COCO8 and COCO8-seg Pytest and CI updates (#307)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: RangiLyu <lyuchqi@gmail.com>

ultralytics/nn/modules.py

@@ -82,7 +82,7 @@ class ConvTranspose(nn.Module):
 
 
 class DFL(nn.Module):
-    # DFL module
+    # Integral module of Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
     def __init__(self, c1=16):
         super().__init__()
         self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)

ultralytics/yolo/data/datasets/coco8-seg.yaml

@@ -0,0 +1,101 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco8-seg.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco8-seg  ← downloads here (1 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8-seg  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco8-seg.zip

ultralytics/yolo/data/datasets/coco8.yaml

@@ -0,0 +1,101 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco8.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco8  ← downloads here (1 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8  # dataset root dir
+train: images/train  # train images (relative to 'path') 4 images
+val: images/val  # val images (relative to 'path') 4 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco8.zip

ultralytics/yolo/utils/loss.py

@@ -47,6 +47,7 @@ class BboxLoss(nn.Module):
     @staticmethod
     def _df_loss(pred_dist, target):
         # Return sum of left and right DFL losses
+        # Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
         tl = target.long()  # target left
         tr = tl + 1  # target right
         wl = tr - target  # weight left

ultralytics/yolo/v8/__init__.py

@@ -1,11 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-from pathlib import Path
-
+from ultralytics.yolo.configs import hydra_patch  # noqa (patch hydra cli)
 from ultralytics.yolo.v8 import classify, detect, segment
 
-ROOT = Path(__file__).parents[0]  # yolov8 ROOT
-
 __all__ = ["classify", "segment", "detect"]
-
-from ultralytics.yolo.configs import hydra_patch  # noqa (patch hydra cli)