diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md index b7fc7c5a8838..62a02a3a6948 100644 --- a/.github/ISSUE_TEMPLATE/bug-report.md +++ b/.github/ISSUE_TEMPLATE/bug-report.md @@ -7,21 +7,24 @@ assignees: '' --- -Before submitting a bug report, please be aware that your issue **must be reproducible** with all of the following, otherwise it is non-actionable, and we can not help you: - - **Current repo**: run `git fetch && git status -uno` to check and `git pull` to update repo - - **Common dataset**: coco.yaml or coco128.yaml - - **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments - -If this is a custom dataset/training question you **must include** your `train*.jpg`, `val*.jpg` and `results.png` figures, or we can not help you. You can generate these with `utils.plot_results()`. +Before submitting a bug report, please be aware that your issue **must be reproducible** with all of the following, +otherwise it is non-actionable, and we can not help you: +- **Current repo**: run `git fetch && git status -uno` to check and `git pull` to update repo +- **Common dataset**: coco.yaml or coco128.yaml +- **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments + +If this is a custom dataset/training question you **must include** your `train*.jpg`, `val*.jpg` and `results.png` +figures, or we can not help you. You can generate these with `utils.plot_results()`. ## πŸ› Bug -A clear and concise description of what the bug is. +A clear and concise description of what the bug is. ## To Reproduce (REQUIRED) Input: + ``` import torch @@ -30,6 +33,7 @@ c = a / 0 ``` Output: + ``` Traceback (most recent call last): File "/Users/glennjocher/opt/anaconda3/envs/env1/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code @@ -39,17 +43,17 @@ Traceback (most recent call last): RuntimeError: ZeroDivisionError ``` - ## Expected behavior -A clear and concise description of what you expected to happen. +A clear and concise description of what you expected to happen. ## Environment -If applicable, add screenshots to help explain your problem. - - OS: [e.g. Ubuntu] - - GPU [e.g. 2080 Ti] +If applicable, add screenshots to help explain your problem. +- OS: [e.g. Ubuntu] +- GPU [e.g. 2080 Ti] ## Additional context + Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md index 02320771b5f5..1fdf99045488 100644 --- a/.github/ISSUE_TEMPLATE/feature-request.md +++ b/.github/ISSUE_TEMPLATE/feature-request.md @@ -13,7 +13,8 @@ assignees: '' ## Motivation - + ## Pitch diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md index 2c22aea70a7b..2892cfe262fb 100644 --- a/.github/ISSUE_TEMPLATE/question.md +++ b/.github/ISSUE_TEMPLATE/question.md @@ -9,5 +9,4 @@ assignees: '' ## ❔Question - ## Additional context diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 7c0ba3ae9f18..38601775caeb 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -8,32 +8,44 @@ We love your input! We want to make contributing to YOLOv5 as easy and transpare - Proposing a new feature - Becoming a maintainer -YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be helping push the frontiers of what's possible in AI πŸ˜ƒ! - +YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be +helping push the frontiers of what's possible in AI πŸ˜ƒ! ## Submitting a Pull Request (PR) πŸ› οΈ + Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps: ### 1. Select File to Update + Select `requirements.txt` to update by clicking on it in GitHub.

PR_step1

### 2. Click 'Edit this file' + Button is in top-right corner.

PR_step2

### 3. Make Changes + Change `matplotlib` version from `3.2.2` to `3.3`.

PR_step3

### 4. Preview Changes and Submit PR -Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose changes** button. All done, your PR is now submitted to YOLOv5 for review and approval πŸ˜ƒ! + +Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** +for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose +changes** button. All done, your PR is now submitted to YOLOv5 for review and approval πŸ˜ƒ!

PR_step4

### PR recommendations To allow your work to be integrated as seamlessly as possible, we advise you to: -- βœ… Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature' with the name of your local branch: + +- βœ… Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an + automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may + be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature' + with the name of your local branch: + ```bash git remote add upstream https://github.com/ultralytics/yolov5.git git fetch upstream @@ -41,30 +53,42 @@ git checkout feature # <----- replace 'feature' with local branch name git merge upstream/master git push -u origin -f ``` -- βœ… Verify all Continuous Integration (CI) **checks are passing**. -- βœ… Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ -Bruce Lee +- βœ… Verify all Continuous Integration (CI) **checks are passing**. +- βœ… Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase + but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ -Bruce Lee ## Submitting a Bug Report πŸ› If you spot a problem with YOLOv5 please submit a Bug Report! -For us to start investigating a possibel problem we need to be able to reproduce it ourselves first. We've created a few short guidelines below to help users provide what we need in order to get started. +For us to start investigating a possibel problem we need to be able to reproduce it ourselves first. We've created a few +short guidelines below to help users provide what we need in order to get started. -When asking a question, people will be better able to provide help if you provide **code** that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces the problem should be: +When asking a question, people will be better able to provide help if you provide **code** that they can easily +understand and use to **reproduce** the problem. This is referred to by community members as creating +a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces +the problem should be: * βœ… **Minimal** – Use as little code as possible that still produces the same problem * βœ… **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself * βœ… **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem -In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code should be: - -* βœ… **Current** – Verify that your code is up-to-date with current GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits. -* βœ… **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. +In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code +should be: -If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the πŸ› **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better understand and diagnose your problem. +* βœ… **Current** – Verify that your code is up-to-date with current + GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new + copy to ensure your problem has not already been resolved by previous commits. +* βœ… **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this + repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. +If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the πŸ› ** +Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing +a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better +understand and diagnose your problem. ## License -By contributing, you agree that your contributions will be licensed under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/) +By contributing, you agree that your contributions will be licensed under +the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/) diff --git a/README.md b/README.md index b4aacd78b0ca..df4e9add519d 100644 --- a/README.md +++ b/README.md @@ -52,31 +52,33 @@ YOLOv5 πŸš€ is a family of object detection architectures and models pretrained - ##
Documentation
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. - ##
Quick Start Examples
-
Install -[**Python>=3.6.0**](https://www.python.org/) is required with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/): +[**Python>=3.6.0**](https://www.python.org/) is required with all +[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including +[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/): + ```bash $ git clone https://github.com/ultralytics/yolov5 $ cd yolov5 $ pip install -r requirements.txt ``` +
Inference -Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). +Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download +from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). ```python import torch @@ -85,7 +87,7 @@ import torch model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom # Images -img = 'https://ultralytics.com/images/zidane.jpg' # or PosixPath, PIL, OpenCV, numpy, list +img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) @@ -101,7 +103,9 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
Inference with detect.py -`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. +`detect.py` runs inference on a variety of sources, downloading models automatically from +the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. + ```bash $ python detect.py --source 0 # webcam file.jpg # image @@ -117,13 +121,18 @@ $ python detect.py --source 0 # webcam
Training -Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). +Run commands below to reproduce results +on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on +first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the +largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). + ```bash $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 yolov5m 40 yolov5l 24 yolov5x 16 ``` +
@@ -132,7 +141,8 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size Tutorials * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  πŸš€ RECOMMENDED -* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️ RECOMMENDED +* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️ + RECOMMENDED * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  🌟 NEW * [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518)  🌟 NEW * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) @@ -147,10 +157,11 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size
- ##
Environments and Integrations
-Get started in seconds with our verified environments and integrations, including [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) for automatic YOLOv5 experiment logging. Click each icon below for details. +Get started in seconds with our verified environments and integrations, +including [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) for automatic YOLOv5 experiment +logging. Click each icon below for details.
@@ -173,33 +184,33 @@ Get started in seconds with our verified environments and integrations, includin
- ##
Compete and Win
-We are super excited about our first-ever Ultralytics YOLOv5 πŸš€ EXPORT Competition with **$10,000** in cash prizes! +We are super excited about our first-ever Ultralytics YOLOv5 πŸš€ EXPORT Competition with **$10,000** in cash prizes!

- ##
Why YOLOv5

YOLOv5-P5 640 Figure (click to expand) - +

Figure Notes (click to expand) - - * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. - * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. - * **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` -
+* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size + 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. +* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. +* **Reproduce** by + `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` + +
### Pretrained Checkpoints @@ -221,24 +232,30 @@ We are super excited about our first-ever Ultralytics YOLOv5 πŸš€ EXPORT Competi
Table Notes (click to expand) - - * APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. - * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` - * SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half` - * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). - * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` -
+* APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results + denote val2017 accuracy. +* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** + by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` +* SpeedGPU averaged over 5000 COCO val2017 images using a + GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and + includes FP16 inference, postprocessing and NMS. **Reproduce speed** + by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half` +* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). +* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale + augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` -##
Contribute
+ -We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started. +##
Contribute
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see +our [Contributing Guide](CONTRIBUTING.md) to get started. ##
Contact
-For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or professional support requests please visit -[https://ultralytics.com/contact](https://ultralytics.com/contact). +For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or +professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
diff --git a/data/Argoverse.yaml b/data/Argoverse.yaml index c42624c5783f..3bf91ce7d504 100644 --- a/data/Argoverse.yaml +++ b/data/Argoverse.yaml @@ -15,7 +15,7 @@ test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/c # Classes nc: 8 # number of classes -names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] # class names +names: ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign'] # class names # Download script/URL (optional) --------------------------------------------------------------------------------------- diff --git a/data/GlobalWheat2020.yaml b/data/GlobalWheat2020.yaml index 842456047953..de9c7837cf57 100644 --- a/data/GlobalWheat2020.yaml +++ b/data/GlobalWheat2020.yaml @@ -27,7 +27,7 @@ test: # test images (optional) 1276 images # Classes nc: 1 # number of classes -names: [ 'wheat_head' ] # class names +names: ['wheat_head'] # class names # Download script/URL (optional) --------------------------------------------------------------------------------------- diff --git a/data/Objects365.yaml b/data/Objects365.yaml index 52577581d7bb..457b9fd9bf69 100644 --- a/data/Objects365.yaml +++ b/data/Objects365.yaml @@ -15,47 +15,47 @@ test: # test images (optional) # Classes nc: 365 # number of classes -names: [ 'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup', - 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book', - 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', - 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV', - 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', - 'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird', - 'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck', - 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning', - 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife', - 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock', - 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish', - 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan', - 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', - 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign', - 'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat', - 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard', - 'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry', - 'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks', - 'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors', - 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape', - 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck', - 'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette', - 'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket', - 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine', - 'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine', - 'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon', - 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse', - 'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball', - 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', - 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts', - 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit', - 'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD', - 'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder', - 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', - 'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab', - 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', - 'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart', - 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French', - 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', - 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil', - 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis' ] +names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup', + 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book', + 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', + 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV', + 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', + 'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird', + 'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck', + 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning', + 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife', + 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock', + 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish', + 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan', + 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', + 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign', + 'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat', + 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard', + 'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry', + 'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks', + 'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors', + 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape', + 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck', + 'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette', + 'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket', + 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine', + 'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine', + 'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon', + 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse', + 'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball', + 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', + 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts', + 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit', + 'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD', + 'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder', + 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', + 'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab', + 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', + 'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart', + 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French', + 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', + 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil', + 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis'] # Download script/URL (optional) --------------------------------------------------------------------------------------- diff --git a/data/SKU-110K.yaml b/data/SKU-110K.yaml index 01bf36c0d870..c85fa81d2e03 100644 --- a/data/SKU-110K.yaml +++ b/data/SKU-110K.yaml @@ -15,7 +15,7 @@ test: test.txt # test images (optional) 2936 images # Classes nc: 1 # number of classes -names: [ 'object' ] # class names +names: ['object'] # class names # Download script/URL (optional) --------------------------------------------------------------------------------------- diff --git a/data/VOC.yaml b/data/VOC.yaml index 55f39d852d31..e59fb6afd2fd 100644 --- a/data/VOC.yaml +++ b/data/VOC.yaml @@ -21,8 +21,8 @@ test: # test images (optional) # Classes nc: 20 # number of classes -names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', - 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ] # class names +names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', + 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names # Download script/URL (optional) --------------------------------------------------------------------------------------- diff --git a/data/VisDrone.yaml b/data/VisDrone.yaml index 12e0e7c4a009..fe6cb9199ce1 100644 --- a/data/VisDrone.yaml +++ b/data/VisDrone.yaml @@ -15,7 +15,7 @@ test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images # Classes nc: 10 # number of classes -names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ] +names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor'] # Download script/URL (optional) --------------------------------------------------------------------------------------- diff --git a/data/coco.yaml b/data/coco.yaml index cab1a0171963..acf8e84f3e21 100644 --- a/data/coco.yaml +++ b/data/coco.yaml @@ -15,15 +15,15 @@ test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions. # Classes nc: 80 # number of classes -names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', - 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', - 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', - 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', - 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', - 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', - 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', - 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', - 'hair drier', 'toothbrush' ] # class names +names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush'] # class names # Download script/URL (optional) diff --git a/data/coco128.yaml b/data/coco128.yaml index 6902eb9397a1..eda39dcdaa8d 100644 --- a/data/coco128.yaml +++ b/data/coco128.yaml @@ -15,15 +15,15 @@ test: # test images (optional) # Classes nc: 80 # number of classes -names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', - 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', - 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', - 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', - 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', - 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', - 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', - 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', - 'hair drier', 'toothbrush' ] # class names +names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush'] # class names # Download script/URL (optional) diff --git a/data/scripts/get_coco.sh b/data/scripts/get_coco.sh index 1f484beee34c..f6c075689709 100755 --- a/data/scripts/get_coco.sh +++ b/data/scripts/get_coco.sh @@ -12,7 +12,7 @@ d='../datasets' # unzip directory url=https://github.com/ultralytics/yolov5/releases/download/v1.0/ f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB echo 'Downloading' $url$f ' ...' -curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background +curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # Download/unzip images d='../datasets/coco/images' # unzip directory @@ -22,6 +22,6 @@ f2='val2017.zip' # 1G, 5k images f3='test2017.zip' # 7G, 41k images (optional) for f in $f1 $f2; do echo 'Downloading' $url$f '...' - curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background + curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & done wait # finish background tasks diff --git a/data/scripts/get_coco128.sh b/data/scripts/get_coco128.sh index 3d705890b56d..6eb47bfe5595 100644 --- a/data/scripts/get_coco128.sh +++ b/data/scripts/get_coco128.sh @@ -12,6 +12,6 @@ d='../datasets' # unzip directory url=https://github.com/ultralytics/yolov5/releases/download/v1.0/ f='coco128.zip' # or 'coco2017labels-segments.zip', 68 MB echo 'Downloading' $url$f ' ...' -curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background +curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & wait # finish background tasks diff --git a/data/xView.yaml b/data/xView.yaml index f4f27bfbc8ec..e191188da0f0 100644 --- a/data/xView.yaml +++ b/data/xView.yaml @@ -15,15 +15,15 @@ val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 tr # Classes nc: 60 # number of classes -names: [ 'Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus', - 'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer', - 'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car', - 'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge', - 'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane', - 'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck', - 'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed', - 'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad', - 'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower' ] # class names +names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus', + 'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer', + 'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car', + 'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge', + 'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane', + 'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck', + 'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed', + 'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad', + 'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower'] # class names # Download script/URL (optional) --------------------------------------------------------------------------------------- diff --git a/models/hub/anchors.yaml b/models/hub/anchors.yaml index a07a4dc72387..57512955ac1f 100644 --- a/models/hub/anchors.yaml +++ b/models/hub/anchors.yaml @@ -4,55 +4,55 @@ # P5 ------------------------------------------------------------------------------------------------------------------- # P5-640: anchors_p5_640: - - [ 10,13, 16,30, 33,23 ] # P3/8 - - [ 30,61, 62,45, 59,119 ] # P4/16 - - [ 116,90, 156,198, 373,326 ] # P5/32 + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 # P6 ------------------------------------------------------------------------------------------------------------------- # P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 anchors_p6_640: - - [ 9,11, 21,19, 17,41 ] # P3/8 - - [ 43,32, 39,70, 86,64 ] # P4/16 - - [ 65,131, 134,130, 120,265 ] # P5/32 - - [ 282,180, 247,354, 512,387 ] # P6/64 + - [9,11, 21,19, 17,41] # P3/8 + - [43,32, 39,70, 86,64] # P4/16 + - [65,131, 134,130, 120,265] # P5/32 + - [282,180, 247,354, 512,387] # P6/64 # P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 anchors_p6_1280: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 # P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 anchors_p6_1920: - - [ 28,41, 67,59, 57,141 ] # P3/8 - - [ 144,103, 129,227, 270,205 ] # P4/16 - - [ 209,452, 455,396, 358,812 ] # P5/32 - - [ 653,922, 1109,570, 1387,1187 ] # P6/64 + - [28,41, 67,59, 57,141] # P3/8 + - [144,103, 129,227, 270,205] # P4/16 + - [209,452, 455,396, 358,812] # P5/32 + - [653,922, 1109,570, 1387,1187] # P6/64 # P7 ------------------------------------------------------------------------------------------------------------------- # P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 anchors_p7_640: - - [ 11,11, 13,30, 29,20 ] # P3/8 - - [ 30,46, 61,38, 39,92 ] # P4/16 - - [ 78,80, 146,66, 79,163 ] # P5/32 - - [ 149,150, 321,143, 157,303 ] # P6/64 - - [ 257,402, 359,290, 524,372 ] # P7/128 + - [11,11, 13,30, 29,20] # P3/8 + - [30,46, 61,38, 39,92] # P4/16 + - [78,80, 146,66, 79,163] # P5/32 + - [149,150, 321,143, 157,303] # P6/64 + - [257,402, 359,290, 524,372] # P7/128 # P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 anchors_p7_1280: - - [ 19,22, 54,36, 32,77 ] # P3/8 - - [ 70,83, 138,71, 75,173 ] # P4/16 - - [ 165,159, 148,334, 375,151 ] # P5/32 - - [ 334,317, 251,626, 499,474 ] # P6/64 - - [ 750,326, 534,814, 1079,818 ] # P7/128 + - [19,22, 54,36, 32,77] # P3/8 + - [70,83, 138,71, 75,173] # P4/16 + - [165,159, 148,334, 375,151] # P5/32 + - [334,317, 251,626, 499,474] # P6/64 + - [750,326, 534,814, 1079,818] # P7/128 # P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 anchors_p7_1920: - - [ 29,34, 81,55, 47,115 ] # P3/8 - - [ 105,124, 207,107, 113,259 ] # P4/16 - - [ 247,238, 222,500, 563,227 ] # P5/32 - - [ 501,476, 376,939, 749,711 ] # P6/64 - - [ 1126,489, 801,1222, 1618,1227 ] # P7/128 + - [29,34, 81,55, 47,115] # P3/8 + - [105,124, 207,107, 113,259] # P4/16 + - [247,238, 222,500, 563,227] # P5/32 + - [501,476, 376,939, 749,711] # P6/64 + - [1126,489, 801,1222, 1618,1227] # P7/128 diff --git a/models/hub/yolov3-spp.yaml b/models/hub/yolov3-spp.yaml index 0ca7b7f6577b..ddc0549f50d6 100644 --- a/models/hub/yolov3-spp.yaml +++ b/models/hub/yolov3-spp.yaml @@ -3,47 +3,47 @@ nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - - [ 10,13, 16,30, 33,23 ] # P3/8 - - [ 30,61, 62,45, 59,119 ] # P4/16 - - [ 116,90, 156,198, 373,326 ] # P5/32 + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 # darknet53 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Conv, [ 32, 3, 1 ] ], # 0 - [ -1, 1, Conv, [ 64, 3, 2 ] ], # 1-P1/2 - [ -1, 1, Bottleneck, [ 64 ] ], - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 3-P2/4 - [ -1, 2, Bottleneck, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 5-P3/8 - [ -1, 8, Bottleneck, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 7-P4/16 - [ -1, 8, Bottleneck, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P5/32 - [ -1, 4, Bottleneck, [ 1024 ] ], # 10 + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 ] # YOLOv3-SPP head head: - [ [ -1, 1, Bottleneck, [ 1024, False ] ], - [ -1, 1, SPP, [ 512, [ 5, 9, 13 ] ] ], - [ -1, 1, Conv, [ 1024, 3, 1 ] ], - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, Conv, [ 1024, 3, 1 ] ], # 15 (P5/32-large) + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, SPP, [512, [5, 9, 13]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) - [ -2, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 1, Bottleneck, [ 512, False ] ], - [ -1, 1, Bottleneck, [ 512, False ] ], - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, Conv, [ 512, 3, 1 ] ], # 22 (P4/16-medium) + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) - [ -2, 1, Conv, [ 128, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 1, Bottleneck, [ 256, False ] ], - [ -1, 2, Bottleneck, [ 256, False ] ], # 27 (P3/8-small) + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) - [ [ 27, 22, 15 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5) + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] diff --git a/models/hub/yolov3-tiny.yaml b/models/hub/yolov3-tiny.yaml index d39a6b1f581c..537ad755b166 100644 --- a/models/hub/yolov3-tiny.yaml +++ b/models/hub/yolov3-tiny.yaml @@ -3,37 +3,37 @@ nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - - [ 10,14, 23,27, 37,58 ] # P4/16 - - [ 81,82, 135,169, 344,319 ] # P5/32 + - [10,14, 23,27, 37,58] # P4/16 + - [81,82, 135,169, 344,319] # P5/32 # YOLOv3-tiny backbone backbone: # [from, number, module, args] - [ [ -1, 1, Conv, [ 16, 3, 1 ] ], # 0 - [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 1-P1/2 - [ -1, 1, Conv, [ 32, 3, 1 ] ], - [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 3-P2/4 - [ -1, 1, Conv, [ 64, 3, 1 ] ], - [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 5-P3/8 - [ -1, 1, Conv, [ 128, 3, 1 ] ], - [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 7-P4/16 - [ -1, 1, Conv, [ 256, 3, 1 ] ], - [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 9-P5/32 - [ -1, 1, Conv, [ 512, 3, 1 ] ], - [ -1, 1, nn.ZeroPad2d, [ [ 0, 1, 0, 1 ] ] ], # 11 - [ -1, 1, nn.MaxPool2d, [ 2, 1, 0 ] ], # 12 + [[-1, 1, Conv, [16, 3, 1]], # 0 + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 + [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 ] # YOLOv3-tiny head head: - [ [ -1, 1, Conv, [ 1024, 3, 1 ] ], - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, Conv, [ 512, 3, 1 ] ], # 15 (P5/32-large) + [[-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) - [ -2, 1, Conv, [ 128, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 1, Conv, [ 256, 3, 1 ] ], # 19 (P4/16-medium) + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) - [ [ 19, 15 ], 1, Detect, [ nc, anchors ] ], # Detect(P4, P5) + [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) ] diff --git a/models/hub/yolov3.yaml b/models/hub/yolov3.yaml index 09df0d9ef362..3adfc2c6d2f9 100644 --- a/models/hub/yolov3.yaml +++ b/models/hub/yolov3.yaml @@ -3,47 +3,47 @@ nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - - [ 10,13, 16,30, 33,23 ] # P3/8 - - [ 30,61, 62,45, 59,119 ] # P4/16 - - [ 116,90, 156,198, 373,326 ] # P5/32 + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 # darknet53 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Conv, [ 32, 3, 1 ] ], # 0 - [ -1, 1, Conv, [ 64, 3, 2 ] ], # 1-P1/2 - [ -1, 1, Bottleneck, [ 64 ] ], - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 3-P2/4 - [ -1, 2, Bottleneck, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 5-P3/8 - [ -1, 8, Bottleneck, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 7-P4/16 - [ -1, 8, Bottleneck, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P5/32 - [ -1, 4, Bottleneck, [ 1024 ] ], # 10 + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 ] # YOLOv3 head head: - [ [ -1, 1, Bottleneck, [ 1024, False ] ], - [ -1, 1, Conv, [ 512, [ 1, 1 ] ] ], - [ -1, 1, Conv, [ 1024, 3, 1 ] ], - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, Conv, [ 1024, 3, 1 ] ], # 15 (P5/32-large) + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, Conv, [512, [1, 1]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) - [ -2, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 1, Bottleneck, [ 512, False ] ], - [ -1, 1, Bottleneck, [ 512, False ] ], - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, Conv, [ 512, 3, 1 ] ], # 22 (P4/16-medium) + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) - [ -2, 1, Conv, [ 128, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 1, Bottleneck, [ 256, False ] ], - [ -1, 2, Bottleneck, [ 256, False ] ], # 27 (P3/8-small) + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) - [ [ 27, 22, 15 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5) + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] diff --git a/models/hub/yolov5-fpn.yaml b/models/hub/yolov5-fpn.yaml index b8b7fc1a23d4..217e4ca6ac96 100644 --- a/models/hub/yolov5-fpn.yaml +++ b/models/hub/yolov5-fpn.yaml @@ -3,38 +3,38 @@ nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - - [ 10,13, 16,30, 33,23 ] # P3/8 - - [ 30,61, 62,45, 59,119 ] # P4/16 - - [ 116,90, 156,198, 373,326 ] # P5/32 + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, Bottleneck, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, BottleneckCSP, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, BottleneckCSP, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 - [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ], - [ -1, 6, BottleneckCSP, [ 1024 ] ], # 9 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 6, BottleneckCSP, [1024]], # 9 ] # YOLOv5 FPN head head: - [ [ -1, 3, BottleneckCSP, [ 1024, False ] ], # 10 (P5/32-large) + [[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large) - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 3, BottleneckCSP, [ 512, False ] ], # 14 (P4/16-medium) + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [512, 1, 1]], + [-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium) - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 3, BottleneckCSP, [ 256, False ] ], # 18 (P3/8-small) + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Conv, [256, 1, 1]], + [-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small) - [ [ 18, 14, 10 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5) + [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] diff --git a/models/hub/yolov5-p2.yaml b/models/hub/yolov5-p2.yaml index 62122363df2d..6a932a868229 100644 --- a/models/hub/yolov5-p2.yaml +++ b/models/hub/yolov5-p2.yaml @@ -7,46 +7,46 @@ anchors: 3 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 - [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 9 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, C3, [1024, False]], # 9 ] # YOLOv5 head head: - [ [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 13 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) - - [ -1, 1, Conv, [ 128, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 2 ], 1, Concat, [ 1 ] ], # cat backbone P2 - [ -1, 1, C3, [ 128, False ] ], # 21 (P2/4-xsmall) - - [ -1, 1, Conv, [ 128, 3, 2 ] ], - [ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P3 - [ -1, 3, C3, [ 256, False ] ], # 24 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 27 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 1024, False ] ], # 30 (P5/32-large) - - [ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5) + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 2], 1, Concat, [1]], # cat backbone P2 + [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P3 + [-1, 3, C3, [256, False]], # 24 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 27 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 30 (P5/32-large) + + [[24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] diff --git a/models/hub/yolov5-p6.yaml b/models/hub/yolov5-p6.yaml index c5ef5177f0c8..58b86b0ca892 100644 --- a/models/hub/yolov5-p6.yaml +++ b/models/hub/yolov5-p6.yaml @@ -7,48 +7,48 @@ anchors: 3 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 1, SPP, [1024, [3, 5, 7]]], + [-1, 3, C3, [1024, False]], # 11 ] # YOLOv5 head head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P5/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P5/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] diff --git a/models/hub/yolov5-p7.yaml b/models/hub/yolov5-p7.yaml index 505c590ca168..f6e8fc7928cc 100644 --- a/models/hub/yolov5-p7.yaml +++ b/models/hub/yolov5-p7.yaml @@ -7,59 +7,59 @@ anchors: 3 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 3, C3, [ 1024 ] ], - [ -1, 1, Conv, [ 1280, 3, 2 ] ], # 11-P7/128 - [ -1, 1, SPP, [ 1280, [ 3, 5 ] ] ], - [ -1, 3, C3, [ 1280, False ] ], # 13 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 + [-1, 1, SPP, [1280, [3, 5]]], + [-1, 3, C3, [1280, False]], # 13 ] # YOLOv5 head head: - [ [ -1, 1, Conv, [ 1024, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat backbone P6 - [ -1, 3, C3, [ 1024, False ] ], # 17 + [[-1, 1, Conv, [1024, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 10], 1, Concat, [1]], # cat backbone P6 + [-1, 3, C3, [1024, False]], # 17 - [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 21 + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 21 - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 25 + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 25 - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 29 (P3/8-small) + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 29 (P3/8-small) - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 26 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 32 (P4/16-medium) + [-1, 1, Conv, [256, 3, 2]], + [[-1, 26], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 32 (P4/16-medium) - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 22 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 35 (P5/32-large) + [-1, 1, Conv, [512, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 35 (P5/32-large) - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 38 (P6/64-xlarge) + [-1, 1, Conv, [768, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) - [ -1, 1, Conv, [ 1024, 3, 2 ] ], - [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P7 - [ -1, 3, C3, [ 1280, False ] ], # 41 (P7/128-xxlarge) + [-1, 1, Conv, [1024, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P7 + [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) - [ [ 29, 32, 35, 38, 41 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6, P7) + [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) ] diff --git a/models/hub/yolov5-panet.yaml b/models/hub/yolov5-panet.yaml index aee5dab01fa1..c5f3b4817102 100644 --- a/models/hub/yolov5-panet.yaml +++ b/models/hub/yolov5-panet.yaml @@ -3,44 +3,44 @@ nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - - [ 10,13, 16,30, 33,23 ] # P3/8 - - [ 30,61, 62,45, 59,119 ] # P4/16 - - [ 116,90, 156,198, 373,326 ] # P5/32 + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, BottleneckCSP, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, BottleneckCSP, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, BottleneckCSP, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 - [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ], - [ -1, 3, BottleneckCSP, [ 1024, False ] ], # 9 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 ] # YOLOv5 PANet head head: - [ [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, BottleneckCSP, [ 512, False ] ], # 13 + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, BottleneckCSP, [ 256, False ] ], # 17 (P3/8-small) + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, BottleneckCSP, [ 512, False ] ], # 20 (P4/16-medium) + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, BottleneckCSP, [ 1024, False ] ], # 23 (P5/32-large) + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) - [ [ 17, 20, 23 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5) + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] diff --git a/models/hub/yolov5l6.yaml b/models/hub/yolov5l6.yaml index 91c57da1939e..d5afd7d84100 100644 --- a/models/hub/yolov5l6.yaml +++ b/models/hub/yolov5l6.yaml @@ -3,56 +3,56 @@ nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple anchors: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 1, SPP, [1024, [3, 5, 7]]], + [-1, 3, C3, [1024, False]], # 11 ] # YOLOv5 head head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] diff --git a/models/hub/yolov5m6.yaml b/models/hub/yolov5m6.yaml index 4bef2e074a96..16a841a0b4b0 100644 --- a/models/hub/yolov5m6.yaml +++ b/models/hub/yolov5m6.yaml @@ -3,56 +3,56 @@ nc: 80 # number of classes depth_multiple: 0.67 # model depth multiple width_multiple: 0.75 # layer channel multiple anchors: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 1, SPP, [1024, [3, 5, 7]]], + [-1, 3, C3, [1024, False]], # 11 ] # YOLOv5 head head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] diff --git a/models/hub/yolov5s-transformer.yaml b/models/hub/yolov5s-transformer.yaml index 8023ba480d24..b999ebb7583d 100644 --- a/models/hub/yolov5s-transformer.yaml +++ b/models/hub/yolov5s-transformer.yaml @@ -3,44 +3,44 @@ nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: - - [ 10,13, 16,30, 33,23 ] # P3/8 - - [ 30,61, 62,45, 59,119 ] # P4/16 - - [ 116,90, 156,198, 373,326 ] # P5/32 + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 - [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ], - [ -1, 3, C3TR, [ 1024, False ] ], # 9 <-------- C3TR() Transformer module + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module ] # YOLOv5 head head: - [ [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 13 + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 1024, False ] ], # 23 (P5/32-large) + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - [ [ 17, 20, 23 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5) + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] diff --git a/models/hub/yolov5s6.yaml b/models/hub/yolov5s6.yaml index ba1025ec87ad..2fb245050053 100644 --- a/models/hub/yolov5s6.yaml +++ b/models/hub/yolov5s6.yaml @@ -3,56 +3,56 @@ nc: 80 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple anchors: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 1, SPP, [1024, [3, 5, 7]]], + [-1, 3, C3, [1024, False]], # 11 ] # YOLOv5 head head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] diff --git a/models/hub/yolov5x6.yaml b/models/hub/yolov5x6.yaml index 4fc9c9a119b8..c5187101072b 100644 --- a/models/hub/yolov5x6.yaml +++ b/models/hub/yolov5x6.yaml @@ -3,56 +3,56 @@ nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple anchors: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 # YOLOv5 backbone backbone: # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 1, SPP, [1024, [3, 5, 7]]], + [-1, 3, C3, [1024, False]], # 11 ] # YOLOv5 head head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) ] diff --git a/train.py b/train.py index 7a8c15a6551a..3f5b5ed1195b 100644 --- a/train.py +++ b/train.py @@ -74,7 +74,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary with open(save_dir / 'opt.yaml', 'w') as f: yaml.safe_dump(vars(opt), f, sort_keys=False) data_dict = None - + # Loggers if RANK in [-1, 0]: loggers = Loggers(save_dir, weights, opt, hyp, LOGGER).start() # loggers dict @@ -83,7 +83,6 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary if resume: weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp - # Config plots = not evolve # create plots cuda = device.type != 'cpu' @@ -96,7 +95,6 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check is_coco = data.endswith('coco.yaml') and nc == 80 # COCO dataset - # Model pretrained = weights.endswith('.pt') if pretrained: diff --git a/utils/downloads.py b/utils/downloads.py index 00156962380b..588db5170e0e 100644 --- a/utils/downloads.py +++ b/utils/downloads.py @@ -115,7 +115,6 @@ def get_token(cookie="./cookie"): return line.split()[-1] return "" - # Google utils: https://cloud.google.com/storage/docs/reference/libraries ---------------------------------------------- # # diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index 603837d57052..06d562d60f99 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -1,7 +1,8 @@ # YOLOv5 experiment logging utils -import torch import warnings from threading import Thread + +import torch from torch.utils.tensorboard import SummaryWriter from utils.general import colorstr, emojis diff --git a/utils/loggers/wandb/log_dataset.py b/utils/loggers/wandb/log_dataset.py index 1328e20806ef..8447272cdb48 100644 --- a/utils/loggers/wandb/log_dataset.py +++ b/utils/loggers/wandb/log_dataset.py @@ -1,5 +1,4 @@ import argparse -import yaml from wandb_utils import WandbLogger diff --git a/utils/loggers/wandb/sweep.py b/utils/loggers/wandb/sweep.py index a0c76a10caa1..8e952d03c085 100644 --- a/utils/loggers/wandb/sweep.py +++ b/utils/loggers/wandb/sweep.py @@ -1,7 +1,8 @@ import sys -import wandb from pathlib import Path +import wandb + FILE = Path(__file__).absolute() sys.path.append(FILE.parents[2].as_posix()) # add utils/ to path diff --git a/utils/loggers/wandb/sweep.yaml b/utils/loggers/wandb/sweep.yaml index dcc95264f8cd..c3727de82d4a 100644 --- a/utils/loggers/wandb/sweep.yaml +++ b/utils/loggers/wandb/sweep.yaml @@ -25,9 +25,9 @@ parameters: data: value: "data/coco128.yaml" batch_size: - values: [ 64 ] + values: [64] epochs: - values: [ 10 ] + values: [10] lr0: distribution: uniform diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py index c978e3ea838d..66fa8f85ec4e 100644 --- a/utils/loggers/wandb/wandb_utils.py +++ b/utils/loggers/wandb/wandb_utils.py @@ -3,9 +3,10 @@ import logging import os import sys -import yaml from contextlib import contextmanager from pathlib import Path + +import yaml from tqdm import tqdm FILE = Path(__file__).absolute() diff --git a/val.py b/val.py index ee2287644b92..06b2501515b5 100644 --- a/val.py +++ b/val.py @@ -13,7 +13,6 @@ import numpy as np import torch -import yaml from tqdm import tqdm FILE = Path(__file__).absolute()