Comments (7)
Hi,
we used 256 of the initial size to be able to augment the images by random cropping to its final size of 224. I suggest to make sure that the data preparation is done and no axis are accidentally switched. https://github.com/thohemp/6DRepNet/blob/master/sixdrepnet/datasets.py#L177
from 6drepnet.
Thanks for the response,
I have identified axes using Your demo code. Im processing the BIWI dataset with this code too. I'm loading BIWI_70_30 model. I have noticed, that there are many persons on some pictures, thus i pick the area with the biggest face for each image.
The only difference from https://github.com/shamangary/FSA-Net/blob/4361d0e48103bb215d15734220c9d17e6812bb48/data/TYY_create_db_biwi.py#L40 is that in the demo code Retina Face detector is used instead of MTCNN. Results I obtain are:
X Y Z corresponds to Pitch Yaw Roll respectively
from 6drepnet.
What evaluation script do you use? I just tested this repo again and it gives me reported results:
python test.py --dataset BIWI --filename-list /path/to/BIWI_70_30_test.npz --snapshot 6DRepNet_70_30_BIWI.pth
Output:
Yaw: 2.6911, Pitch: 2.9193, Roll: 2.3557, MAE: 2.6553
from 6drepnet.
Im using BIWI from HuggingFace. Im not using this preprocessing step from FSA-Net.
Steps Im doing:
For each image in the BIWI:
- from ground truth rotation matrix compute X Y Z angles.
- Crop face with Retina Face detector
- Add 0.4*size offset
- Make predictions with 6DRepNet_70_30_BIWI.pth
- Compute absolute error for each angle.
At the end, I compute MAE.
Im doing this by modyfing demo.py
to not to work with webcam, but with the BIWI.
Does my results mean I should retrain 6DRepNet with the BIWI dataset preprocessed in my way to obtain results simiar to Yours?
from 6drepnet.
I haven't used the BIWI data from HuggingFace. Hence, I can't tell where the problem lies. But the huggingface data won't have the same split as mine, so, by using my model, you are probably testing on part of the data the model is already trained on. So, either stick with my way on data preparation or completely retrain on a new model on your data.
from 6drepnet.
I have followed Your steps and i got results as below:
Yaw: 1.6349, Pitch: 1.8322, Roll: 1.7049, MAE: 1.7240
.
However, I have found splitting script to be non deterministic, thus probably some of test data have been seen durning training.
Do You mind sending me Your test part so I can compare the results with other algorithms?
[email protected]
from 6drepnet.
I won't share the dataset to not come in conflict with the corresponding license. Beyond that, my test data will give you different results, as your model is trained on different data. The script will provide you a dataset split, where training and test images are not the same. So it allows you to train the model yourself and generate similar results to mine.
from 6drepnet.
Related Issues (20)
- Pip install failed HOT 4
- quick start version will install torch-cpu automaticlly HOT 1
- Pretrained weight cannot be downloaded HOT 2
- is the pretrained model for only faces? HOT 1
- may i ask how does the performance compare with 3ddfav2?
- is there possible to provide a smaller version? HOT 3
- what the difference between those different pretain models?
- RGB inputs or BGR inputs for model.predict(img)? HOT 4
- Import error with pip package HOT 9
- Reference System
- Finetuning the model HOT 1
- Why did you set up the scheduler as MultiStepLR=False? HOT 2
- please tell me where 'output/snapshots/1.pth' in test.py HOT 6
- Using your own trained weight files, the test results lose a lot
- Getting different results using your code and your fine_tuned model for BIWI.
- Is there a way to run this model on Apple M1 that doesn't have CUDA support? HOT 2
- Could not achieve same results in demo HOT 3
- Where is the face detection model in test.py?
- module 'utils' has no attribute 'compute_rotation_matrix_from_ortho6d' HOT 3
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from 6drepnet.