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riding_horse.png: detected 0 instances in 0.10s

See original GitHub issue

Hi everyone,

I followed the instructions in here for the installation. Things went well. But then I tried running the first demo. The model did not detect any object.

I have made no change to the code.

Instructions To Reproduce the Issue:

pyenv local 3.7.6
poetry init
poetry shell
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
cd detectron2/demo/
python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml   --input  ~/Downloads/riding_horse.png

And this is the Full logs of what I got

[08/25 10:53:42 detectron2]: Arguments: Namespace(confidence_threshold=0.5, config_file='../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml', input=['/home/tai/Downloads/riding_horse.png'], opts=[], output=None, video_input=None, webcam=False)
[08/25 10:53:44 fvcore.common.checkpoint]: [Checkpointer] Loading from detectron2://ImageNetPretrained/MSRA/R-50.pkl ...
[08/25 10:53:44 d2.checkpoint.c2_model_loading]: Renaming Caffe2 weights ......
[08/25 10:53:44 d2.checkpoint.c2_model_loading]: Following weights matched with submodule backbone.bottom_up:
| Names in Model    | Names in Checkpoint      | Shapes                                          |
|:------------------|:-------------------------|:------------------------------------------------|
| res2.0.conv1.*    | res2_0_branch2a_{bn_*,w} | (64,) (64,) (64,) (64,) (64,64,1,1)             |
| res2.0.conv2.*    | res2_0_branch2b_{bn_*,w} | (64,) (64,) (64,) (64,) (64,64,3,3)             |
| res2.0.conv3.*    | res2_0_branch2c_{bn_*,w} | (256,) (256,) (256,) (256,) (256,64,1,1)        |
| res2.0.shortcut.* | res2_0_branch1_{bn_*,w}  | (256,) (256,) (256,) (256,) (256,64,1,1)        |
| res2.1.conv1.*    | res2_1_branch2a_{bn_*,w} | (64,) (64,) (64,) (64,) (64,256,1,1)            |
| res2.1.conv2.*    | res2_1_branch2b_{bn_*,w} | (64,) (64,) (64,) (64,) (64,64,3,3)             |
| res2.1.conv3.*    | res2_1_branch2c_{bn_*,w} | (256,) (256,) (256,) (256,) (256,64,1,1)        |
| res2.2.conv1.*    | res2_2_branch2a_{bn_*,w} | (64,) (64,) (64,) (64,) (64,256,1,1)            |
| res2.2.conv2.*    | res2_2_branch2b_{bn_*,w} | (64,) (64,) (64,) (64,) (64,64,3,3)             |
| res2.2.conv3.*    | res2_2_branch2c_{bn_*,w} | (256,) (256,) (256,) (256,) (256,64,1,1)        |
| res3.0.conv1.*    | res3_0_branch2a_{bn_*,w} | (128,) (128,) (128,) (128,) (128,256,1,1)       |
| res3.0.conv2.*    | res3_0_branch2b_{bn_*,w} | (128,) (128,) (128,) (128,) (128,128,3,3)       |
| res3.0.conv3.*    | res3_0_branch2c_{bn_*,w} | (512,) (512,) (512,) (512,) (512,128,1,1)       |
| res3.0.shortcut.* | res3_0_branch1_{bn_*,w}  | (512,) (512,) (512,) (512,) (512,256,1,1)       |
| res3.1.conv1.*    | res3_1_branch2a_{bn_*,w} | (128,) (128,) (128,) (128,) (128,512,1,1)       |
| res3.1.conv2.*    | res3_1_branch2b_{bn_*,w} | (128,) (128,) (128,) (128,) (128,128,3,3)       |
| res3.1.conv3.*    | res3_1_branch2c_{bn_*,w} | (512,) (512,) (512,) (512,) (512,128,1,1)       |
| res3.2.conv1.*    | res3_2_branch2a_{bn_*,w} | (128,) (128,) (128,) (128,) (128,512,1,1)       |
| res3.2.conv2.*    | res3_2_branch2b_{bn_*,w} | (128,) (128,) (128,) (128,) (128,128,3,3)       |
| res3.2.conv3.*    | res3_2_branch2c_{bn_*,w} | (512,) (512,) (512,) (512,) (512,128,1,1)       |
| res3.3.conv1.*    | res3_3_branch2a_{bn_*,w} | (128,) (128,) (128,) (128,) (128,512,1,1)       |
| res3.3.conv2.*    | res3_3_branch2b_{bn_*,w} | (128,) (128,) (128,) (128,) (128,128,3,3)       |
| res3.3.conv3.*    | res3_3_branch2c_{bn_*,w} | (512,) (512,) (512,) (512,) (512,128,1,1)       |
| res4.0.conv1.*    | res4_0_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,512,1,1)       |
| res4.0.conv2.*    | res4_0_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3)       |
| res4.0.conv3.*    | res4_0_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1)  |
| res4.0.shortcut.* | res4_0_branch1_{bn_*,w}  | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1)  |
| res4.1.conv1.*    | res4_1_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1)      |
| res4.1.conv2.*    | res4_1_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3)       |
| res4.1.conv3.*    | res4_1_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1)  |
| res4.2.conv1.*    | res4_2_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1)      |
| res4.2.conv2.*    | res4_2_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3)       |
| res4.2.conv3.*    | res4_2_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1)  |
| res4.3.conv1.*    | res4_3_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1)      |
| res4.3.conv2.*    | res4_3_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3)       |
| res4.3.conv3.*    | res4_3_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1)  |
| res4.4.conv1.*    | res4_4_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1)      |
| res4.4.conv2.*    | res4_4_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3)       |
| res4.4.conv3.*    | res4_4_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1)  |
| res4.5.conv1.*    | res4_5_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1)      |
| res4.5.conv2.*    | res4_5_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3)       |
| res4.5.conv3.*    | res4_5_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1)  |
| res5.0.conv1.*    | res5_0_branch2a_{bn_*,w} | (512,) (512,) (512,) (512,) (512,1024,1,1)      |
| res5.0.conv2.*    | res5_0_branch2b_{bn_*,w} | (512,) (512,) (512,) (512,) (512,512,3,3)       |
| res5.0.conv3.*    | res5_0_branch2c_{bn_*,w} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1)  |
| res5.0.shortcut.* | res5_0_branch1_{bn_*,w}  | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |
| res5.1.conv1.*    | res5_1_branch2a_{bn_*,w} | (512,) (512,) (512,) (512,) (512,2048,1,1)      |
| res5.1.conv2.*    | res5_1_branch2b_{bn_*,w} | (512,) (512,) (512,) (512,) (512,512,3,3)       |
| res5.1.conv3.*    | res5_1_branch2c_{bn_*,w} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1)  |
| res5.2.conv1.*    | res5_2_branch2a_{bn_*,w} | (512,) (512,) (512,) (512,) (512,2048,1,1)      |
| res5.2.conv2.*    | res5_2_branch2b_{bn_*,w} | (512,) (512,) (512,) (512,) (512,512,3,3)       |
| res5.2.conv3.*    | res5_2_branch2c_{bn_*,w} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1)  |
| stem.conv1.norm.* | res_conv1_bn_*           | (64,) (64,) (64,) (64,)                         |
| stem.conv1.weight | conv1_w                  | (64, 3, 7, 7)                                   |
WARNING [08/25 10:53:44 fvcore.common.checkpoint]: Some model parameters or buffers are not found in the checkpoint:
backbone.fpn_lateral2.{bias, weight}
backbone.fpn_lateral3.{bias, weight}
backbone.fpn_lateral4.{bias, weight}
backbone.fpn_lateral5.{bias, weight}
backbone.fpn_output2.{bias, weight}
backbone.fpn_output3.{bias, weight}
backbone.fpn_output4.{bias, weight}
backbone.fpn_output5.{bias, weight}
proposal_generator.rpn_head.anchor_deltas.{bias, weight}
proposal_generator.rpn_head.conv.{bias, weight}
proposal_generator.rpn_head.objectness_logits.{bias, weight}
roi_heads.box_head.fc1.{bias, weight}
roi_heads.box_head.fc2.{bias, weight}
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.box_predictor.cls_score.{bias, weight}
roi_heads.mask_head.deconv.{bias, weight}
roi_heads.mask_head.mask_fcn1.{bias, weight}
roi_heads.mask_head.mask_fcn2.{bias, weight}
roi_heads.mask_head.mask_fcn3.{bias, weight}
roi_heads.mask_head.mask_fcn4.{bias, weight}
roi_heads.mask_head.predictor.{bias, weight}
WARNING [08/25 10:53:44 fvcore.common.checkpoint]: The checkpoint state_dict contains keys that are not used by the model:
  fc1000.{bias, weight}
  stem.conv1.bias
/media/tai/6TB/Projects/Segmentation/Detectron2/.venv/lib/python3.7/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[08/25 10:53:44 detectron2]: /home/tai/Downloads/riding_horse.png: detected 0 instances in 0.10s

Expected behavior:

The model is expected to detect objects as being shown in the Colaboratory example with multiple detected objects.

Environment:

2022-08-25 11:03:19 URL:https://raw.githubusercontent.com/facebookresearch/detectron2/main/detectron2/utils/collect_env.py [8391/8391] -> "collect_env.py" [1]
----------------------  -----------------------------------------------------------------------------------------------------------
sys.platform            linux
Python                  3.7.6 (default, Nov  3 2021, 18:42:57) [GCC 9.3.0]
numpy                   1.21.6
detectron2              0.6 @/media/tai/6TB/Projects/Segmentation/Detectron2/detectron2/detectron2
Compiler                GCC 10.3
CUDA compiler           CUDA 10.2
detectron2 arch flags   6.1
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.10.2+cu102 @/media/tai/6TB/Projects/Segmentation/Detectron2/.venv/lib/python3.7/site-packages/torch
PyTorch debug build     False
GPU available           Yes
GPU 0                   NVIDIA GeForce GTX 1080 Ti (arch=6.1)
Driver version          515.65.01
CUDA_HOME               /usr/local/cuda-10.2
Pillow                  9.2.0
torchvision             0.11.3+cu102 @/media/tai/6TB/Projects/Segmentation/Detectron2/.venv/lib/python3.7/site-packages/torchvision
torchvision arch flags  3.5, 5.0, 6.0, 7.0, 7.5
fvcore                  0.1.5.post20220512
iopath                  0.1.9
cv2                     4.6.0
----------------------  -----------------------------------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70
  - CuDNN 7.6.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

Hopefully someone know how to fix this. Thanks a lot for your attetion! 😃

Issue Analytics

State:
Created a year ago
Reactions:3
Comments:6

Top GitHub Comments

1reaction

github-actions[bot]commented, Aug 25, 2022

You’ve chosen to report an unexpected problem or bug. Unless you already know the root cause of it, please include details about it by filling the issue template. The following information is missing: “Instructions To Reproduce the Issue and Full Logs”;

0reactions

maidouxiaozicommented, Nov 2, 2022

I’m also facing the same issue. Any update on this?

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