From b8b2161f3ea29d165096c5334391cbdb69753abe Mon Sep 17 00:00:00 2001 From: aimspot Date: Mon, 24 Jun 2024 18:23:21 +0300 Subject: [PATCH] Fix colab output --- .github/workflows/ml-test.yaml | 2 +- .github/workflows/model-test.yaml | 2 +- README.md | 88 +-- docs/img/alg.gif | Bin 0 -> 570177 bytes docs/img/logo_new.png | Bin 0 -> 106548 bytes docs/img/prew.gif | Bin 0 -> 3458339 bytes examples/api_example.ipynb | 659 -------------------- examples/api_example_ODRS.ipynb | 581 +++++++++++++++++ examples/example.ipynb | 484 -------------- src/DL/config/train_config.yaml | 10 +- src/DL/train_models/models/yolov7/train.py | 98 +-- src/DL/train_models/scripts/yolov5_train.py | 53 +- src/DL/train_models/scripts/yolov7_train.py | 59 +- src/DL/train_models/scripts/yolov8_train.py | 56 +- 14 files changed, 814 insertions(+), 1278 deletions(-) create mode 100644 docs/img/alg.gif create mode 100644 docs/img/logo_new.png create mode 100644 docs/img/prew.gif delete mode 100644 examples/api_example.ipynb create mode 100644 examples/api_example_ODRS.ipynb delete mode 100755 examples/example.ipynb diff --git a/.github/workflows/ml-test.yaml b/.github/workflows/ml-test.yaml index 0a2d3d3..d588b24 100644 --- a/.github/workflows/ml-test.yaml +++ b/.github/workflows/ml-test.yaml @@ -26,4 +26,4 @@ jobs: mkdir WaRP/Warp-D/valid cp -r WaRP/Warp-D/test/* WaRP/Warp-D/valid/ - name: Ml predictions - run: python src/ml_utils/run_recommender.py + run: python src/ML/run_recommender.py diff --git a/.github/workflows/model-test.yaml b/.github/workflows/model-test.yaml index ea58b28..3d3bc9f 100644 --- a/.github/workflows/model-test.yaml +++ b/.github/workflows/model-test.yaml @@ -27,4 +27,4 @@ jobs: cp -r WaRP/Warp-D/test/* WaRP/Warp-D/valid/ - name: AI training run: | - python src/train_utils/train_detectors.py \ No newline at end of file + python src/DL/train_detectors.py \ No newline at end of file diff --git a/README.md b/README.md index 17e1301..a66324c 100755 --- a/README.md +++ b/README.md @@ -1,71 +1,39 @@ # ODRS -[![PythonVersion](https://img.shields.io/badge/python-3.8.10%20-blue)](https://pypi.org/project/scikit-learn/) +[![PythonVersion](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue)](https://pypi.org/project/scikit-learn/) [![Documentation Status](https://readthedocs.org/projects/odrs-test/badge/?version=latest)](https://odrs-test.readthedocs.io/en/latest/?badge=latest) - +[![wiki](https://img.shields.io/badge/wiki-latest-blue)](http://www.wiki.odrs.space)

- +

Acknowledgement to ITMO - + Open In Colab
-ODRS - it an open source recommendation system for training object detection models. Our system allows you to choose the most +

+ +**ODRS** - it an open source recommendation system for training object detection models. Our system allows you to choose the most profitable existing object recognition models based on user preferences and data. In addition to choosing the architecture of the model, the system will help you start training and configure the environment. -The proposed recommendation system consists of several components that interact to generate recommendations for machine learning pipelines. -
- -
-External parameters (received from users and third-party resources): -* Dataset: Represents input data (video frames) and associated metadata (e.g. image size, quality, number of objects). -* Model: Framework provides an opportunity to train the most popular object recognition models (including setting up the environment -and choosing the architecture of a specific model). Considered two-stage detectors models such as Faster R-CNN and Mask R-CNN as -well as one-stage detectors such as SSD and YOLO (including families v5, v7, v8). -
-
-
- -
- -Internal components: - -* ***RecommendationEngine***: generates recommendations based on user data and dataset characteristics. -
-
- +
-The recommendation algorithm is based on production rules. The primary set of rules (knowledge base) is formed on -the basis of the results of the analysis of scientific sources and standard data sets, but also empirical processing -of data sets from specific industries. -The main criteria for drawing up the rules were chosen: - -1. Dimension of the model -2. The value of metrics (mAP, Recall, Accuracy) for selected datasets -3. The speed of the model on GPU and CPU -4. Supported image format and dimension - - -* ***Training*** - Training of models proposed by the system -
-
+The proposed recommendation system consists of several components that interact to generate recommendations for machine learning pipelines.
- +
-* ***Evaluation*** – evaluation of the quality of training models ## Contents @@ -87,7 +55,7 @@ cd ODRS/ pip install -r requirements.txt ``` ## Dataset structure -To use the recommendation system or train the desired detector, put your dataset in yolo format in the ***user_datasets/yolo*** directory. The set can have the following structures: +To use the recommendation system or train the desired detector, put your dataset in yolo format in the ***user_datasets/*** directory. The set can have the following structures: ```markdown user_datasets |_ _ @@ -146,39 +114,37 @@ jetski lift ``` ## ML Recommendation system -After you have placed your dataset in the folder ***user_datasets/yolo*** and created in the root directory ***.txt*** a file containing the names of all classes in your set of images. You can start working with the main functionality of the project. +After you have placed your dataset in the folder ***user_datasets/*** and created in the root directory ***.txt*** a file containing the names of all classes in your set of images. You can start working with the main functionality of the project. 1. In order to use the recommendation system, you need to configure **ml_config.yaml**. Go to the desired directory: ```markdown - cd ODRS/ml_utils/config/ + cd src/ML/config/ ``` 2. Open **ml_config.yaml** and set the necessary parameters and paths: ```markdown - #dataset_path: path to data folder - #classes_path: path to classes.txt - #GPU: True/False - #speed: 1 - 5 if you want max speed choose 5. For lower speed 1 - #accuracy: 1 - 10 if you want max accuracy choose 10. For lower accuracy 1 + #dataset_path: path to data folder or name dataset folder in user_dataset + #classes_path: path to classes.txt or name classes.txt in root directory + #GPU: True/False - "Inference mode" + #speed: True/False - "Search for models with a focus on speed" + #accuracy: True/False - "Search for models with a focus on accuracy" + #balance: True/False - "Search for models with a focus on the balance between speed and accuracy" - GPU: true - accuracy: 10 classes_path: classes.txt - dataset_path: /media/farm/ssd_1_tb_evo_sumsung/ODRS/user_datasets/yolo/plant - speed: 1 + dataset_path: /home/runner/work/ODRS/ODRS/user_datasets/WaRP/Warp-D + GPU: False + accuracy: False + speed: False + balance: True ``` -3. Go to the script **ml_model_optimizer.py** and start it: +3. Go to the script **run_recommender.py** and start it: ```markdown cd .. - python ml_model_optimizer.py + python run_recommender.py ``` 4. If everything worked successfully, you will see something like the following answer: ```markdown - Number of images: 3496 - Width: 960 - Height: 540 - Gini Coefficient: 94.0 - Number of classes: 28 + Top models for training: 1) yolov7 2) yolov8x6 diff --git a/docs/img/alg.gif b/docs/img/alg.gif new file mode 100644 index 0000000000000000000000000000000000000000..1c1ec4c93b2181244760e5e65dc95f13bffdb55f GIT binary patch literal 570177 zcmcG#2T)UQyY`z-8W39O5UOCy}J70YFe$U=#pYzQ*GiS~-iS5a;Pj8i$NfG5)BBUp~RS^n5E54?$T;U ze?O~(#o=HD`!3w94IF-NuAc3=9kaN}5Wc z)Oqgw()?0PXpFRy5}m<-V=x#(j71EE?kUab@4$F^(wEYSL}_czc2Z0_*HgNGX_ZK~ zpc4r!Pl^(T&_Q34=Kl%3BgvYgsiY}wp+ux(xI_Y{BOT+}LFB|3SXjU6(o6Pz+&nXr4~NSW`MUENi-^ zwEl1`zsxGc=}>O7S~h@r1iSNo~NekzMV*TfLH7$VV{!J-f; zp;Qim%fWE?mtrjuSwyZTflEwE!X$B7OI%HiCY|WXqG-}B;B?|P(PDcZ6EoBi8cN~9 zF$Ne+%;qYU&e>#Oq&XA|Pr@dbwau{JCdO-p9B zZAKDxb=8{HVZFHukhbOyElEpj5{RDLJ4@*ls)2>l()^}{g(rnhrBhhM&=`1V5{rLZ zcXp(eFzKN&76#G)xF#Tmnw}n`6q>{x;s5{`3u|i&1O8z^P3PbH4y7b&C?Q5ulc0pr 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