Merge branch 'develop' into tf-only-export

zldrobit · Jun 2, 2021 · 9ca5d7a · 9ca5d7a
2 parents d3be281 + fdbe527
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diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
@@ -0,0 +1,5 @@
+# These are supported funding model platforms
+
+github: glenn-jocher
+patreon: ultralytics
+open_collective: ultralytics
diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml
@@ -2,12 +2,10 @@ name: CI CPU testing
 
 on:  # https://help.github.com/en/actions/reference/events-that-trigger-workflows
   push:
-    branches: [ master ]
+    branches: [ master, develop ]
   pull_request:
     # The branches below must be a subset of the branches above
-    branches: [ master ]
-  schedule:
-    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
+    branches: [ master, develop ]
 
 jobs:
   cpu-tests:

diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml
@@ -16,7 +16,7 @@ jobs:
             git remote add upstream https://github.com/ultralytics/yolov5.git
             git fetch upstream
             git checkout feature  # <----- replace 'feature' with local branch name
-            git rebase upstream/master
+            git rebase upstream/develop
             git push -u origin -f
             ```
             - ✅ Verify all Continuous Integration (CI) **checks are passing**.

diff --git a/Dockerfile b/Dockerfile
@@ -8,7 +8,7 @@ RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
 COPY requirements.txt .
 RUN python -m pip install --upgrade pip
 RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
-RUN pip install --no-cache -r requirements.txt gsutil notebook
+RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook
 
 # Create working directory
 RUN mkdir -p /usr/src/app

diff --git a/README.md b/README.md
@@ -1,46 +1,64 @@
-<a href="https://apps.apple.com/app/id1452689527" target="_blank">
-<img src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg" width="1000"></a>
+<a align="left" href="https://apps.apple.com/app/id1452689527" target="_blank">
+<img width="800" src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg"></a>
 &nbsp
 
 <a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
 
 This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
 
-<img src="https://user-images.githubusercontent.com/26833433/103594689-455e0e00-4eae-11eb-9cdf-7d753e2ceeeb.png" width="1000">** 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.
-
-- **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration.
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
+</details>
+<details>
+  <summary>Figure Notes (click to expand)</summary>
+
+  * 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 test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+</details>
+
+- **April 11, 2021**: [v5.0 release](https://github.com/ultralytics/yolov5/releases/tag/v5.0): YOLOv5-P6 1280 models, [AWS](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart), [Supervise.ly](https://github.com/ultralytics/yolov5/issues/2518) and [YouTube](https://github.com/ultralytics/yolov5/pull/2752) integrations.
+- **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration.
 - **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP.
 - **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP.
-- **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972).
-- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).
 
 
 ## Pretrained Checkpoints
 
-| Model | size | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>V100</sub> | FPS<sub>V100</sub> || params | GFLOPS |
-|---------- |------ |------ |------ |------ | -------- | ------| ------ |------  |  :------: |
-| [YOLOv5s](https://github.com/ultralytics/yolov5/releases)    |640 |36.8     |36.8     |55.6     |**2.2ms** |**455** ||7.3M   |17.0
-| [YOLOv5m](https://github.com/ultralytics/yolov5/releases)    |640 |44.5     |44.5     |63.1     |2.9ms     |345     ||21.4M  |51.3
-| [YOLOv5l](https://github.com/ultralytics/yolov5/releases)    |640 |48.1     |48.1     |66.4     |3.8ms     |264     ||47.0M  |115.4
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases)    |640 |**50.1** |**50.1** |**68.7** |6.0ms     |167     ||87.7M  |218.8
-| | | | | | | || |
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA |832 |**51.9** |**51.9** |**69.6** |24.9ms |40      ||87.7M  |1005.3
-
-<!--- 
-| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases)   |640 |49.0     |49.0     |67.4     |4.1ms     |244     ||77.2M  |117.7
-| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases)   |1280 |53.0     |53.0     |70.8     |12.3ms     |81     ||77.2M  |117.7
---->
-
-** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.  
-** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`  
-** Speed<sub>GPU</sub> 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 image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img.  **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`  
-** 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)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` 
+[assets]: https://github.com/ultralytics/yolov5/releases
+
+Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
+---   |---  |---        |---         |---             |---                |---|---              |---
+[YOLOv5s][assets]    |640  |36.7     |36.7     |55.4     |**2.0** | |7.3   |17.0
+[YOLOv5m][assets]    |640  |44.5     |44.5     |63.1     |2.7     | |21.4  |51.3
+[YOLOv5l][assets]    |640  |48.2     |48.2     |66.9     |3.8     | |47.0  |115.4
+[YOLOv5x][assets]    |640  |**50.4** |**50.4** |**68.8** |6.1     | |87.7  |218.8
+| | | | | | || |
+[YOLOv5s6][assets]   |1280 |43.3     |43.3     |61.9     |**4.3** | |12.7  |17.4
+[YOLOv5m6][assets]   |1280 |50.5     |50.5     |68.7     |8.4     | |35.9  |52.4
+[YOLOv5l6][assets]   |1280 |53.4     |53.4     |71.1     |12.3    | |77.2  |117.7
+[YOLOv5x6][assets]   |1280 |**54.4** |**54.4** |**72.0** |22.4    | |141.8 |222.9
+| | | | | | || |
+[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |-  |-
+
+<details>
+  <summary>Table Notes (click to expand)</summary>
+
+  * AP<sup>test</sup> 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 test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`  
+  * Speed<sub>GPU</sub> 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 test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`  
+  * 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 test.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+</details>
 
 
 ## Requirements
 
 Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
+<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
 ```bash
 $ pip install -r requirements.txt
 ```
@@ -49,11 +67,12 @@ $ pip install -r requirements.txt
 ## Tutorials
 
 * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)&nbsp; 🚀 RECOMMENDED
+* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️ RECOMMENDED
 * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW
 * [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518)&nbsp; 🌟 NEW
 * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
 * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ NEW
-* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)
+* [TorchScript, ONNX, CoreML Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
 * [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
 * [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
 * [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
@@ -74,16 +93,15 @@ YOLOv5 may be run in any of the following up-to-date verified environments (with
 
 ## Inference
 
-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 
                             file.mp4  # video
                             path/  # directory
                             path/*.jpg  # glob
-                            rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa  # rtsp stream
-                            rtmp://192.168.1.105/live/test  # rtmp stream
-                            http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8  # http stream
+                            'https://youtu.be/NUsoVlDFqZg'  # YouTube video
+                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 ```
 
 To run inference on example images in `data/images`:
@@ -100,23 +118,22 @@ image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done
 Results saved to runs/detect/exp2
 Done. (0.103s)
 ```
-<img src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg" width="500">  
+<img width="500" src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg">  
 
 ### PyTorch Hub
 
-To run **batched inference** with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
+Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
 ```python
 import torch
 
 # Model
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
 
-# Images
-dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'
-imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')]  # batch of images
+# Image
+img = 'https://ultralytics.com/images/zidane.jpg'
 
 # Inference
-results = model(imgs)
+results = model(img)
 results.print()  # or .show(), .save()
 ```
 
@@ -130,7 +147,7 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size
                                          yolov5l                                24
                                          yolov5x                                16
 ```
-<img src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" width="900">
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
 
 
 ## Citation
@@ -145,9 +162,9 @@ Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of
 - **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
 - **Custom data training**, hyperparameter evolution, and model exportation to any destination.
 
-For business inquiries and professional support requests please visit us at https://www.ultralytics.com. 
+For business inquiries and professional support requests please visit us at https://ultralytics.com. 
 
 
 ## Contact
 
-**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at [email protected]. 
+**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at [email protected]. 
diff --git a/__init__.py b/__init__.py
diff --git a/data/GlobalWheat2020.yaml b/data/GlobalWheat2020.yaml
@@ -0,0 +1,55 @@
+# Global Wheat 2020 dataset http://www.global-wheat.com/
+# Train command: python train.py --data GlobalWheat2020.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /datasets/GlobalWheat2020
+#     /yolov5
+
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: # 3422 images
+  - ../datasets/GlobalWheat2020/images/arvalis_1
+  - ../datasets/GlobalWheat2020/images/arvalis_2
+  - ../datasets/GlobalWheat2020/images/arvalis_3
+  - ../datasets/GlobalWheat2020/images/ethz_1
+  - ../datasets/GlobalWheat2020/images/rres_1
+  - ../datasets/GlobalWheat2020/images/inrae_1
+  - ../datasets/GlobalWheat2020/images/usask_1
+
+val: # 748 images (WARNING: train set contains ethz_1)
+  - ../datasets/GlobalWheat2020/images/ethz_1
+
+test: # 1276 images
+  - ../datasets/GlobalWheat2020/images/utokyo_1
+  - ../datasets/GlobalWheat2020/images/utokyo_2
+  - ../datasets/GlobalWheat2020/images/nau_1
+  - ../datasets/GlobalWheat2020/images/uq_1
+
+# number of classes
+nc: 1
+
+# class names
+names: [ 'wheat_head' ]
+
+
+# download command/URL (optional) --------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, Path
+
+  # Download
+  dir = Path('../datasets/GlobalWheat2020')  # dataset directory
+  urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
+  download(urls, dir=dir)
+
+  # Make Directories
+  for p in 'annotations', 'images', 'labels':
+      (dir / p).mkdir(parents=True, exist_ok=True)
+
+  # Move
+  for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+           'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+      (dir / p).rename(dir / 'images' / p)  # move to /images
+      f = (dir / p).with_suffix('.json')  # json file
+      if f.exists():
+          f.rename((dir / 'annotations' / p).with_suffix('.json'))  # move to /annotations
diff --git a/data/SKU-110K.yaml b/data/SKU-110K.yaml
@@ -0,0 +1,52 @@
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
+# Train command: python train.py --data SKU-110K.yaml
+# Default dataset location is next to YOLOv5:
+#   /parent_folder
+#     /datasets/SKU-110K
+#     /yolov5
+
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: ../datasets/SKU-110K/train.txt  # 8219 images
+val: ../datasets/SKU-110K/val.txt  # 588 images
+test: ../datasets/SKU-110K/test.txt  # 2936 images
+
+# number of classes
+nc: 1
+
+# class names
+names: [ 'object' ]
+
+
+# download command/URL (optional) --------------------------------------------------------------------------------------
+download: |
+  import shutil
+  from tqdm import tqdm
+  from utils.general import np, pd, Path, download, xyxy2xywh
+
+  # Download
+  datasets = Path('../datasets')  # download directory
+  urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+  download(urls, dir=datasets, delete=False)
+
+  # Rename directories
+  dir = (datasets / 'SKU-110K')
+  if dir.exists():
+      shutil.rmtree(dir)
+  (datasets / 'SKU110K_fixed').rename(dir)  # rename dir
+  (dir / 'labels').mkdir(parents=True, exist_ok=True)  # create labels dir
+
+  # Convert labels
+  names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height'  # column names
+  for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+      x = pd.read_csv(dir / 'annotations' / d, names=names).values  # annotations
+      images, unique_images = x[:, 0], np.unique(x[:, 0])
+      with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+          f.writelines(f'./images/{s}\n' for s in unique_images)
+      for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+          cls = 0  # single-class dataset
+          with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+              for r in x[images == im]:
+                  w, h = r[6], r[7]  # image width, height
+                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
+                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label