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PANet update
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glenn-jocher committed Jun 22, 2020
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15 changes: 8 additions & 7 deletions README.md
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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.

<img src="https://user-images.githubusercontent.com/26833433/84200349-729f2680-aa5b-11ea-8f9a-604c9e01a658.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 FP32 inference, postprocessing and NMS.
<img src="https://user-images.githubusercontent.com/26833433/85336627-c6663280-b493-11ea-9b0a-289b0f182b84.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 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.

- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference. Comparison in [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).
- **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates to all YOLOv5 models. New models are faster, smaller and more accurate. Credit to @WongKinYiu for his excellent work with CSP.
Expand All @@ -14,13 +14,14 @@ This repository represents Ultralytics open-source research into future object d

## Pretrained Checkpoints

| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPs |
| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: |
| YOLOv5-s ([ckpt](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)) | 35.5 | 35.5 | 55.0 | **2.1ms** | **476** || 7.1M | 12.6B
| YOLOv5-m ([ckpt](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)) | 42.7 | 42.7 | 62.4 | 3.2ms | 312 || 22.0M | 39.0B
| YOLOv5-l ([ckpt](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)) | 45.7 | 45.9 | 65.1 | 4.1ms | 243 || 50.3M | 89.0B
| YOLOv5-x ([ckpt](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)) | **47.2** | **47.3** | **66.6** | 6.5ms | 153 || 95.9M | 170.3B
| YOLOv3-SPP ([ckpt](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)) | 45.6 | 45.5 | 65.2 | 4.8ms | 208 || 63.0M | 118.0B
| [YOLOv5s](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 36.5 | 36.5 | 55.6 | **2.2ms** | **455** || 7.5M | 13.2B
| [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) | **48.2** | **48.3** | **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


** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --img 736 --conf 0.001`
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3 changes: 1 addition & 2 deletions models/yolov3-spp.yaml
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Expand Up @@ -25,8 +25,7 @@ backbone:
[-1, 4, Bottleneck, [1024]], # 10
]

# yolov3-spp head
# na = len(anchors[0])
# YOLOv3-SPP head
head:
[[-1, 1, Bottleneck, [1024, False]], # 11
[-1, 1, SPP, [512, [5, 9, 13]]],
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49 changes: 28 additions & 21 deletions models/yolov5l.yaml
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Expand Up @@ -5,41 +5,48 @@ width_multiple: 1.0 # layer channel multiple

# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32

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M-o-magic Apr 24, 2023

order relative to feature map order. FPN ->PAN
- [30,61, 62,45, 59,119] # P4/16
- [10,13, 16,30, 33,23] # P3/8

# yolov5 backbone
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 1-P1/2
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 3, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 4-P3/8
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 6-P4/16
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 8-P5/32
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 6, BottleneckCSP, [1024]], # 10
]

# yolov5 head
# YOLOv5 head
head:
[[-1, 3, BottleneckCSP, [1024, False]], # 11
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 12 (P5/32-large)
[[-1, 3, BottleneckCSP, [1024, False]], # 9

[-2, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 1, Conv, [512, 1, 1]],
[-1, 3, BottleneckCSP, [512, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 17 (P4/16-medium)
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13

[-2, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P3/8-small)
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 18 (P3/8-small)

[-2, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P4/16-medium)

[-2, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 26 (P5/32-large)

[[], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
[[], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3)
]
49 changes: 28 additions & 21 deletions models/yolov5m.yaml
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Expand Up @@ -5,41 +5,48 @@ width_multiple: 0.75 # layer channel multiple

# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
- [30,61, 62,45, 59,119] # P4/16
- [10,13, 16,30, 33,23] # P3/8

# yolov5 backbone
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 1-P1/2
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 3, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 4-P3/8
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 6-P4/16
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 8-P5/32
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 6, BottleneckCSP, [1024]], # 10
]

# yolov5 head
# YOLOv5 head
head:
[[-1, 3, BottleneckCSP, [1024, False]], # 11
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 12 (P5/32-large)
[[-1, 3, BottleneckCSP, [1024, False]], # 9

[-2, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 1, Conv, [512, 1, 1]],
[-1, 3, BottleneckCSP, [512, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 17 (P4/16-medium)
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13

[-2, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P3/8-small)
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 18 (P3/8-small)

[-2, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P4/16-medium)

[-2, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 26 (P5/32-large)

[[], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
[[], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3)
]
49 changes: 28 additions & 21 deletions models/yolov5s.yaml
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Expand Up @@ -5,41 +5,48 @@ width_multiple: 0.50 # layer channel multiple

# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
- [30,61, 62,45, 59,119] # P4/16
- [10,13, 16,30, 33,23] # P3/8

# yolov5 backbone
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 1-P1/2
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 3, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 4-P3/8
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 6-P4/16
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 8-P5/32
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 6, BottleneckCSP, [1024]], # 10
]

# yolov5 head
# YOLOv5 head
head:
[[-1, 3, BottleneckCSP, [1024, False]], # 11
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 12 (P5/32-large)
[[-1, 3, BottleneckCSP, [1024, False]], # 9

[-2, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 1, Conv, [512, 1, 1]],
[-1, 3, BottleneckCSP, [512, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 17 (P4/16-medium)
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13

[-2, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P3/8-small)
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 18 (P3/8-small)

[-2, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P4/16-medium)

[-2, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 26 (P5/32-large)

[[], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
[[], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3)
]
49 changes: 28 additions & 21 deletions models/yolov5x.yaml
View file
Expand Up @@ -5,41 +5,48 @@ width_multiple: 1.25 # layer channel multiple

# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
- [30,61, 62,45, 59,119] # P4/16
- [10,13, 16,30, 33,23] # P3/8

# yolov5 backbone
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 1-P1/2
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 3, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 4-P3/8
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 6-P4/16
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 8-P5/32
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 6, BottleneckCSP, [1024]], # 10
]

# yolov5 head
# YOLOv5 head
head:
[[-1, 3, BottleneckCSP, [1024, False]], # 11
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 12 (P5/32-large)
[[-1, 3, BottleneckCSP, [1024, False]], # 9

[-2, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 1, Conv, [512, 1, 1]],
[-1, 3, BottleneckCSP, [512, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 17 (P4/16-medium)
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13

[-2, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P3/8-small)
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 18 (P3/8-small)

[-2, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P4/16-medium)

[-2, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]],
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 26 (P5/32-large)

[[], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
[[], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3)
]
8 changes: 5 additions & 3 deletions utils/utils.py
View file
Expand Up @@ -1094,12 +1094,14 @@ def plot_study_txt(f='study.txt', x=None): # from utils.utils import *; plot_st

ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [33.5, 39.1, 42.5, 45.9, 49., 50.5],
'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')

ax2.grid()
ax2.set_xlim(0, 30)
ax2.set_ylim(25, 50)
ax2.set_xlabel('GPU Latency (ms)')
ax2.set_ylim(28, 50)
ax2.set_yticks(np.arange(30, 55, 5))
ax2.set_xlabel('GPU Speed (ms/img)')
ax2.set_ylabel('COCO AP val')
ax2.legend(loc='lower right')
ax2.grid()
plt.savefig('study_mAP_latency.png', dpi=300)
plt.savefig(f.replace('.txt', '.png'), dpi=200)

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8 comments on commit 364fcfd

@yxNONG
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@yxNONG yxNONG commented on 364fcfd Jun 23, 2020

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@glenn-jocher thanks for great work! the neck is change to PAN now, my question is, is the pretrained weight also been updated now?

@glenn-jocher
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@yxNONG yes, everything is updated including the pretrained checkpoints. If you delete your current checkpoints it will download the new ones automatically. It will not attempt to download new weights if it seems a matching checkpoint filename in your directory however.

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@glenn-jocher how's the speed and accuracy tradeoff leveraged? readmes seems newest?

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Excited for this, any performance numbers?

@glenn-jocher
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@glenn-jocher glenn-jocher commented on 364fcfd Jun 23, 2020 •
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@HaxThePlanet @jinfagang yes, the models are faster, smaller and more accurate. You can see this in the README table changes above.

Before:

Model APval APtest AP50 SpeedGPU FPSGPU params FLOPs
YOLOv5-s 35.5 35.5 55.0 2.1ms 476 7.1M 12.6B
YOLOv5-m 42.7 42.7 62.4 3.2ms 312 22.0M 39.0B
YOLOv5-l 45.7 45.9 65.1 4.1ms 243 50.3M 89.0B
YOLOv5-x 47.2 47.3 66.6 6.5ms 153 95.9M 170.3B
YOLOv3-SPP 45.6 45.5 65.2 4.8ms 208 63.0M 118.0B

After:

Model APval APtest AP50 SpeedGPU FPSGPU params FLOPS
YOLOv5s 36.5 36.5 55.6 2.1ms 476 7.5M 13.2B
YOLOv5m 43.4 43.4 62.4 3.0ms 333 21.8M 39.4B
YOLOv5l 46.6 46.7 65.4 3.9ms 256 47.8M 88.1B
YOLOv5x 48.4 48.4 66.9 6.1ms 164 89.0M 166.4B
YOLOv3-SPP 45.6 45.5 65.2 4.5ms 222 63.0M 118.0B

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i see that all the pretrained checkpoints are based on F16, i wonder that all the results are based on F16 or F32 ?

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@Laughing-q all checkpoints and results are in FP16.

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All older FPN models are still accessible in yolov5/models/hub Screen Shot 2020-06-24 at 11 59 52 AM

And the pretrained weights are accessable from the Google Drive folder in the hub/ directory:
Screen Shot 2020-06-24 at 12 01 31 PM

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