Update `fraction` arg to employ a random selection #8234

johnnynunez · 2024-02-15T22:34:04Z

better if you have datasets unified

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Improved image selection for diverse dataset sampling 📸✨

📊 Key Changes

Introduced randomness in selecting a subset of image files for datasets when not using the full dataset.

🎯 Purpose & Impact

Purpose: This change aims to make dataset samples more diverse by randomly selecting images instead of taking the first portion of the dataset. This can help in training models to be more robust and perform better across varied data.
Impact: Users employing a fraction of the dataset for model training or evaluation will now get a more diverse and possibly more representative sample of the whole dataset. This could lead to improvements in model accuracy and generalization.

codecov · 2024-02-15T22:37:00Z

Codecov Report

Attention: 2 lines in your changes are missing coverage. Please review.

Comparison is base (d8b8b71) 75.75% compared to head (ece9c1f) 75.70%.

Files	Patch %	Lines
ultralytics/data/base.py	0.00%	2 Missing ⚠️

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johnnynunez · 2024-02-15T22:42:10Z

#8233

Burhan-Q · 2024-02-16T19:53:16Z

@johnnynunez I believe I understand the change you're proposing. I suspect that the difference is slightly nuanced in what happens now vs what you're proposing. Your proposed change would randomize the selections for the train/val split. The existing code uses the shuffle argument in the Dataloader but I'm not certain that this would accomplish the same result, since the randomization is within the Dataloader which is probably specific to either train or val set (I'm not 100% certain).

I suspect that in most cases, people who care enough to want randomized train and val sampling, will probably split the data before passing in for training. Anyone else using fraction either doesn't care about randomizing or has a large enough dataset that splitting it this way wouldn't matter all that much.

Out of curiosity, have you tested to measure how much process time would be added using random.sample to split the dataset? I suspect this would be one of the most important considerations when evaluating this change.

glenn-jocher · 2024-02-17T17:13:33Z

@johnnynunez yes agree with @Burhan-Q here, the function is working as intended, it's simply using the first X percent of the dataset rather than sampling it randomly.

I suppose the only conceivable drawback is that the images in this first percent of the dataset can be replaced by changing the seed. @Burhan-Q what do you think? With the default settings, i.e. same seed the randomization is almost the same as the slice op.

My main usage for this was for training on the first i.e. 1% or 10% of huge datasets like OIV7 and Objects365 while developing and debugging.

Burhan-Q · 2024-02-17T20:02:22Z

@glenn-jocher I agree that this good as-is, especially given the argument description in the docs does not mention anything about randomly splitting the data:

Specifies the fraction of the dataset to use for training. Allows for training on a subset of the full dataset, useful for experiments or when resources are limited.

There's also this

ultralytics/ultralytics/data/utils.py

Lines 615 to 647 in 2f22ff4

    
           def autosplit(path=DATASETS_DIR / "coco8/images", weights=(0.9, 0.1, 0.0), annotated_only=False): 
        
               """ 
        
               Automatically split a dataset into train/val/test splits and save the resulting splits into autosplit_*.txt files. 
        
               Args: 
        
                   path (Path, optional): Path to images directory. Defaults to DATASETS_DIR / 'coco8/images'. 
        
                   weights (list | tuple, optional): Train, validation, and test split fractions. Defaults to (0.9, 0.1, 0.0). 
        
                   annotated_only (bool, optional): If True, only images with an associated txt file are used. Defaults to False. 
        
               Example: 
        
                   ```python 
        
                   from ultralytics.data.utils import autosplit 
        
                   autosplit() 
        
                   ``` 
        
               """ 
        
               path = Path(path)  # images dir 
        
               files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS)  # image files only 
        
               n = len(files)  # number of files 
        
               random.seed(0)  # for reproducibility 
        
               indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split 
        
               txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"]  # 3 txt files 
        
               for x in txt: 
        
                   if (path.parent / x).exists(): 
        
                       (path.parent / x).unlink()  # remove existing 
        
               LOGGER.info(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only) 
        
               for i, img in TQDM(zip(indices, files), total=n): 
        
                   if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label 
        
                       with open(path.parent / txt[i], "a") as f: 
        
                           f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n")  # add image to txt file

which I commented about in the referenced issue, which can be used for randomly sampling/splitting the dataset. Maybe it could be worth while to reference this function in the docs table in the fraction argument description for anyone who may want something more randomized.

johnnynunez · 2024-02-19T11:13:39Z

For example in my case I have put together 4 datasets and converted them into coco format. As I am doing experiments to try to improve the architecture of Yolov8 I make 50 epochs but then I saw the slice so I could be eliminating 3/4 datasets, and this is a serious mistake. With my selection I may be randomly picking items from all dataset.

glenn-jocher · 2024-02-19T21:49:20Z

@johnnynunez hi there! It sounds like you're doing some interesting work combining multiple datasets for your YOLOv8 experiments. If you're concerned about the dataset slicing potentially leaving out some of your data, you might want to consider shuffling your combined dataset before training. This way, you can ensure that your training and validation sets are randomly sampled from across all your datasets.

Here's a quick example of how you could shuffle your dataset in Python:

from ultralytics.data.utils import autosplit

autosplit("path/to/dataset", weights=(0.9, 0.1, 0.0))

This way, you're randomly selecting from all your datasets for both training and validation. Hope this helps, and best of luck with your experiments! 🚀

glenn-jocher · 2024-03-11T11:02:39Z

@johnnynunez I've decided to merge this in as I believe it makes sense. Thank you for your contributions to YOLO!

@Retry

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> * `ultralytics 8.1.22` HUB model `pathlib` fix (ultralytics#8621) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `ultralytics 8.1.23` add YOLOv9-C and E models (ultralytics#8571) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add settings `runs_dir` == `datasets_dir` warning (ultralytics#8653) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add Ultralytics HUB Cloud Training banner to Docs (ultralytics#8656) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update CITATION.cff with cffinit corrections (ultralytics#8658) * Update Colab notebook for YOLOv8.1 (ultralytics#8660) * `ultralytics 8.1.24` new OpenVINO 2023.3 export updates (ultralytics#8417) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * OBB: Fix when training on zip data (ultralytics#8680) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Object Counter improvements (ultralytics#8648) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Clarify triton server boolean value (ultralytics#8532) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add `model.eval()` in TensorBoad graph visualization to avoid BN stats changes (ultralytics#8629) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add domain checks to CI docs page (ultralytics#8714) * `ultralytics 8.1.25` OpenVINO `LATENCY` and `THROUGHPUT` modes (ultralytics#8058) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Adrian Boguszewski <adekboguszewski@gmail.com> * Add https://youtu.be/OpWpBI35A5Y to inference-api Docs (ultralytics#8717) * OpenVino 2024.0.0 (ultralytics#8726) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Fix tfjs exports for ARM64 Dockerfile (ultralytics#8766) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Modify tuner best.pt logic to train first (ultralytics#8792) * Update CI.md with CI links (ultralytics#8786) * Add https://youtu.be/ZzUSXQkLbNw to Integration Docs (ultralytics#8793) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `ultralytics 8.1.25` fix `**kwargs: (dict)` warnings (ultralytics#8815) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add docs guide terminal images (ultralytics#8819) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `RTDETRDetectionModel` TorchScript, ONNX Predict and Val support (ultralytics#8818) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * New `MLFLOW_KEEP_RUN_ACTIVE` flag for enhanced MLflow run management (ultralytics#8808) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `ultralytics 8.1.26` `LoadImagesAndVideos` batched inference (ultralytics#8817) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update `fraction` arg to employ a random selection (ultralytics#8234) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Batch inference fixes (ultralytics#8854) * Integrate OpenVINO `CUMULATIVE_THROUGHPUT` mode batched inference (ultralytics#8834) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add PaddlePaddle Docs Integrations Page (ultralytics#8858) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `ultralytics 8.1.27` batched tracking fixes (ultralytics#8842) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Faster batch `same_shapes` (ultralytics#8851) * More robust PNNX downloads (ultralytics#8866) * Fix `test_mlflow_keep_run_active()` (ultralytics#8868) * Add polygon regions drawing support in `object-counting.md` and minor docs update (ultralytics#8885) * Add TFLite Edge TPU Docs Integrations Page (ultralytics#8900) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update Docs README (ultralytics#8919) * Fix `names` bug when exporting YOLOv8-World to ONNX (ultralytics#8941) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * New test for labels and crops (ultralytics#8861) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Optimize TFJS export on ARM64 (ultralytics#8946) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions * `ultralytics 8.1.28` avoid * ops on bool Tensors for RT-DETR OpenVINO export (ultralytics#8937) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add https://youtu.be/YDXKa1EljmU and https://youtu.be/5BO0Il_YYAg to Docs (ultralytics#8958) * `ultralytics 8.1.29` improved disk space checking on correct path (ultralytics#8977) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com> * Add OpenVINO Latency vs Throughput modes Docs page (ultralytics#9001) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add `brain-tumor` dataset docs page (ultralytics#9041) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Fix Neural Magic links (ultralytics#9144) * Updating a typo on paddlepaddle.md (ultralytics#9108) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `ultralytics 8.1.30` add advanced HUB train arguments (ultralytics#9110) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * fix yolo classify model loading error (ultralytics#9196) * Add TF GraphDef Docs Integrations Page (ultralytics#9203) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `ultralytics 8.1.31` NCNN and CLIP updates (ultralytics#9235) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Fix TypeError in check_cfg() (ultralytics#9245) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add https://youtu.be/aeAX6vWpfR0 to docs & `african-wildlife` dataset docs page (ultralytics#9102) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add TF SavedModel Docs Integrations Page (ultralytics#9162) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Limit `ray<2.9.3` due to bug in `ray==2.10.0` (ultralytics#9254) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `ultralytics 8.1.32` fix CLIP backwards compatibility (ultralytics#9253) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Conda cv2 ImportError libEGL.so.1 fix (ultralytics#9255) * Fix `IS_PYTHON_3_12` bug (ultralytics#9258) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> * Reverse IoU threshold for NMS docs descriptions (ultralytics#9151) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add `bgr` hyperparameter (ultralytics#9139) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update pyproject.toml `[tool.pytest.ini_options]` (ultralytics#9259) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Fix OpenVINO missing batch-size bug (ultralytics#9264) * `ultralytics 8.1.33` fix HUB model checks (ultralytics#9153) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update Discord and Contributing Guide URLs (ultralytics#9270) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Add https://youtu.be/uDrn9QZJ2lk and `object_counting.py`, `ai_gym.py` updates (ultralytics#9265) * `ultralytics 8.1.34` Inference API robust imgsz checks (ultralytics#9274) Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Bump contributor-assistant/github-action from 2.3.1 to 2.3.2 in /.github/workflows (ultralytics#9279) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Add https://youtu.be/u3pLlgzUeV8 to docs (ultralytics#9367) * Fix typo in YOLOv8-Libtorch-CPP-Inference (ultralytics#9330) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Simplify metrics calculation (ultralytics#9338) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Fix typo in example readme (ultralytics#9306) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Specify YOLODataset task arg during INT8 calibration (ultralytics#9309) Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * `ultralytics 8.1.35` simplify network modules (ultralytics#9321) Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Signed-off-by: dependabot[bot] <support@github.com> Signed-off-by: Skillnoob <78843978+Skillnoob@users.noreply.github.com> Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Abirami Vina <abirami.vina@gmail.com> Co-authored-by: Paula Derrenger <107626595+pderrenger@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com> Co-authored-by: WangQvQ <1579093407@qq.com> Co-authored-by: Martin Pl <martin-plank@gmx.de> Co-authored-by: Mactarvish <Mactarvish@users.noreply.github.com> Co-authored-by: DieterTimmers <46112022+DieterTimmers@users.noreply.github.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: AdamP <7806910+adamp87@users.noreply.github.com> Co-authored-by: Youho99 <44434482+Youho99@users.noreply.github.com> Co-authored-by: Ankan Ghosh <79740115+0xSynapse@users.noreply.github.com> Co-authored-by: Skillnoob <78843978+Skillnoob@users.noreply.github.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Priytosh Tripathi <priytosh.revolution@live.com> Co-authored-by: Johannes Kaisinger <johannes.kaisinger@gmail.com> Co-authored-by: Johnny <johnnynuca14@gmail.com> Co-authored-by: Lakshantha Dissanayake <lakshanthad@yahoo.com> Co-authored-by: Alexander Suslov <alexander.suslov@intel.com> 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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

randomly

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Merge branch 'main' into patch-1

bf1ac63

Merge branch 'main' into patch-1

ece9c1f

glenn-jocher changed the title ~~randomly selection #8233~~ Update fraction to employ a random selection Mar 11, 2024

Merge branch 'main' into patch-1

2a02ad2

glenn-jocher changed the title ~~Update fraction to employ a random selection~~ Update fraction arg to employ a random selection Mar 11, 2024

glenn-jocher merged commit 5893d44 into ultralytics:main Mar 11, 2024
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Burhan-Q linked an issue Mar 18, 2024 that may be closed by this pull request

fraction randomly instead of slice #8233

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Burhan-Q mentioned this pull request Mar 18, 2024

fraction randomly instead of slice #8233

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gkinman pushed a commit to Octasic/ultralytics that referenced this pull request May 30, 2024

Update fraction arg to employ a random selection (ultralytics#8234)

3cefb55

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

puzhiyuan pushed a commit to puzhiyuan/ultralytics that referenced this pull request Oct 18, 2024

Update fraction arg to employ a random selection (ultralytics#8234)

7d95852

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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Update `fraction` arg to employ a random selection #8234

Update `fraction` arg to employ a random selection #8234

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glenn-jocher commented Mar 11, 2024

Update fraction arg to employ a random selection #8234

Update fraction arg to employ a random selection #8234

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glenn-jocher commented Mar 11, 2024

Update `fraction` arg to employ a random selection #8234

Update `fraction` arg to employ a random selection #8234

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