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ultralytics · Mar 10, 2021 · 8530f33 · 8530f33
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diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml
@@ -74,6 +74,7 @@ jobs:
           python test.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --device $di
           python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
 
+          python hubconf.py  # hub
           python models/yolo.py --cfg models/${{ matrix.model }}.yaml  # inspect
           python models/export.py --img 128 --batch 1 --weights weights/${{ matrix.model }}.pt  # export
         shell: bash
diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml
@@ -11,7 +11,7 @@ jobs:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           pr-message: |
             👋 Hello @${{ github.actor }}, thank you for submitting a 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
-            - ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master update by running the following, replacing 'feature' with the name of your local branch:
+            - ✅ 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
@@ -42,10 +42,11 @@ jobs:
             
             YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
             
-            - **Google Colab Notebook** with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-            - **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)
-            - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) 
-            - **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)
+            - **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+            - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
+            - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
+            - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+            
             
             ## Status
             

diff --git a/Dockerfile b/Dockerfile
@@ -1,13 +1,13 @@
 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:20.12-py3
+FROM nvcr.io/nvidia/pytorch:21.02-py3
 
 # Install linux packages
-RUN apt update && apt install -y screen libgl1-mesa-glx
+RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
 
 # Install python dependencies
-RUN pip install --upgrade pip
 COPY requirements.txt .
-RUN pip install -r requirements.txt gsutil
+RUN python -m pip install --upgrade pip
+RUN pip install --no-cache -r requirements.txt gsutil notebook
 
 # Create working directory
 RUN mkdir -p /usr/src/app
@@ -16,11 +16,8 @@ WORKDIR /usr/src/app
 # Copy contents
 COPY . /usr/src/app
 
-# Copy weights
-#RUN python3 -c "from models import *; \
-#attempt_download('weights/yolov5s.pt'); \
-#attempt_download('weights/yolov5m.pt'); \
-#attempt_download('weights/yolov5l.pt')"
+# Set environment variables
+ENV HOME=/usr/src/app
 
 
 # ---------------------------------------------------  Extras Below  ---------------------------------------------------
@@ -39,13 +36,13 @@ COPY . /usr/src/app
 # sudo docker kill $(sudo docker ps -q)
 
 # Kill all image-based
-# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov5:latest)
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
 
 # Bash into running container
 # sudo docker exec -it 5a9b5863d93d bash
 
 # Bash into stopped container
-# id=5a9b5863d93d && sudo docker start $id && sudo docker exec -it $id bash
+# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
 
 # Send weights to GCP
 # python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt

diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
 <img src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg" width="1000"></a>
 &nbsp
 
-![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
+<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.
 
@@ -65,10 +65,10 @@ $ pip install -r requirements.txt
 
 YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
 
-- **Google Colab Notebook** with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)
-- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) 
-- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)
+- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
+- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
+- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
 
 
 ## Inference
@@ -89,17 +89,15 @@ To run inference on example images in `data/images`:
 ```bash
 $ python detect.py --source data/images --weights yolov5s.pt --conf 0.25
 
-Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])
-Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)
-
-Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s]
+Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])
+YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)
 
 Fusing layers... 
-Model Summary: 232 layers, 7459581 parameters, 0 gradients
-image 1/2 data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s)
-image 2/2 data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s)
-Results saved to runs/detect/exp
-Done. (0.113s)
+Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
+image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s)
+image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s)
+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">  
 
@@ -108,18 +106,17 @@ Done. (0.113s)
 To run **batched inference** with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
 ```python
 import torch
-from PIL import Image
 
 # Model
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
 
 # Images
-img1 = Image.open('zidane.jpg')
-img2 = Image.open('bus.jpg')
-imgs = [img1, img2]  # batched list of images
+dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'
+imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')]  # batched list of images
 
 # Inference
-result = model(imgs)
+results = model(imgs)
+results.print()  # or .show(), .save()
 ```
 
 

diff --git a/data/scripts/get_coco.sh b/data/scripts/get_coco.sh
@@ -10,8 +10,9 @@
 # Download/unzip labels
 d='../' # unzip directory
 url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco2017labels.zip'                                                                 # 68 MB
-echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
+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
 
 # Download/unzip images
 d='../coco/images' # unzip directory
@@ -20,5 +21,7 @@ f1='train2017.zip' # 19G, 118k images
 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
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
 done
+wait # finish background tasks
diff --git a/data/scripts/get_voc.sh b/data/scripts/get_voc.sh
@@ -17,9 +17,11 @@ url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
 f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
 f2=VOCtest_06-Nov-2007.zip     # 438MB, 4953 images
 f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
-for f in $f1 $f2 $f3; do
-  echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
+for f in $f3 $f2 $f1; do
+  echo 'Downloading' $url$f '...' 
+  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
 done
+wait # finish background tasks
 
 end=$(date +%s)
 runtime=$((end - start))

diff --git a/detect.py b/detect.py
@@ -9,8 +9,8 @@
 
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
-from utils.general import check_img_size, check_requirements, non_max_suppression, apply_classifier, scale_coords, \
-    xyxy2xywh, strip_optimizer, set_logging, increment_path
+from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
+    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
 from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
@@ -31,7 +31,8 @@ def detect(save_img=False):
 
     # Load model
     model = attempt_load(weights, map_location=device)  # load FP32 model
-    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
+    stride = int(model.stride.max())  # model stride
+    imgsz = check_img_size(imgsz, s=stride)  # check img_size
     if half:
         model.half()  # to FP16
 
@@ -44,21 +45,21 @@ def detect(save_img=False):
     # Set Dataloader
     vid_path, vid_writer = None, None
     if webcam:
-        view_img = True
+        view_img = check_imshow()
         cudnn.benchmark = True  # set True to speed up constant image size inference
-        dataset = LoadStreams(source, img_size=imgsz)
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
     else:
         save_img = True
-        dataset = LoadImages(source, img_size=imgsz)
+        dataset = LoadImages(source, img_size=imgsz, stride=stride)
 
     # Get names and colors
     names = model.module.names if hasattr(model, 'module') else model.names
     colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
 
     # Run inference
+    if device.type != 'cpu':
+        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
     t0 = time.time()
-    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
-    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
     for path, img, im0s, vid_cap in dataset:
         img = torch.from_numpy(img).to(device)
         img = img.half() if half else img.float()  # uint8 to fp16/32
@@ -117,6 +118,7 @@ def detect(save_img=False):
             # Stream results
             if view_img:
                 cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
 
             # Save results (image with detections)
             if save_img:

diff --git a/hubconf.py b/hubconf.py
@@ -120,7 +120,7 @@ def custom(path_or_model='path/to/model.pt', autoshape=True):
     """
     model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model  # load checkpoint
     if isinstance(model, dict):
-        model = model['model']  # load model
+        model = model['ema' if model.get('ema') else 'model']  # load model
 
     hub_model = Model(model.yaml).to(next(model.parameters()).device)  # create
     hub_model.load_state_dict(model.float().state_dict())  # load state_dict
@@ -133,9 +133,14 @@ def custom(path_or_model='path/to/model.pt', autoshape=True):
     # model = custom(path_or_model='path/to/model.pt')  # custom example
 
     # Verify inference
+    import numpy as np
     from PIL import Image
 
-    imgs = [Image.open(x) for x in Path('data/images').glob('*.jpg')]
-    results = model(imgs)
-    results.show()
+    imgs = [Image.open('data/images/bus.jpg'),  # PIL
+            'data/images/zidane.jpg',  # filename
+            'https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg',  # URI
+            np.zeros((640, 480, 3))]  # numpy
+
+    results = model(imgs)  # batched inference
     results.print()
+    results.save()