YOLOv5 v4.0 Release

README.md

@@ -4,35 +4,40 @@
 
 ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
 
-This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.
+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/26833433/90187293-6773ba00-dd6e-11ea-8f90-cd94afc0427f.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.
+<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.
 - **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).
-- **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
-- **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
 
 
 ## Pretrained Checkpoints
 
-| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | GFLOPS |
-|---------- |------ |------ |------ | -------- | ------| ------ |------  |  :------: |
-| [YOLOv5s](https://github.com/ultralytics/yolov5/releases)    | 37.0     | 37.0     | 56.2     | **2.4ms** | **416** || 7.5M   | 17.5
-| [YOLOv5m](https://github.com/ultralytics/yolov5/releases)    | 44.3     | 44.3     | 63.2     | 3.4ms     | 294     || 21.8M  | 52.3
-| [YOLOv5l](https://github.com/ultralytics/yolov5/releases)    | 47.7     | 47.7     | 66.5     | 4.4ms     | 227     || 47.8M  | 117.2
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases)    | **49.2** | **49.2** | **67.7** | 6.9ms     | 145     || 89.0M  | 221.5
-| | | | | | || |
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA|**50.8**| **50.8** | **68.9** | 25.5ms    | 39      || 89.0M  | 801.0
+| 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` 
 
+
 ## 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:
@@ -106,21 +111,21 @@ import torch
 from PIL import Image
 
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)  # for PIL/cv2/np inputs and NMS
+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
 
 # Inference
-prediction = model(imgs, size=640)  # includes NMS
+result = model(imgs)
 ```
 
 
 ## Training
 
-Download [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) and run command below. 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

data/coco.yaml

@@ -18,15 +18,15 @@ test: ../coco/test-dev2017.txt  # 20288 of 40670 images, submit to https://compe
 nc: 80
 
 # 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']
+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' ]
 
 # Print classes
 # with open('data/coco.yaml') as f:

data/coco128.yaml

@@ -17,12 +17,12 @@ val: ../coco128/images/train2017/  # 128 images
 nc: 80
 
 # 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']
+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' ]

data/voc.yaml

@@ -17,5 +17,5 @@ val: ../VOC/images/val/  # 4952 images
 nc: 20
 
 # class names
-names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
-        'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
+names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
+         'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ]

models/common.py

@@ -30,7 +30,7 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, k
         super(Conv, self).__init__()
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
         self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.Hardswish() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
+        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
 
     def forward(self, x):
         return self.act(self.bn(self.conv(x)))
@@ -105,9 +105,39 @@ class Focus(nn.Module):
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
         super(Focus, self).__init__()
         self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
+        # self.contract = Contract(gain=2)
 
     def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
         return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+        # return self.conv(self.contract(x))
+
+
+class Contract(nn.Module):
+    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        N, C, H, W = x.size()  # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
+        s = self.gain
+        x = x.view(N, C, H // s, s, W // s, s)  # x(1,64,40,2,40,2)
+        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
+        return x.view(N, C * s * s, H // s, W // s)  # x(1,256,40,40)
+
+
+class Expand(nn.Module):
+    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        N, C, H, W = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
+        s = self.gain
+        x = x.view(N, s, s, C // s ** 2, H, W)  # x(1,2,2,16,80,80)
+        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
+        return x.view(N, C // s ** 2, H * s, W * s)  # x(1,16,160,160)
 
 
 class Concat(nn.Module):
@@ -253,20 +283,13 @@ def tolist(self):
         return x
 
 
-class Flatten(nn.Module):
-    # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
-    @staticmethod
-    def forward(x):
-        return x.view(x.size(0), -1)
-
-
 class Classify(nn.Module):
     # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
         super(Classify, self).__init__()
         self.aap = nn.AdaptiveAvgPool2d(1)  # to x(b,c1,1,1)
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g)  # to x(b,c2,1,1)
-        self.flat = Flatten()
+        self.flat = nn.Flatten()
 
     def forward(self, x):
         z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1)  # cat if list

models/experimental.py

@@ -105,8 +105,8 @@ def forward(self, x, augment=False):
         for module in self:
             y.append(module(x, augment)[0])
         # y = torch.stack(y).max(0)[0]  # max ensemble
-        # y = torch.cat(y, 1)  # nms ensemble
-        y = torch.stack(y).mean(0)  # mean ensemble
+        # y = torch.stack(y).mean(0)  # mean ensemble
+        y = torch.cat(y, 1)  # nms ensemble
         return y, None  # inference, train output
 
 

models/hub/anchors.yaml

@@ -0,0 +1,58 @@
+# Default YOLOv5 anchors for COCO data
+
+
+# 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
+
+
+# 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
+
+# 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
+
+# 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
+
+
+# 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
+
+# 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
+
+# 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