Merge remote-tracking branch 'origin/master'

README.md

@@ -21,7 +21,7 @@ This repository represents Ultralytics open-source research into future object d
 | [YOLOv5m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | 43.4     | 43.4     | 62.4     | 3.0ms     | 333     || 21.8M  | 39.4B
 | [YOLOv5l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | 46.6     | 46.7     | 65.4     | 3.9ms     | 256     || 47.8M  | 88.1B
 | [YOLOv5x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)    | **48.4** | **48.4** | **66.9** | 6.1ms     | 164     || 89.0M  | 166.4B
-| [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)  | 45.6     | 45.5     | 65.2     | 4.5ms     | 222     || 63.0M  | 118.0B
+| [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6     | 45.5     | 65.2     | 4.5ms     | 222     || 63.0M  | 118.0B
 
 
 ** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.  
@@ -54,10 +54,11 @@ $ pip install -U -r requirements.txt
 
 Inference can be run on most common media formats. Model [checkpoints](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) are downloaded automatically if available. Results are saved to `./inference/output`.
 ```bash
-$ python detect.py --source file.jpg  # image 
+$ python detect.py --source 0  # webcam
+                            file.jpg  # image 
                             file.mp4  # video
-                            ./dir  # directory
-                            0  # webcam
+                            path/  # directory
+                            path/*.jpg  # glob
                             rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa  # rtsp stream
                             http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8  # http stream
 ```

data/coco.yaml

@@ -1,13 +1,13 @@
 # COCO 2017 dataset http://cocodataset.org
 # Download command: bash yolov5/data/get_coco2017.sh
-# Train command: python train.py --data ./data/coco.yaml
-# Dataset should be placed next to yolov5 folder:
+# Train command: python train.py --data coco.yaml
+# Default dataset location is next to /yolov5:
 #   /parent_folder
 #     /coco
 #     /yolov5
 
 
-# train and val datasets (image directory or *.txt file with image paths)
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
 train: ../coco/train2017.txt  # 118k images
 val: ../coco/val2017.txt  # 5k images
 test: ../coco/test-dev2017.txt  # 20k images for submission to https://competitions.codalab.org/competitions/20794

data/coco128.yaml

@@ -1,15 +1,15 @@
 # COCO 2017 dataset http://cocodataset.org - first 128 training images
-# Download command:  python -c "from yolov5.utils.google_utils import gdrive_download; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip')"
-# Train command: python train.py --data ./data/coco128.yaml
-# Dataset should be placed next to yolov5 folder:
+# Download command:  python -c "from yolov5.utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')"
+# Train command: python train.py --data coco128.yaml
+# Default dataset location is next to /yolov5:
 #   /parent_folder
 #     /coco128
 #     /yolov5
 
 
-# train and val datasets (image directory or *.txt file with image paths)
-train: ../coco128/images/train2017/
-val: ../coco128/images/train2017/
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: ../coco128/images/train2017/  # 128 images
+val: ../coco128/images/train2017/  # 128 images
 
 # number of classes
 nc: 80

data/get_coco2017.sh

@@ -1,12 +1,13 @@
 #!/bin/bash
 # COCO 2017 dataset http://cocodataset.org
 # Download command: bash yolov5/data/get_coco2017.sh
-# Train command: python train.py --data ./data/coco.yaml
-# Dataset should be placed next to yolov5 folder:
+# Train command: python train.py --data coco.yaml
+# Default dataset location is next to /yolov5:
 #   /parent_folder
 #     /coco
 #     /yolov5
 
+
 # Download labels from Google Drive, accepting presented query
 filename="coco2017labels.zip"
 fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L"

data/get_voc.sh

@@ -1,11 +1,12 @@
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
 # Download command: bash ./data/get_voc.sh
 # Train command: python train.py --data voc.yaml
-# Dataset should be placed next to yolov5 folder:
+# Default dataset location is next to /yolov5:
 #   /parent_folder
 #     /VOC
 #     /yolov5
 
+
 start=`date +%s`
 
 # handle optional download dir

data/voc.yaml

@@ -1,14 +1,15 @@
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
 # Download command: bash ./data/get_voc.sh
 # Train command: python train.py --data voc.yaml
-# Dataset should be placed next to yolov5 folder:
+# Default dataset location is next to /yolov5:
 #   /parent_folder
 #     /VOC
 #     /yolov5
 
-# train and val datasets (image directory or *.txt file with image paths)
-train: ../VOC/images/train/
-val: ../VOC/images/val/
+
+# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
+train: ../VOC/images/train/  # 16551 images
+val: ../VOC/images/val/  # 4952 images
 
 # number of classes
 nc: 20

test.py

@@ -41,9 +41,9 @@ def test(data,
         #     model = nn.DataParallel(model)
 
     # Half
-    half = device.type != 'cpu' and torch.cuda.device_count() == 1  # half precision only supported on single-GPU
+    half = device.type != 'cpu'  # half precision only supported on CUDA
     if half:
-        model.half()  # to FP16
+        model.half()
 
     # Configure
     model.eval()

utils/datasets.py

@@ -68,35 +68,39 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
 
 class LoadImages:  # for inference
     def __init__(self, path, img_size=640):
-        path = str(Path(path))  # os-agnostic
-        files = []
-        if os.path.isdir(path):
-            files = sorted(glob.glob(os.path.join(path, '*.*')))
-        elif os.path.isfile(path):
-            files = [path]
+        p = str(Path(path))  # os-agnostic
+        p = os.path.abspath(p)  # absolute path
+        if '*' in p:
+            files = sorted(glob.glob(p))  # glob
+        elif os.path.isdir(p):
+            files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir
+        elif os.path.isfile(p):
+            files = [p]  # files
+        else:
+            raise Exception('ERROR: %s does not exist' % p)
 
         images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
         videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
-        nI, nV = len(images), len(videos)
+        ni, nv = len(images), len(videos)
 
         self.img_size = img_size
         self.files = images + videos
-        self.nF = nI + nV  # number of files
-        self.video_flag = [False] * nI + [True] * nV
+        self.nf = ni + nv  # number of files
+        self.video_flag = [False] * ni + [True] * nv
         self.mode = 'images'
         if any(videos):
             self.new_video(videos[0])  # new video
         else:
             self.cap = None
-        assert self.nF > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
-                            (path, img_formats, vid_formats)
+        assert self.nf > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \
+                            (p, img_formats, vid_formats)
 
     def __iter__(self):
         self.count = 0
         return self
 
     def __next__(self):
-        if self.count == self.nF:
+        if self.count == self.nf:
             raise StopIteration
         path = self.files[self.count]
 
@@ -107,7 +111,7 @@ class LoadImages:  # for inference
             if not ret_val:
                 self.count += 1
                 self.cap.release()
-                if self.count == self.nF:  # last video
+                if self.count == self.nf:  # last video
                     raise StopIteration
                 else:
                     path = self.files[self.count]
@@ -115,14 +119,14 @@ class LoadImages:  # for inference
                     ret_val, img0 = self.cap.read()
 
             self.frame += 1
-            print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nF, self.frame, self.nframes, path), end='')
+            print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nf, self.frame, self.nframes, path), end='')
 
         else:
             # Read image
             self.count += 1
             img0 = cv2.imread(path)  # BGR
             assert img0 is not None, 'Image Not Found ' + path
-            print('image %g/%g %s: ' % (self.count, self.nF, path), end='')
+            print('image %g/%g %s: ' % (self.count, self.nf, path), end='')
 
         # Padded resize
         img = letterbox(img0, new_shape=self.img_size)[0]
@@ -140,7 +144,7 @@ class LoadImages:  # for inference
         self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
     def __len__(self):
-        return self.nF  # number of files
+        return self.nf  # number of files
 
 
 class LoadWebcam:  # for inference
@@ -470,6 +474,13 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             img, labels = load_mosaic(self, index)
             shapes = None
 
+            # MixUp https://arxiv.org/pdf/1710.09412.pdf
+            # if random.random() < 0.5:
+            #     img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
+            #     r = np.random.beta(0.3, 0.3)  # mixup ratio, alpha=beta=0.3
+            #     img = (img * r + img2 * (1 - r)).astype(np.uint8)
+            #     labels = np.concatenate((labels, labels2), 0)
+
         else:
             # Load image
             img, (h0, w0), (h, w) = load_image(self, index)

utils/torch_utils.py

@@ -195,6 +195,8 @@ class ModelEMA:
     def __init__(self, model, decay=0.9999, updates=0):
         # Create EMA
         self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
+        if next(model.parameters()).device.type != 'cpu':
+            self.ema.half()  # FP16 EMA
         self.updates = updates  # number of EMA updates
         self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
         for p in self.ema.parameters():