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opencv_opencv_model

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
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SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
feature
  • 'imshow速度太慢，如何能加快imshow ### Descripe the feature and motivation\n\nimshow速度太慢，如何能加快imshow\n\n### Additional context\n\n_No response_'
  • 'Add H264 / H265 writter support for Android ### Pull Request Readiness Checklist\r\n\r\nSee details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request\r\n\r\n- [x] I agree to contribute to the project under Apache 2 License.\r\n- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV\r\n- [x] The PR is proposed to the proper branch\r\n- [ ] There is a reference to the original bug report and related work\r\n- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable\r\n Patch to opencv_extra has the same branch name.\r\n- [ ] The feature is well documented and sample code can be built with the project CMake\r\n'
  • '4-bit_palette_color ### Pull Request Readiness Checklist\r\n\r\nSee details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request\r\n\r\n- [x] I agree to contribute to the project under Apache 2 License.\r\n- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV\r\n- [x] The PR is proposed to the proper branch\r\n- [ ] There is a reference to the original bug report and related work\r\n- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable\r\n Patch to opencv_extra has the same branch name.\r\n- [ ] The feature is well documented and sample code can be built with the project CMake\r\n'
bug
  • 'DNN: support the split node of onnx opset >= 13 Merge with test case: https://github.com/opencv/opencv_extra/pull/1053.\r\n\r\nThe attribute of split in Split layer has been moved from attribute to input.\r\nRelated link: https://github.com/onnx/onnx/blob/main/docs/Operators.md#inputs-1---2-12\r\nThe purpose of this PR is to support the split with input type.\r\n\r\n### Pull Request Readiness Checklist\r\n\r\nSee details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request\r\n\r\n- [x] I agree to contribute to the project under Apache 2 License.\r\n- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV\r\n- [x] The PR is proposed to the proper branch\r\n- [ ] There is a reference to the original bug report and related work\r\n- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable\r\n Patch to opencv_extra has the same branch name.\r\n- [ ] The feature is well documented and sample code can be built with the project CMake\r\n'
  • "Fixed bug when MSMF webcamera doesn't start when build with VIDEOIO_PLUGIN_ALL Fixed #23937 and #23056\r\n"
  • "Fixes pixel info color font for dark Qt themes For dark Qt themes, it is hard to read the pixel color information on the bottom left, like the coordinates or RGB values. This PR proposes a way on how the dynamically sets the font colors based on the system's theme.\r\nOriginal Example:\r\n\r\noriginal\r\n\r\nWith patch:\r\n\r\nimage\r\n\r\n\r\nFor Windows, nothing is changed (tested on a windows 11 system), because the font color is #000000 when using the default Qt theme.\r\n\r\n### Pull Request Readiness Checklist\r\n\r\nSee details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request\r\n\r\n- [x] I agree to contribute to the project under Apache 2 License.\r\n- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV\r\n- [x] The PR is proposed to the proper branch\r\n- [ ] There is a reference to the original bug report and related work\r\n- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable\r\n Patch to opencv_extra has the same branch name.\r\n- [ ] The feature is well documented and sample code can be built with the project CMake\r\n"
question
  • 'In file included from /home/xxx/opencv/modules/python/src2/cv2.cpp:11:0: /home/xxx/opencv/build/modules/python_bindings_generator/pyopencv_generated_include.h:19:10: fatal error: opencv2/hdf/hdf5.hpp: no file or directory #include "opencv2/hdf/hdf5.hpp" ^~~~~~~~~~~~~~~~~~~~~~ I'm building opencv-cuda,it happens at the end.'
  • "Opencv error with GPU backend \r\n\r\n##### System information (version)\r\n\r\n- OpenCV => 4.5.5-dev\r\n- Operating System / Platform => Ubuntu 20\r\n- Compiler => GCC\r\n\r\n##### Detailed description\r\n\r\nI cannot run anymore my cv2.dnn on GPU. \r\nI tried to run the code in CPU and its workings. I also tested with the older version of OpenCV and is working. I think the new commit merged in February changed the dnn output is an np.array[] and is not compatible with 4.5.5 release version. Could you please give an example of how to use the new output?\r\n\r\n##### Steps to reproduce\r\n .python\r\n //with CUDA enabled\r\n net = cv2.dnn.readNetFromONNX(model_yolo_v5))\r\n net.setInput(_image)\r\n results = net.forward()\r\n \r\n##### Issue submission checklist\r\n\r\n - [ ] I report the issue, it's not a question\r\n \r\n - [x] I checked the problem with documentation, FAQ, open issues,\r\n forum.opencv.org, Stack Overflow, etc and have not found any solution\r\n \r\n - [x] I updated to the latest OpenCV version and the issue is still there\r\n \r\n - [ ] There is reproducer code and related data files: videos, images, onnx, etc\r\n \r\n"
  • 'cv2.imread() fails when filenames have multiple dots (.) \r\n\r\n##### System information (version)\r\n\r\n\r\n- OpenCV => 4.5.5.64\r\n- Operating System / Platform => Windows\r\n- Compiler => PyCharm Community Edition 2020.1.1\r\n\r\n##### Detailed description\r\n\r\nWhen the cv2.imread() method encounters a filename with multiple dots (.), a NoneType is returned. I think this might be because the file extension is assumed to be everything after the first dot, and not the last, but it could be something completely different.\r\n\r\nExample: \r\n\r\n##### Steps to reproduce\r\n\r\n\r\nRunning the following code, with two images in the same directory,\r\n.py\r\nimport cv2\r\n\r\nim = cv2.imread("C:/Users/.../Videos/Captures/test project – main.py 18_05_2022 15_44_50.png")\r\nim2 = cv2.imread("C:/Users/.../Videos/Captures/Screenshot 16_05_2022 23_40_07.png")\r\n\r\nprint(im)\r\nprint(im2)\r\n\r\ngives the result:\r\n.py\r\n[ WARN:0@0.221] global D:\\\\a\\\\opencv-python\\\\opencv-python\\\\opencv\\\\modules\\\\imgcodecs\\\\src\\\\loadsave.cpp (239) cv::findDecoder imread_(\'C:/Users/.../Videos/Captures/test project – main.py 18_05_2022 15_44_50.png\'): can\'t open/read file: check file path/integrity\r\nNone\r\n[[[255 255 255]\r\n [255 255 255]\r\n [255 255 255]\r\n ...\r\n [255 255 255]\r\n [255 255 255]\r\n [255 255 255]]\r\n\r\n [[255 255 255]\r\n [255 255 255]\r\n [255 255 255]\r\n ...\r\n [255 255 255]\r\n [255 255 255]\r\n [255 255 255]]\r\n\r\n [[153 139 140]\r\n [152 139 139]\r\n [151 139 138]\r\n ...\r\n [148 136 135]\r\n [150 138 138]\r\n [151 140 139]]]\r\n\r\nwhich shows that the first image (one dot) loaded successfully, but the second one (with the multiple dots) failed.\r\n##### Issue submission checklist\r\n\r\n - [x] I report the issue, it's not a question\r\n \r\n - [x] I checked the problem with documentation, FAQ, open issues,\r\n forum.opencv.org, Stack Overflow, etc and have not found any solution\r\n \r\n - [x] I updated to the latest OpenCV version and the issue is still there\r\n \r\n - [x] There is reproducer code and related data files: videos, images, onnx, etc\r\n \r\n'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("OpenCV build - cmake don't recognize GTK2 or GTK3 ##### System information (version)

- OpenCV => 4.7.0
- Operating System / Platform => Ubuntu 20.04
- Compiler => g++ (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
- C++ Standard: 11

##### Detailed description

I am trying to create OpenCV with the GTK GUI. I have `GTK2` and `GTK3` installed on the system, but neither version is recognised by cmake. 

![image](https://user-images.githubusercontent.com/62354721/230026534-66053f4e-67d3-45cd-93c2-4902365400c7.png)
![image](https://user-images.githubusercontent.com/62354721/230026727-07d64f8c-c453-4048-9d23-3608b1c94663.png)


The output after executing cmake remains 
**GUI: NONE** 
**GTK+:NO**

I execute the cmake command as follows:

`cmake -DPYTHON_DEFAULT_EXECUTABLE=$(which python3) -DWITH_GTK=ON ../opencv`

or

`cmake -DPYTHON_DEFAULT_EXECUTABLE=$(which python3) -DWITH_GTK=ON -DWITH_GTK_2_X=ON ../opencv`

##### Steps to reproduce

git clone https://github.com/opencv/opencv.git
mkdir -p build && cd build
cmake ../opencv (default, custom look above)

Thank you for your help
")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 393.0933 13648
Label Training Sample Count
bug 200
feature 200
question 200

Training Hyperparameters

  • batch_size: (16, 2)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0007 1 0.2963 -
0.0067 10 0.2694 -
0.0133 20 0.2443 -
0.02 30 0.2872 -
0.0267 40 0.2828 -
0.0333 50 0.2279 -
0.04 60 0.1852 -
0.0467 70 0.1983 -
0.0533 80 0.224 -
0.06 90 0.2315 -
0.0667 100 0.2698 -
0.0733 110 0.1178 -
0.08 120 0.1216 -
0.0867 130 0.1065 -
0.0933 140 0.1519 -
0.1 150 0.073 -
0.1067 160 0.0858 -
0.1133 170 0.1697 -
0.12 180 0.1062 -
0.1267 190 0.0546 -
0.1333 200 0.139 -
0.14 210 0.0513 -
0.1467 220 0.1168 -
0.1533 230 0.0455 -
0.16 240 0.0521 -
0.1667 250 0.0307 -
0.1733 260 0.0618 -
0.18 270 0.0964 -
0.1867 280 0.0707 -
0.1933 290 0.0524 -
0.2 300 0.0358 -
0.2067 310 0.0238 -
0.2133 320 0.0759 -
0.22 330 0.0197 -
0.2267 340 0.0053 -
0.2333 350 0.0035 -
0.24 360 0.0036 -
0.2467 370 0.0079 -
0.2533 380 0.0033 -
0.26 390 0.0021 -
0.2667 400 0.0026 -
0.2733 410 0.0018 -
0.28 420 0.0014 -
0.2867 430 0.0019 -
0.2933 440 0.0027 -
0.3 450 0.0016 -
0.3067 460 0.0027 -
0.3133 470 0.0095 -
0.32 480 0.0005 -
0.3267 490 0.0006 -
0.3333 500 0.0006 -
0.34 510 0.0634 -
0.3467 520 0.0025 -
0.3533 530 0.0013 -
0.36 540 0.0007 -
0.3667 550 0.0007 -
0.3733 560 0.0003 -
0.38 570 0.0006 -
0.3867 580 0.0007 -
0.3933 590 0.0004 -
0.4 600 0.0006 -
0.4067 610 0.0007 -
0.4133 620 0.0005 -
0.42 630 0.0004 -
0.4267 640 0.0005 -
0.4333 650 0.0013 -
0.44 660 0.0005 -
0.4467 670 0.0007 -
0.4533 680 0.0008 -
0.46 690 0.0018 -
0.4667 700 0.0007 -
0.4733 710 0.0008 -
0.48 720 0.0007 -
0.4867 730 0.0007 -
0.4933 740 0.0002 -
0.5 750 0.0002 -
0.5067 760 0.0002 -
0.5133 770 0.0007 -
0.52 780 0.0004 -
0.5267 790 0.0003 -
0.5333 800 0.0007 -
0.54 810 0.0004 -
0.5467 820 0.0003 -
0.5533 830 0.0002 -
0.56 840 0.001 -
0.5667 850 0.008 -
0.5733 860 0.0003 -
0.58 870 0.0002 -
0.5867 880 0.0011 -
0.5933 890 0.0005 -
0.6 900 0.0004 -
0.6067 910 0.0003 -
0.6133 920 0.0002 -
0.62 930 0.0002 -
0.6267 940 0.0002 -
0.6333 950 0.0001 -
0.64 960 0.0002 -
0.6467 970 0.0003 -
0.6533 980 0.0002 -
0.66 990 0.0005 -
0.6667 1000 0.0003 -
0.6733 1010 0.0002 -
0.68 1020 0.0003 -
0.6867 1030 0.0008 -
0.6933 1040 0.0003 -
0.7 1050 0.0005 -
0.7067 1060 0.0012 -
0.7133 1070 0.0001 -
0.72 1080 0.0003 -
0.7267 1090 0.0002 -
0.7333 1100 0.0001 -
0.74 1110 0.0003 -
0.7467 1120 0.0002 -
0.7533 1130 0.0003 -
0.76 1140 0.0596 -
0.7667 1150 0.0012 -
0.7733 1160 0.0004 -
0.78 1170 0.0003 -
0.7867 1180 0.0003 -
0.7933 1190 0.0002 -
0.8 1200 0.0015 -
0.8067 1210 0.0002 -
0.8133 1220 0.0001 -
0.82 1230 0.0002 -
0.8267 1240 0.0002 -
0.8333 1250 0.0002 -
0.84 1260 0.0002 -
0.8467 1270 0.0003 -
0.8533 1280 0.0001 -
0.86 1290 0.0001 -
0.8667 1300 0.0002 -
0.8733 1310 0.0004 -
0.88 1320 0.0004 -
0.8867 1330 0.0004 -
0.8933 1340 0.0001 -
0.9 1350 0.0002 -
0.9067 1360 0.055 -
0.9133 1370 0.0002 -
0.92 1380 0.0004 -
0.9267 1390 0.0001 -
0.9333 1400 0.0002 -
0.94 1410 0.0002 -
0.9467 1420 0.0004 -
0.9533 1430 0.0009 -
0.96 1440 0.0003 -
0.9667 1450 0.0427 -
0.9733 1460 0.0004 -
0.98 1470 0.0001 -
0.9867 1480 0.0002 -
0.9933 1490 0.0002 -
1.0 1500 0.0002 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.0
  • PyTorch: 2.3.0+cu121
  • Datasets: 2.20.0
  • Tokenizers: 0.15.2

Citation

BibTeX 

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
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Safetensors
Model size
109M params
Tensor type
F32
·
Inference API
Text Classification
Examples
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