HigherHRNet-Human-Pose-Estimation | official implementation of our CVPR 2020 paper | Machine Learning library
kandi X-RAY | HigherHRNet-Human-Pose-Estimation Summary
kandi X-RAY | HigherHRNet-Human-Pose-Estimation Summary
This is the official code of HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation. Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped with multi-resolution supervision for training and multi-resolution aggregation for inference, the proposed approach is able to solve the scale variation challenge in bottom-up multi-person pose estimation and localize keypoints more precisely, especially for small person. The feature pyramid in HigherHRNet consists of feature map outputs from HRNet and upsampled higher-resolution outputs through a transposed convolution. HigherHRNet outperforms the previous best bottom-up method by 2.5% AP for medium person on COCO test-dev, showing its effectiveness in handling scale variation. Furthermore, HigherHRNet achieves new state-of-the-art result on COCO test-dev (70.5% AP) without using refinement or other post-processing techniques, surpassing all existing bottom-up methods. HigherHRNet even surpasses all top-down methods on CrowdPose test (67.6% AP), suggesting its robustness in crowded scene.
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Top functions reviewed by kandi - BETA
- Main worker function
- Builds the transformer
- Load the optimizer state
- Build a dataset
- Create a data loader from the given configuration
- Get a summary of a model
- Compute the output of multiple layers
- Evaluate the model
- Calculate keypoint results for each category
- Writes keypoints results to a file
- Do the python keypoint evaluation
- Evaluate the preds
- Write keypoints results to a coco_keypoint
- Loads the state of the optimizer
- Calculate heatmaps
- Create convolutional layers
- Create logger
- Aggregate heatmaps
- Parse command line arguments
- Create a single stage
- Resize an image
- Parse the detector and return the scores
- Test the AELoss
- Calculate the center of the image
- Create a test data loader
- Prints the name value to the logger
- Check that the configuration is correct
- Resize a multi - scale image
HigherHRNet-Human-Pose-Estimation Key Features
HigherHRNet-Human-Pose-Estimation Examples and Code Snippets
# install tensorflow v1.5
pip install -U https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl
# install required packages
pip install -U easydict numpy scipy scikit-image init_weights: /BS/mihai/work/e
$./run.py -h
usage: Train [-h] -g GPU_ID -c CONFIG -o OUTPUT [-t TOML]
{PG2-1,PG2-2,PG2-Generator}
positional arguments:
{PG2-1,PG2-2,PG2-Generator}
run which?
optional arguments:
-h, --help show
cd human-pose-estimation-by-deep-learning\labels\python\
python draw_point.py
01060538.png 137,68,136,80,138,105,121,90,107,102,87,114,152,90,168,105,189,118,133,131,130,163,128,201,146,130,149,161,153,199,1
drawing image 01060538.png
coordinate of
Community Discussions
Trending Discussions on Machine Learning
QUESTION
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.
...ANSWER
Answered 2022-Feb-17 at 10:47You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
QUESTION
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset.
...ANSWER
Answered 2022-Jan-14 at 23:47Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model)
is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true
and y_pred
.
QUESTION
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi
I see these processes running
ANSWER
Answered 2021-Nov-23 at 06:13This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
QUESTION
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWER
Answered 2021-Nov-04 at 21:17No, they aren't comparable.
Your baseline model used X_train
to fit the model. Then you're using the fitted model to score the X_train
sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
X_train
sample. - Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
X_val
per fold).
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of best_model.best_score_
? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
QUESTION
I am not able to access jupyter lab created on google cloud
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
I tried the diagnostic tool, which gave the following result:
but didn't fix it.
Thanks in advance.
...ANSWER
Answered 2021-Aug-20 at 14:00You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
QUESTION
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:
...ANSWER
Answered 2021-Sep-29 at 22:47Turns out its just documented incorrectly.
In reality the export from brain.js is this:
QUESTION
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:
...ANSWER
Answered 2021-Sep-04 at 06:43You're right. Just one thing to consider for choosing OrdinalEncoder
or OneHotEncoder
is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]
orshirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white
category should be encoded as 0
and black
should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
QUESTION
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
QUESTION
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
Table example:
...ANSWER
Answered 2021-Aug-11 at 15:55You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install HigherHRNet-Human-Pose-Estimation
Install pytorch >= v1.1.0 following official instruction. Tested with pytorch v1.4.0
Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.
Install dependencies: pip install -r requirements.txt
Install COCOAPI: # COCOAPI=/path/to/clone/cocoapi git clone https://github.com/cocodataset/cocoapi.git $COCOAPI cd $COCOAPI/PythonAPI # Install into global site-packages make install # Alternatively, if you do not have permissions or prefer # not to install the COCO API into global site-packages python3 setup.py install --user Note that instructions like # COCOAPI=/path/to/install/cocoapi indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (COCOAPI in this case) accordingly.
Install CrowdPoseAPI exactly the same as COCOAPI. There is a bug in the CrowdPoseAPI, please reverse https://github.com/Jeff-sjtu/CrowdPose/commit/785e70d269a554b2ba29daf137354103221f479e
Init output(training model output directory) and log(tensorboard log directory) directory: mkdir output mkdir log Your directory tree should look like this: ${POSE_ROOT} ├── data ├── experiments ├── lib ├── log ├── models ├── output ├── tools ├── README.md └── requirements.txt
Download pretrained models from our model zoo(GoogleDrive or OneDrive) ${POSE_ROOT} `-- models `-- pytorch |-- imagenet | `-- hrnet_w32-36af842e.pth `-- pose_coco `-- pose_higher_hrnet_w32_512.pth
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