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ultralytics 8.1.21 Add YOLOv8-World-v2 models #8580

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merged 27 commits into from
Mar 3, 2024
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@Laughing-q Laughing-q commented Mar 1, 2024

πŸ› οΈ PR Summary

Made with ❀️ by Ultralytics Actions

🌟 Summary

Introducing enhanced YOLOv8-World models and improved export capabilities.

πŸ“Š Key Changes

  • CI Pipeline Update: Added benchmarking for the new "YOLOv8s-Worldv2" model.
  • Documentation Enhancement: Included new YOLOv8-Worldv2 models and updated metrics in the models' documentation.
  • Model Version Increment: The Ultralytics library version is bumped from 8.1.20 to 8.1.21.
  • Export Functionality: Expanded with warnings for YOLOWorld (original) model, which is not supported for export.
  • Model Saving Update: Integrated metadata including versioning and documentation links in the saved model checkpoints.
  • YOLO Model Enhancements: YOLOv8 initialization now detects and switches to YOLOWorld models based on the file name.
  • New YOLOWorldv2 models: Introduced in various sizes (s, m, l, x) with updated configurations and detection heads.
  • Benchmark Utility Improvements: Adjusted for compatibility with YOLOWorld models and export capabilities.
  • Downloads: Added new pre-trained YOLOv8-Worldv2 model weights to the download list.

🎯 Purpose & Impact

  • The addition of YOLOv8-Worldv2 models aims to provide users with more advanced versions of pre-trained weights, offering better performance and export options.
  • The library version update ensures users have the latest features and bug fixes.
  • Improved export warnings and capabilities enhance model portability and deployment across different platforms, which is crucial for production environments.
  • Updated documentation ensures users have accurate and up-to-date information about model performance and capabilities.
  • Metadata in saved models helps in tracking model versions and related documentation, improving model management.
  • The CI pipeline and benchmarking updates ensure that the models' performance is consistently tracked and that the models maintain a high standard of quality.

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codecov bot commented Mar 1, 2024

Codecov Report

Attention: Patch coverage is 81.57895% with 7 lines in your changes are missing coverage. Please review.

Project coverage is 75.82%. Comparing base (906b8d3) to head (57c9f7a).

Files Patch % Lines
ultralytics/engine/model.py 20.00% 4 Missing ⚠️
ultralytics/utils/patches.py 62.50% 3 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff             @@
##             main    #8580      +/-   ##
==========================================
+ Coverage   75.75%   75.82%   +0.07%     
==========================================
  Files         117      117              
  Lines       14693    14709      +16     
==========================================
+ Hits        11130    11153      +23     
+ Misses       3563     3556       -7     
Flag Coverage Ξ”
Benchmarks 36.88% <63.15%> (+0.58%) ⬆️
GPU 38.96% <23.68%> (-0.04%) ⬇️
Tests 70.81% <57.89%> (-0.05%) ⬇️

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@Laughing-q Laughing-q linked an issue Mar 1, 2024 that may be closed by this pull request
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@glenn-jocher
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@Laughing-q @Burhan-Q hey guys, not sure what naming convention to use here. Should we always just support the latest without a version number i.e. yolov8x-world.pt, or should be attempt to support multiple versions, i.e. yolov8x-world-v2.pt?

The versioned filename is probably more precise, but will create many more assets for us to track and maintain too.

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Burhan-Q commented Mar 3, 2024

I think for external models, unless is a major difference/change, we should only keep the most recent or up-to-date version with the base-name. So for this one, since the changes didn't seem to be too big, I think that yolov8x-world.pt makes sense to me.

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@glenn-jocher @Burhan-Q Hey guys, after some thinking I prefer to keep both two versions:

  • From the official paper and the update notes, actually v1 models got slightly better evaluation values on LVIS dataset. The only problem is that it's hard to export to other formats because of I-PoolingAttention module, so I figure there are users who want this version(that has better metrics but only pytorch available).
  • The main difference between v1 and v2 is that v2 has chance to support exporting other formats(and I believe we'll support in near future). And if we simply replace the weights with v2 version then there's no way to identify v2 and old v1 weights, and once we support exporting v2, users who has downloaded v1 models are going to ask questions about why their weights can not be exported.
  • As the topic of yolo-world is still hot I think, supporting both its v1 and v2 versions would have positive impact on traffic.

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@Laughing-q ok got it. Then should we rename the first weights as v1 or leave them without a version number?

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@Laughing-q also have you verified export functionality for v2? Should we green-checkmark the export column for the v2 models here?
Screenshot 2024-03-03 at 19 02 20

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glenn-jocher commented Mar 3, 2024

Export seems to work well for torchscript and ONNX

(venv) glennjocher@Glenns-MacBook-Air-2 ultralytics % yolo export model=yolov8s-worldv2.pt
WARNING ⚠️ 'format' is missing. Using default 'format=torchscript'.
Ultralytics YOLOv8.1.20 πŸš€ Python-3.11.2 torch-2.2.0 CPU (Apple M2)
YOLOv8s-worldv2 summary (fused): 195 layers, 12749288 parameters, 0 gradients, 50.8 GFLOPs

PyTorch: starting from 'yolov8s-worldv2.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (24.7 MB)

TorchScript: starting export with torch 2.2.0...
TorchScript: export success βœ… 1.3s, saved as 'yolov8s-worldv2.torchscript' (49.3 MB)

Export complete (1.7s)
Results saved to /Users/glennjocher/PycharmProjects/ultralytics
Predict:         yolo predict task=detect model=yolov8s-worldv2.torchscript imgsz=640  
Validate:        yolo val task=detect model=yolov8s-worldv2.torchscript imgsz=640 data=None  
Visualize:       https://netron.app
πŸ’‘ Learn more at https://docs.ultralytics.com/modes/export


(venv) glennjocher@Glenns-MacBook-Air-2 ultralytics % yolo export model=yolov8s-worldv2.pt format=onnx
Ultralytics YOLOv8.1.20 πŸš€ Python-3.11.2 torch-2.2.0 CPU (Apple M2)
YOLOv8s-worldv2 summary (fused): 195 layers, 12749288 parameters, 0 gradients, 50.8 GFLOPs

PyTorch: starting from 'yolov8s-worldv2.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (24.7 MB)

ONNX: starting export with onnx 1.15.0 opset 17...
ONNX: export success βœ… 0.9s, saved as 'yolov8s-worldv2.onnx' (49.9 MB)

Export complete (1.3s)
Results saved to /Users/glennjocher/PycharmProjects/ultralytics
Predict:         yolo predict task=detect model=yolov8s-worldv2.onnx imgsz=640  
Validate:        yolo val task=detect model=yolov8s-worldv2.onnx imgsz=640 data=None  
Visualize:       https://netron.app
πŸ’‘ Learn more at https://docs.ultralytics.com/modes/export
(venv) glennjocher@Glenns-MacBook-Air-2 ultralytics % 

@Burhan-Q Burhan-Q added the enhancement New feature or request label Mar 3, 2024
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@Laughing-q I noticed the v2 models were saved using dill, could you re-save them after uninstalling dill?

The way the ultralytics package works I think is it might use it when saving if it's available, but our official models should not require it as it not in our pyproject.toml
Screenshot 2024-03-03 at 19 51 03

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
@glenn-jocher glenn-jocher changed the title YOLO-World: add yolo-world-v2 models ultralytics 8.1.21 Add YOLO-World-v2 models Mar 3, 2024
glenn-jocher and others added 4 commits March 3, 2024 20:22
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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glenn-jocher commented Mar 3, 2024

@Laughing-q @Burhan-Q ok I've gotten export to work for a few formats, added World to benchmarks for the supported export formats, re-saved and re-uploaded all YOLO-World assets without dill usage (script below). I think this is all good now.

Strip-dill script

from pathlib import Path

from ultralytics import YOLO


def resave_models(model_names, source_dir, target_dir):
    """Load and resave YOLOv8 models using the Ultralytics package."""
    for model_name in model_names:
        model_path = source_dir / model_name
        print(f"Loading model from {model_path}")

        # Load model
        model = YOLO(model_path)
        model.half()

        # Define new save path
        save_path = target_dir / model_name

        # Save model using model.save()
        print(f"Resaving model to {save_path}")
        model.save(save_path, use_dill=False)

        print(f"Model {model_name} resaved successfully.")


if __name__ == "__main__":
    # Define source and target directories
    source_dir = Path(".")
    target_dir = Path("./resaved_models")
    target_dir.mkdir(parents=True, exist_ok=True)  # Ensure target directory exists

    # List of model names to process
    model_names = [f"yolov8{size}-world{version}.pt" for size in "smlx" for version in ("", "v2")]

    # Run the resave process
    resave_models(model_names, source_dir, target_dir)

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The benchmarks look like this:
Screenshot 2024-03-03 at 21 28 03

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
@glenn-jocher glenn-jocher changed the title ultralytics 8.1.21 Add YOLO-World-v2 models ultralytics 8.1.21 Add YOLOv8-World-v2 models Mar 3, 2024
@glenn-jocher glenn-jocher merged commit 946e18f into main Mar 3, 2024
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@glenn-jocher glenn-jocher deleted the yolo-world-v2 branch March 3, 2024 21:56
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@Laughing-q PR merged! Awesome work on this!!

DannyCooler added a commit to ecs-enerserv/ultralytics that referenced this pull request Mar 28, 2024
* Explorer API video https://youtu.be/3VryynorQeo  (ultralytics#7838)

* Add HUB-SDK Docs reference section (ultralytics#7781)

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* Try Apple M1 runners for Tests and Benchmarks CI (ultralytics#8162)

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* `ultralytics 8.1.12` new `Results.show()` and `Results.save()` (ultralytics#8166)

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* Fix solution functions description + `integrations/openvino.md` cover image update (ultralytics#8170)

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* Pin `duckdb<=0.9.2` to avoid 0.10.0 errors (ultralytics#8181)

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* New HUB Cloud Training docs page (ultralytics#8174)

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* `ultralytics 8.1.13` fix `yolo train time=` bug (ultralytics#8179)

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* Docs updates and improvements (ultralytics#8183)

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* Fix xyxyxyxyn calculation in obb result (ultralytics#8188)

* `ultralytics 8.1.14` new YOLOv8-World models (ultralytics#8054)

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* Fix PIL `show()` and `save()` (ultralytics#8202)

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* Remove OpenVINO INT8 RTDETRDecoder ops (ultralytics#8219)

* Fix xyxyxyxyn calculation, swap axis (ultralytics#8249)

* Improve Docs Modes tables (ultralytics#8266)

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* `ultralytics 8.1.15` add Python 3.12 compatibility (ultralytics#8210)

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* Expand `Model` method type hinting (ultralytics#8279)

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* `ultralytics 8.1.16` OBB ConfusionMatrix support (ultralytics#8299)

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* Move Explorer tests to scheduled CI (ultralytics#8305)

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* Remove JAX constraints in Exporter (ultralytics#8309)

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* Avoid creating `-hub` dir without processing images (ultralytics#8334)

* Added check of parameter type before setting `required_grad=True` for frozen layers (ultralytics#8318)

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* Add `estimate_speed(region_color)` parameter (ultralytics#8285)

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* Update `mkdocs-ultralytics-plugin>=0.0.44` (ultralytics#8347)

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* `ultralytics 8.1.17` fix `ClassificationDataset` caching (ultralytics#8358)

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* Add missing single-line docstrings (ultralytics#8362)

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* `ultralytics 8.1.18` add cmake for building onnxsim on aarch64 (ultralytics#8395)

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* Allow Annotator PIL.Image inputs (ultralytics#8397)

* Add Non-Maximum Suppression (NMS) `inplace` flag (ultralytics#8368)

* Compare `plt.get_backend()` in lowercase (ultralytics#8409)

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* Improve Docs arguments tables (ultralytics#8415)

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* Add plot_images `conf_thresh` parameter (ultralytics#8446)

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* Rename `model_id` to `model.id` (ultralytics#8447)

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* Add TorchScript Docs Integrations Page (ultralytics#8501)

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* Do RTDETR file suffix check using pathlib instead of string manipulations (ultralytics#8525)

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* Add TFLite Docs Integrations Page (ultralytics#8522)

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* Fix undefined β€˜im_array’ bug in predict.md (ultralytics#8565)

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* Add https://youtu.be/ie3vLUDNYZo and other YT videos in Docs (ultralytics#8551)

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* Hold failed upload metrics and upload with next upload metrics (ultralytics#8513)

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* `ultralytics 8.1.20` add YOLOv8x-World support (ultralytics#8539)

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* Remove unused variables (ultralytics#8511)

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* Add NCNN Docs Integrations Page (ultralytics#8562)

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* Replacing Images for torchscript.md (ultralytics#8596)

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* Fix cmake requirements for ARM64 (ultralytics#8589)

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* Add https://youtu.be/rCggzXRRSRo to Speed Estimation Docs (ultralytics#8577)

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* Update IoU capitalization (ultralytics#8604)

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* Update TFLite Docs images (ultralytics#8605)

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* Add `distance calculation` feature in `vision-eye` (ultralytics#8616)

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* `ultralytics 8.1.21` Add YOLOv8-World-v2 models (ultralytics#8580)

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* `ultralytics 8.1.22` HUB model `pathlib` fix (ultralytics#8621)

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* `ultralytics 8.1.23` add YOLOv9-C and E models (ultralytics#8571)

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* Add settings `runs_dir` == `datasets_dir` warning (ultralytics#8653)

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* Add Ultralytics HUB Cloud Training banner to Docs (ultralytics#8656)

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* 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)

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* OBB: Fix when training on zip data (ultralytics#8680)

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* Object Counter improvements (ultralytics#8648)

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* Clarify triton server boolean value (ultralytics#8532)

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* Add `model.eval()` in TensorBoad graph visualization to avoid BN stats changes (ultralytics#8629)

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* Add domain checks to CI docs page (ultralytics#8714)

* `ultralytics 8.1.25` OpenVINO `LATENCY` and `THROUGHPUT` modes (ultralytics#8058)

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* Add https://youtu.be/OpWpBI35A5Y to inference-api Docs (ultralytics#8717)

* OpenVino 2024.0.0 (ultralytics#8726)

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* Fix tfjs exports for ARM64 Dockerfile (ultralytics#8766)

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* 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)

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* `ultralytics 8.1.25` fix `**kwargs: (dict)` warnings (ultralytics#8815)

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* Add docs guide terminal images (ultralytics#8819)

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* `RTDETRDetectionModel` TorchScript, ONNX Predict and Val support (ultralytics#8818)

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* New `MLFLOW_KEEP_RUN_ACTIVE` flag for enhanced MLflow run management (ultralytics#8808)

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* `ultralytics 8.1.26` `LoadImagesAndVideos` batched inference (ultralytics#8817)

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

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* Batch inference fixes (ultralytics#8854)

* Integrate OpenVINO `CUMULATIVE_THROUGHPUT` mode batched inference (ultralytics#8834)

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* Add PaddlePaddle Docs Integrations Page (ultralytics#8858)

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* `ultralytics 8.1.27` batched tracking fixes (ultralytics#8842)

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* 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)

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* Update Docs README (ultralytics#8919)

* Fix `names` bug when exporting YOLOv8-World to ONNX (ultralytics#8941)

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* New test for labels and crops (ultralytics#8861)

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* Optimize TFJS export on ARM64 (ultralytics#8946)

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* `ultralytics 8.1.28` avoid * ops on bool Tensors for RT-DETR OpenVINO export (ultralytics#8937)

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* 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)

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* Add OpenVINO Latency vs Throughput modes Docs page (ultralytics#9001)

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* Add `brain-tumor` dataset docs page (ultralytics#9041)

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* Fix Neural Magic links (ultralytics#9144)

* Updating a typo on paddlepaddle.md (ultralytics#9108)

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* `ultralytics 8.1.30` add advanced HUB train arguments (ultralytics#9110)

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* fix yolo classify model loading error (ultralytics#9196)

* Add TF GraphDef Docs Integrations Page (ultralytics#9203)

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* `ultralytics 8.1.31` NCNN and CLIP updates (ultralytics#9235)

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* Fix TypeError in check_cfg() (ultralytics#9245)

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* Add https://youtu.be/aeAX6vWpfR0 to docs & `african-wildlife` dataset docs page (ultralytics#9102)

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* Add TF SavedModel Docs Integrations Page (ultralytics#9162)

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* Limit `ray<2.9.3` due to bug in `ray==2.10.0` (ultralytics#9254)

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* `ultralytics 8.1.32` fix CLIP backwards compatibility (ultralytics#9253)

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* Conda cv2 ImportError libEGL.so.1 fix (ultralytics#9255)

* Fix `IS_PYTHON_3_12` bug (ultralytics#9258)

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* Reverse IoU threshold for NMS docs descriptions (ultralytics#9151)

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* Add `bgr` hyperparameter (ultralytics#9139)

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* Update pyproject.toml `[tool.pytest.ini_options]` (ultralytics#9259)

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* Fix OpenVINO missing batch-size bug (ultralytics#9264)

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* Update Discord and Contributing Guide URLs (ultralytics#9270)

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* Add https://youtu.be/u3pLlgzUeV8 to docs (ultralytics#9367)

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* Simplify metrics calculation (ultralytics#9338)

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* Fix typo in example readme (ultralytics#9306)

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* Specify YOLODataset task arg during INT8 calibration (ultralytics#9309)

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* `ultralytics 8.1.35` simplify network modules (ultralytics#9321)

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---------

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gkinman pushed a commit to Octasic/ultralytics that referenced this pull request May 30, 2024
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Integrate YOLO-Worldv2 updates
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