - acc: 0.8988 - val_loss: 0.5027 - val_acc: 0.8479 Epoch 10/80 39/39 [==============================] - 7s 187ms/step - loss: 0.3953 - acc: 0.8877 - val_loss: 0.4883 - val_acc: 0.8451 Epoch 11/80 39/39 [==============================] - 7s 187ms/step - loss: 0.4002 - acc: 0.8963 - val_loss: 0.5026 - val_acc: 0.8423 Epoch 12/80 39/39 [==============================] - 8s 197ms/step - loss: 0.3911 - acc: 0.9054 - val_loss: 0.4949 - val_acc: 0.8394 Epoch 13/80 39/39 [==============================] - 7s 192ms/step - loss: 0.3851 - acc: 0.8972 - val_loss: 0.4870 - val_acc: 0.8423 Epoch 14/80 39/39 [==============================] - 7s 182ms/step - loss: 0.3968 - acc: 0.8918 - val_loss: 0.4820 - val_acc: 0.8451 Epoch 15/80 39/39 [==============================] - 7s 178ms/step - loss: 0.3877 - acc: 0.8994 - val_loss: 0.4957 - val_acc: 0.8423 Epoch 16/80 39/39 [==============================] - 7s 189ms/step - loss: 0.3968 - acc: 0.8959 - val_loss: 0.4913 - val_acc: 0.8394 Epoch 17/80 39/39 [==============================] - 7s 190ms/step - loss: 0.3864 - acc: 0.8969 - val_loss: 0.4861 - val_acc: 0.8451 Epoch 18/80 39/39 [==============================] - 8s 199ms/step - loss: 0.3972 - acc: 0.8904 - val_loss: 0.4856 - val_acc: 0.8451 Epoch 19/80 39/39 [==============================] - 8s 198ms/step - loss: 0.3849 - acc: 0.8969 - val_loss: 0.4842 - val_acc: 0.8451 Epoch 20/80 39/39 [==============================] - 8s 198ms/step - loss: 0.3945 - acc: 0.8876 - val_loss: 0.4912 - val_acc: 0.8394 Epoch 21/80 39/39 [==============================] - 8s 202ms/step - loss: 0.3933 - acc: 0.8919 - val_loss: 0.4834 - val_acc: 0.8423 Epoch 22/80 39/39 [==============================] - 8s 201ms/step - loss: 0.3873 - acc: 0.9014 - val_loss: 0.4788 - val_acc: 0.8451 Epoch 23/80 39/39 [==============================] - 8s 202ms/step - loss: 0.3844 - acc: 0.9009 - val_loss: 0.4860 - val_acc: 0.8423 Epoch 24/80 39/39 [==============================] - 8s 194ms/step - loss: 0.3861 - acc: 0.8931 - val_loss: 0.4857 - val_acc: 0.8451 Epoch 25/80 39/39 [==============================] - 8s 195ms/step - loss: 0.3906 - acc: 0.8941 - val_loss: 0.4871 - val_acc: 0.8394 Epoch 26/80 39/39 [==============================] - 7s 190ms/step - loss: 0.3944 - acc: 0.8945 - val_loss: 0.4848 - val_acc: 0.8451 Epoch 27/80 39/39 [==============================] - 8s 197ms/step - loss: 0.3846 - acc: 0.9027 - val_loss: 0.4769 - val_acc: 0.8451 Epoch 28/80 39/39 [==============================] - 8s 192ms/step - loss: 0.3831 - acc: 0.8997 - val_loss: 0.4939 - val_acc: 0.8423 Epoch 29/80 39/39 [==============================] - 7s 192ms/step - loss: 0.3869 - acc: 0.8967 - val_loss: 0.4870 - val_acc: 0.8451 Epoch 30/80 39/39 [==============================] - 7s 188ms/step - loss: 0.3878 - acc: 0.8979 - val_loss: 0.4837 - val_acc: 0.8451 Epoch 31/80 39/39 [==============================] - 7s 186ms/step - loss: 0.3920 - acc: 0.8922 - val_loss: 0.4781 - val_acc: 0.8451 Epoch 32/80 39/39 [==============================] - 7s 185ms/step - loss: 0.3896 - acc: 0.8900 - val_loss: 0.4855 - val_acc: 0.8423 Epoch 33/80 39/39 [==============================] - 8s 194ms/step - loss: 0.3896 - acc: 0.9013 - val_loss: 0.4810 - val_acc: 0.8451 Epoch 34/80 39/39 [==============================] - 7s 192ms/step - loss: 0.3902 - acc: 0.8927 - val_loss: 0.4876 - val_acc: 0.8423 Epoch 35/80 39/39 [==============================] - 8s 194ms/step - loss: 0.3930 - acc: 0.8943 - val_loss: 0.4930 - val_acc: 0.8451 Epoch 36/80 39/39 [==============================] - 8s 194ms/step - loss: 0.3874 - acc: 0.9020 - val_loss: 0.4877 - val_acc: 0.8394 Epoch 37/80 39/39 [==============================] - 8s 198ms/step - loss: 0.3921 - acc: 0.8957 - val_loss: 0.4803 - val_acc: 0.8451 Epoch 38/80 39/39 [==============================] - 7s 189ms/step - loss: 0.3947 - acc: 0.8920 - val_loss: 0.4840 - val_acc: 0.8451 Epoch 39/80 39/39 [==============================] - 7s 184ms/step - loss: 0.3963 - acc: 0.8960 - val_loss: 0.4905 - val_acc: 0.8394 Epoch 40/80 39/39 [==============================] - 7s 176ms/step - loss: 0.3853 - acc: 0.9009 - val_loss: 0.4897 - val_acc: 0.8423 Epoch 41/80 39/39 [==============================] - 6s 150ms/step - loss: 0.3919 - acc: 0.8959 - val_loss: 0.4897 - val_acc: 0.8394 Epoch 42/80 39/39 [==============================] - 8s 195ms/step - loss: 0.3930 - acc: 0.8894 - val_loss: 0.4906 - val_acc: 0.8394 Epoch 43/80 39/39 [==============================] - 8s 195ms/step - loss: 0.3926 - acc: 0.8947 - val_loss: 0.4787 - val_acc: 0.8451 Epoch 44/80 39/39 [==============================] - 8s 195ms/step - loss: 0.3968 - acc: 0.8908 - val_loss: 0.4873 - val_acc: 0.8451 Epoch 45/80 39/39 [==============================] - 7s 190ms/step - loss: 0.3907 - acc: 0.8900 - val_loss: 0.4878 - val_acc: 0.8451 Epoch 46/80 39/39 [==============================] - 7s 186ms/step - loss: 0.3974 - acc: 0.8986 - val_loss: 0.4797 - val_acc: 0.8451 Epoch 47/80 39/39 [==============================] - 7s 188ms/step - loss: 0.3960 - acc: 0.8877 - val_loss: 0.4926 - val_acc: 0.8423 Epoch 48/80 39/39 [==============================] - 7s 188ms/step - loss: 0.3854 - acc: 0.8993 - val_loss: 0.4903 - val_acc: 0.8423 Epoch 49/80 39/39 [==============================] - 7s 189ms/step - loss: 0.3883 - acc: 0.8976 - val_loss: 0.4825 - val_acc: 0.8451 Epoch 50/80 39/39 [==============================] - 7s 186ms/step - loss: 0.3848 - acc: 0.9090 - val_loss: 0.4873 - val_acc: 0.8394 Epoch 51/80 39/39 [==============================] - 8s 194ms/step - loss: 0.3866 - acc: 0.8984 - val_loss: 0.4934 - val_acc: 0.8423 Epoch 52/80 39/39 [==============================] - 7s 189ms/step - loss: 0.3929 - acc: 0.8969 - val_loss: 0.4906 - val_acc: 0.8394 Epoch 53/80 39/39 [==============================] - 7s 192ms/step - loss: 0.3924 - acc: 0.8913 - val_loss: 0.4883 - val_acc: 0.8394 Epoch 54/80 39/39 [==============================] - 7s 189ms/step - loss: 0.3888 - acc: 0.9011 - val_loss: 0.4947 - val_acc: 0.8451 Epoch 55/80 39/39 [==============================] - 8s 194ms/step - loss: 0.3938 - acc: 0.9030 - val_loss: 0.4885 - val_acc: 0.8423 Epoch 56/80 39/39 [==============================] - 8s 196ms/step - loss: 0.3858 - acc: 0.9003 - val_loss: 0.4866 - val_acc: 0.8451 Epoch 57/80 39/39 [==============================] - 8s 193ms/step - loss: 0.3917 - acc: 0.9005 - val_loss: 0.4904 - val_acc: 0.8423 Epoch 58/80 39/39 [==============================] - 7s 187ms/step - loss: 0.3822 - acc: 0.8985 - val_loss: 0.4835 - val_acc: 0.8423 Epoch 59/80 39/39 [==============================] - 8s 199ms/step - loss: 0.3852 - acc: 0.9022 - val_loss: 0.4861 - val_acc: 0.8423 Epoch 60/80 39/39 [==============================] - 8s 196ms/step - loss: 0.3916 - acc: 0.8926 - val_loss: 0.4782 - val_acc: 0.8451 Epoch 61/80 39/39 [==============================] - 8s 193ms/step - loss: 0.3899 - acc: 0.9013 - val_loss: 0.4754 - val_acc: 0.8451 Epoch 62/80 39/39 [==============================] - 7s 186ms/step - loss: 0.4002 - acc: 0.8879 - val_loss: 0.4911 - val_acc: 0.8394 Epoch 63/80 39/39 [==============================] - 7s 183ms/step - loss: 0.3851 - acc: 0.8980 - val_loss: 0.4844 - val_acc: 0.8423 Epoch 64/80 39/39 [==============================] - 7s 191ms/step - loss: 0.3951 - acc: 0.8984 - val_loss: 0.4880 - val_acc: 0.8423 Epoch 65/80 39/39 [==============================] - 8s 195ms/step - loss: 0.3876 - acc: 0.9012 - val_loss: 0.4876 - val_acc: 0.8423 Epoch 66/80 39/39 [==============================] - 8s 197ms/step - loss: 0.3893 - acc: 0.8964 - val_loss: 0.4903 - val_acc: 0.8423 Epoch 67/80 39/39 [==============================] - 7s 178ms/step - loss: 0.3933 - acc: 0.8945 - val_loss: 0.4848 - val_acc: 0.8423 Epoch 68/80 39/39 [==============================] - 5s 137ms/step - loss: 0.3913 - acc: 0.8933 - val_loss: 0.4830 - val_acc: 0.8451 Epoch 69/80 39/39 [==============================] - 5s 136ms/step - loss: 0.3833 - acc: 0.8921 - val_loss: 0.4860 - val_acc: 0.8423 Epoch 70/80 39/39 [==============================] - 6s 144ms/step - loss: 0.3895 - acc: 0.8967 - val_loss: 0.4857 - val_acc: 0.8423 Epoch 71/80 39/39 [==============================] - 5s 137ms/step - loss: 0.3945 - acc: 0.8906 - val_loss: 0.4910 - val_acc: 0.8423 Epoch 72/80 39/39 [==============================] - 5s 140ms/step - loss: 0.3840 - acc: 0.9029 - val_loss: 0.4822 - val_acc: 0.8451 Epoch 73/80 39/39 [==============================] - 6s 154ms/step - loss: 0.3908 - acc: 0.8995 - val_loss: 0.4809 - val_acc: 0.8451 Epoch 74/80 39/39 [==============================] - 7s 174ms/step - loss: 0.3971 - acc: 0.8932 - val_loss: 0.4855 - val_acc: 0.8423 Epoch 75/80 39/39 [==============================] - 5s 140ms/step - loss: 0.3947 - acc: 0.8951 - val_loss: 0.4864 - val_acc: 0.8394 Epoch 76/80 39/39 [==============================] - 6s 148ms/step - loss: 0.3899 - acc: 0.8963 - val_loss: 0.4879 - val_acc: 0.8423 Epoch 77/80 39/39 [==============================] - 6s 143ms/step - loss: 0.3915 - acc: 0.9019 - val_loss: 0.4866 - val_acc: 0.8423 Epoch 78/80 39/39 [==============================] - 5s 140ms/step - loss: 0.3886 - acc: 0.8947 - val_loss: 0.4810 - val_acc: 0.8451 Epoch 79/80 39/39 [==============================] - 6s 156ms/step - loss: 0.3946 - acc: 0.8977 - val_loss: 0.4832 - val_acc: 0.8423 Epoch 80/80 39/39 [==============================] - 5s 139ms/step - loss: 0.3949 - acc: 0.8934 - val_loss: 0.4809 - val_acc: 0.8451 2020-06-16 17:30:05,392 [INFO] iva.makenet.scripts.train: Total Val Loss: 0.486088007689 2020-06-16 17:30:05,393 [INFO] iva.makenet.scripts.train: Total Val accuracy: 0.842253506184 2020-06-16 17:30:05,393 [INFO] iva.makenet.scripts.train: Training finished successfully. resnet10_class/labels.txt
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How about the accuracy when run inference with Deepstream?
You mentioned “incorrect result”, does it mean all frames are wrong?