faster_rcnn | C Version Train and Inference Faster-RCNN | Computer Vision library
kandi X-RAY | faster_rcnn Summary
kandi X-RAY | faster_rcnn Summary
C++ Version Train and Inference Faster-RCNN.
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Top functions reviewed by kandi - BETA
- Visualize all object classes
- Wrapper for nms
- Transform boxes and deltas
- Visualize visdetections
- Return the ROIDB
- Set the roidb handler
- Calls build extensions
- Decorator to customize the cuda library
- Load configuration from a file
- Recursively merge two config dictionaries
- Append flipped images
- Returns the widths of each image
- Locate CUDA
- Find a file in a given search path
- Setup the image
- Add a path to sys path
- Parse command line arguments
faster_rcnn Key Features
faster_rcnn Examples and Code Snippets
Community Discussions
Trending Discussions on faster_rcnn
QUESTION
I am trying to adapt the code detailed here https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html for crack detection using the Kanha dataset (https://github.com/khanhha/crack_segmentation).
My problem is that when converting masks to bounding boxes, some are empty - and thus invalid.
ValueError: All bounding boxes should have positive height and width. Found invalid box
Should I add a minimum area clause before appending boxes[], boxes.append([xmin, ymin, xmax, ymax])? Or does anyone have another idea how to make progress?
...ANSWER
Answered 2022-Feb-24 at 11:18I have done exactly the same for a project and I believe the issue comes from detection that will have a width or a height of 1 pixel only.
In this case, xmin = xmax or ymin = ymax which creates the issue.
I just added 1 to my xmax and ymax to ensure boxes are never empty.
QUESTION
I went through the mmdetection documentation to convert a pytorch model to onnx here link
All installations are correct and i'm using onnxruntime==1.8.1, custom operators for ONNX Runtime MMCV_WITH_OPS.
I'm using the configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py for faster rcnn link and using R-5-FPN pretrained model link
I used this to convert the pretrained model to onnx and successfully saved an onnx file named fasterrcnn.onnx
...ANSWER
Answered 2021-Dec-03 at 21:00Looking at the conversion script seems like dets is a combo of boxes plus score
QUESTION
I want to train a custom dataset on using faster_rcnn or mask_rcnn with the Pytorch and Detectron2 .Everything works well but I wanted to know I want to know what are the results I have.
...ANSWER
Answered 2021-Dec-02 at 12:49Those are metrics printed out at every iteration of the training loop. The most important ones are the loss values, but below are basic descriptions of them all (eta and iter are self-explanatory I think).
total_loss: This is a weighted sum of the following individual losses calculated during the iteration. By default, the weights are all one.
loss_cls: Classification loss in the ROI head. Measures the loss for box classification, i.e., how good the model is at labelling a predicted box with the correct class.loss_box_reg: Localisation loss in the ROI head. Measures the loss for box localisation (predicted location vs true location).loss_rpn_cls: Classification loss in the Region Proposal Network. Measures the "objectness" loss, i.e., how good the RPN is at labelling the anchor boxes as foreground or background.loss_rpn_loc: Localisation loss in the Region Proposal Network. Measures the loss for localisation of the predicted regions in the RPN.loss_mask: Mask loss in the Mask head. Measures how "correct" the predicted binary masks are.For more details on the losses (1) and (2), take a look at the Fast R-CNN paper and the code.
For more details on the losses (3) and (4), take a look at the Faster R-CNN paper and the code.
For more details on the loss (5), take a look at the Mask R-CNN paper and the code.
time: Time taken by the iteration.
data_time: Time taken by the dataloader in that iteration.
lr: The learning rate in that iteration.
max_mem: Maximum GPU memory occupied by tensors in bytes.
QUESTION
I'm digging through the source code of the Faster R-CNN implementation of torchvision and I'm facing some things I don't quite understand. Namely, assuming that I want to create a Faster R-CNN model, not pretrained on COCO, with a backbone pre-trained on ImageNet, and then just get the backbone I do the following:
ANSWER
Answered 2020-Dec-15 at 14:27This is because fasterrcnn_resnet50_fpn uses a custom normalization layer (FrozenBatchNorm2d) instead of the default BatchNorm2D. They are very similar but I suspect that the small numerical differences are causing issues.
It will pass the check if you specify the same normalization layer to be used for the standard resnet:
QUESTION
I am doing tensorflow (v1.14) object detection api. I was using faster_rcnn_inception_resnet_v2_atrous_coco with num_of_stages : 1 in config.
I tried generating inference graph using command:
ANSWER
Answered 2020-Nov-25 at 05:25okay, found the solution, turns out the github solution does work, particularly this one. so i just added these lines on exporter.py:
QUESTION
I am trying to train an object detection algorithm with samples that I have labeled using Label-img. My images have dimensions of 1100 x 1100 pixels. The algorithm I am using is the Faster R-CNN Inception ResNet V2 1024x1024, found on the TensorFlow 2 Detection Model Zoo. The specs of my operation are as follows:
- TensorFlow 2.3.1
- Python 3.8.6
- GPU: NVIDIA GEFORCE RTX 2060 (laptop has 16 GB RAM and 6 processing cores)
- CUDA: 10.1
- cuDNN: 7.6
- Anaconda 3 command prompt
The .config file is as follows:
...ANSWER
Answered 2020-Nov-19 at 15:28Take a look on this thread ( By your post I think you are read it):
Resource exhausted: OOM when allocating tensor only on gpu
The two possible solutions are to change config.gpu_options.per_process_gpu_memory_fraction to a greater number.
The other solutions were to reinstall cuda.
You can use nvidia docker. Then you can switch between versions quickly.
https://github.com/NVIDIA/nvidia-docker
You can change cuda versions and see if the error persists.
QUESTION
- GPU: NVIDIA GEFORCE RTX 2060
- GPU: 16GB RAM, 6 processor cores
- TensorFlow: 2.3.1
- Python: 3.8.6
- CUDA: 10.1
- cuDNN: 7.6
I am training a Mask R-CNN Inception ResNet V2 1024x1024 algorithm (on my computer's GPU), as downloaded from the TensorFlow 2 Detection Model Zoo. I am training this algorithm on my custom dataset, which I have labeled using Label-img . When I train the model using the Anaconda command python model_main_tf2.py --model_dir=models/my_faster_rcnn --pipeline_config_path=models/my_faster_rcnn/pipeline.config, I get the following error:
ANSWER
Answered 2020-Nov-13 at 19:03You may be missing fine_tune_checkpoint_version: V2 in train_config{}. Try custom modifications with this config below,
QUESTION
I am using the latest version of tensorflow hub, and wondering how one gets information on a model's expected input shape, as well as on what type of collection the model belongs to. For example, is there a way to get info on the expected image shape after having loaded a model in Python this way?
...ANSWER
Answered 2020-Nov-07 at 19:38you can get shape of input which model expects, from accessing first layer of the model, and accessing input_shape attribute of that layer
QUESTION
so I am trying to run my object detection program and I keep getting the following error message:
AssertionError: Torch not compiled with CUDA enabled.
I don't understand why this happens, as I have a 2017 MacBook Pro with an AMD GPU, so I have no CUDA enabled GPU.
I added this statement in my code to make sure the device is set to 'cpu', however, it looks as if the program keeps trying to run it through a GPU even though it does not exist.
...ANSWER
Answered 2020-Sep-09 at 16:58Turns out I had to reinstall torch and torch vision to make everything work
QUESTION
I am using scikit-image to load a random image from a folder. OpenCV is being used for operations later on..
Code is as follows (only relevant parts included)
...ANSWER
Answered 2020-Apr-15 at 12:15This is a bug in Pillow 7.1.0. You can upgrade Pillow with pip install -U pillow. See this bug report for more information:
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Install faster_rcnn
You can use faster_rcnn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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