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opencv_opencv_model
nilcars/opencv_opencv_model
opencv_opencv_model is a text classification model from nilcars. Use it when you need a label for a piece of text. It is set up for setfit.
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 cl…
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At a glance
- Task
- Text Classification
- Library
- setfit
- Model type
- mpnet
- Access
- Public
- Created
- Jun 24, 2024
- Updated
- Jun 24, 2024
- SHA
- 884c9d41
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Base models
Model card
From the Hugging Face model README
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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 384 tokens
- Number of Classes: 3 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| feature | <ul><li>'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_'</li><li>'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'</li><li>'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'</li></ul> |
| bug | <ul><li>'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'</li><li>"Fixed bug when MSMF webcamera doesn't start when build with VIDEOIO_PLUGIN_ALL Fixed #23937 and #23056\r\n"</li><li>"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\n |
| question | <ul><li>'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.'</li><li>"Opencv error with GPU backend <!--\r\nIf you have a question rather than reporting a bug please go to https://forum.opencv.org where you get much faster responses.\r\nIf you need further assistance please read [How To Contribute](https://github.com/opencv/opencv/wiki/How_to_contribute).\r\n\r\nThis is a template helping you to create an issue that can be processed as quickly as possible. This is the bug reporting section for the OpenCV library.\r\n-->\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 OpenCV team works with forum.opencv.org, Stack Overflow and other communities\r\n to discuss problems. Tickets with questions without a real issue statement will be\r\n closed.\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 Places to check:\r\n * OpenCV documentation: https://docs.opencv.org\r\n * FAQ page: https://github.com/opencv/opencv/wiki/FAQ\r\n * OpenCV forum: https://forum.opencv.org\r\n * OpenCV issue tracker: https://github.com/opencv/opencv/issues?q=is%3Aissue\r\n * Stack Overflow branch: https://stackoverflow.com/questions/tagged/opencv\r\n -->\r\n - [x] I updated to the latest OpenCV version and the issue is still there\r\n <!--\r\n master branch for OpenCV 4.x and 3.4 branch for OpenCV 3.x releases.\r\n OpenCV team supports only the latest release for each branch.\r\n The ticket is closed if the problem is not reproduced with the modern version.\r\n -->\r\n - [ ] There is reproducer code and related data files: videos, images, onnx, etc\r\n <!--\r\n The best reproducer -- test case for OpenCV that we can add to the library.\r\n Recommendations for media files and binary files:\r\n * Try to reproduce the issue with images and videos in opencv_extra repository\r\n to reduce attachment size\r\n * Use PNG for images, if you report some CV related bug, but not image reader\r\n issue\r\n * Attach the image as an archive to the ticket, if you report some reader issue.\r\n Image hosting services compress images and it breaks the repro code.\r\n * Provide ONNX file for some public model or ONNX file with random weights,\r\n if you report ONNX parsing or handling issue. Architecture details diagram\r\n from netron tool can be very useful too. See https://lutzroeder.github.io/netron/\r\n -->\r\n"</li><li>'cv2.imread() fails when filenames have multiple dots (.) <!--\r\nIf you have a question rather than reporting a bug please go to https://forum.opencv.org where you get much faster responses.\r\nIf you need further assistance please read [How To Contribute](https://github.com/opencv/opencv/wiki/How_to_contribute).\r\n\r\nThis is a template helping you to create an issue which can be processed as quickly as possible. This is the bug reporting section for the OpenCV library.\r\n-->\r\n\r\n##### System information (version)\r\n<!-- Example\r\n- OpenCV => 4.2\r\n- Operating System / Platform => Windows 64 Bit\r\n- Compiler => Visual Studio 2017\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<!-- to add code example fence it with triple backticks and optional file extension\r\n ```.cpp\r\n // C++ code example\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:[email protected]] 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 OpenCV team works with forum.opencv.org, Stack Overflow and other communities\r\n to discuss problems. Tickets with questions without a real issue statement will be\r\n closed.\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 Places to check:\r\n * OpenCV documentation: https://docs.opencv.org\r\n * FAQ page: https://github.com/opencv/opencv/wiki/FAQ\r\n * OpenCV forum: https://forum.opencv.org\r\n * OpenCV issue tracker: https://github.com/opencv/opencv/issues?q=is%3Aissue\r\n * Stack Overflow branch: https://stackoverflow.com/questions/tagged/opencv\r\n -->\r\n - [x] I updated to the latest OpenCV version and the issue is still there\r\n <!--\r\n master branch for OpenCV 4.x and 3.4 branch for OpenCV 3.x releases.\r\n OpenCV team supports only the latest release for each branch.\r\n The ticket is closed if the problem is not reproduced with the modern version.\r\n -->\r\n - [x] There is reproducer code and related data files: videos, images, onnx, etc\r\n <!--\r\n The best reproducer -- test case for OpenCV that we can add to the library.\r\n Recommendations for media files and binary files:\r\n * Try to reproduce the issue with images and videos in opencv_extra repository\r\n to reduce attachment size\r\n * Use PNG for images, if you report some CV related bug, but not image reader\r\n issue\r\n * Attach the image as an archive to the ticket, if you report some reader issue.\r\n Image hosting services compress images and it breaks the repro code.\r\n * Provide ONNX file for some public model or ONNX file with random weights,\r\n if you report ONNX parsing or handling issue. Architecture details diagram\r\n from netron tool can be very useful too. See https://lutzroeder.github.io/netron/\r\n -->\r\n'</li></ul> |
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.
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}
}
- Task
- Text Classification
- Library
- setfit
- Type
- mpnet
- Created
- Jun 24, 2024
- Updated
- Jun 24, 2024