Visualize Features in Yolov5

[Bstrum36]

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Question

The visualize=False, # visualize features flag enables the visualization of the feature maps of some set of convolutions in Yolov5. The png files displaying the features are labeled stage#_Conv_features.png

What am I actually seeing, what are they referring to as stages, can you point me to a reference.

Thanks!

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[github-actions]

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[glenn-jocher]

@Bstrum36 👋 Hello! Thanks for asking about feature visualization. YOLOv5 🚀 features can be visualized through all stages of the model from input to output. To visualize features from a given source run detect.py with the --visualize flag:

python detect.py --weights yolov5s.pt --source data/images/bus.jpg --visualize

An example Notebook visualizing bus.jpg features with YOLOv5s is shown below:

All stages are visualized by default, each with its own PNG showing the first 32 feature maps output from that stage. You can open any PNG for a closer look. For example the first 32 feature maps of the Focus() layer output are shown in stage0_Focus_features.png:

Feature maps may be customized by updating the feature_visualization() function in utils/plots.py:

yolov5/utils/plots.py

Lines 403 to 427 in bb5ebc2

	def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
	"""
	x: Features to be visualized
	module_type: Module type
	stage: Module stage within model
	n: Maximum number of feature maps to plot
	save_dir: Directory to save results
	"""
	if 'Detect' not in module_type:
	batch, channels, height, width = x.shape # batch, channels, height, width
	if height > 1 and width > 1:
	f = f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
	blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
	n = min(n, channels) # number of plots
	fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
	ax = ax.ravel()
	plt.subplots_adjust(wspace=0.05, hspace=0.05)
	for i in range(n):
	ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
	ax[i].axis('off')
	print(f'Saving {save_dir / f}... ({n}/{channels})')
	plt.savefig(save_dir / f, dpi=300, bbox_inches='tight')
	plt.close()

Good luck 🍀 and let us know if you have any other questions!

[Bstrum36]

Thanks. I will spend some time on this

…

On Mon, Jul 25, 2022 at 4:05 PM Glenn Jocher ***@***.***> wrote: @Bstrum36 <https://github.com/Bstrum36> 👋 Hello! Thanks for asking about *feature visualization*. YOLOv5 🚀 features can be visualized through all stages of the model from input to output. To visualize features from a given source run detect.py with the --visualize flag: python detect.py --weights yolov5s.pt --source data/images/bus.jpg --visualize An example Notebook <https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb> visualizing bus.jpg <https://github.com/ultralytics/yolov5/blob/master/data/images/bus.jpg> features with YOLOv5s is shown below: [image: Open In Colab] <https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb> [image: Open In Kaggle] <https://www.kaggle.com/ultralytics/yolov5> [image: Screenshot 2021-08-30 at 16 44 04] <https://user-images.githubusercontent.com/26833433/131357928-663a120a-3133-4e1a-8995-c812bd732e8a.png> All stages are visualized by default, each with its own PNG showing the first 32 feature maps *output* from that stage. You can open any PNG for a closer look. For example the first 32 feature maps of the Focus() layer output are shown in stage0_Focus_features.png: [image: stage0_Focus_features] <https://user-images.githubusercontent.com/26833433/131359874-ecc9aef8-1c9b-44f5-ac17-d6b987093554.png> Feature maps may be customized by updating the feature_visualization() function in utils/plots.py: https://github.com/ultralytics/yolov5/blob/bb5ebc290e5d630a081d7cbc5a9725ed8cea0a24/utils/plots.py#L403-L427 Good luck 🍀 and let us know if you have any other questions! — Reply to this email directly, view it on GitHub <#8717 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AI2XRUI7PA3GC7RU4HD2NRDVV3XRVANCNFSM54TOCOMQ> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[github-actions]

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