yolox-backbone 0.0.1.9

yolox_backbone is a deep-learning library and is a collection of YOLOX Backbone models.

YOLOX-Backbone

yolox-backbone is a deep-learning library and collection of YOLOX backbone models.

Install

pip install yolox-backbone

Load a Pretrained Model

Pretrained models can be loaded using yolox_backbone.create_model.

import yolox_backbone

m = yolox_backbone.create_model('yolox-s', pretrained=True)
m.eval()

Query the architecture information

After a feature backbone has been created, it can be queried to provide architecture information. The .scaling_factor attribute is a dictionary encapsulating the information about the scaling factor.

import yolox_backbone

m = yolox_backbone.create_model('yolox-s', pretrained=True)
print('Network scaling factor: ', m.scaling_factor)

Output:

Network scaling factor:  {'depth': 0.33, 'width': 0.5}

Query the feature information

After a feature backbone has been created, it can be queried to provide channel information to the downstream heads without requiring static config or hardcoded constants. The .out_channels attribute is a dictionary encapsulating the information about the feature extraction points.

import yolox_backbone

m = yolox_backbone.create_model('yolox-s', pretrained=True)
print('Feature channels: ', m.out_channels)

Output:

Feature channels:  {'P3': 128, 'P4': 256, 'P5': 512}

List Supported Models

import yolox_backbone
from pprint import pprint

model_names = yolox_backbone.list_models()
pprint(model_names)

Output:

['yolox-s',
 'yolox-m',
 'yolox-l',
 'yolox-x',
 'yolox-nano',
 'yolox-tiny',
 'yolox-darknet53']

Select specific feature levels

There is one creation argument impacting the output features.

• out_features selects which FPN features to output

Support for different number of input channels

You can create the model without the constraint that the number of input channel is 3.

But you have to set pretrained to False.

import yolox_backbone

model = yolox_backbone.create_model(model_name=model_name, 
                                    pretrained=False, 
                                    input_tensor_channels=4,
                                    out_features=["P3", "P4", "P5"]
                                    )

Example

import yolox_backbone
import torch
from pprint import pprint

pprint(yolox_backbone.list_models())

model_names = yolox_backbone.list_models()
for model_name in model_names:
    print("model_name: ", model_name)
    model = yolox_backbone.create_model(model_name=model_name, 
                                        pretrained=True, 
                                        out_features=["P3", "P4", "P5"]
                                        )

    input_tensor = torch.randn((1, 3, 640, 640))
    fpn_output_tensors = model(input_tensor)

    p3 = fpn_output_tensors["P3"]
    p4 = fpn_output_tensors["P4"]
    p5 = fpn_output_tensors["P5"]
    
    print("input_tensor.shape: ", input_tensor.shape)
    print("p3.shape: ", p3.shape)
    print("p4.shape: ", p4.shape)
    print("p5.shape: ", p5.shape)
    print("-" * 50)
    

Output:

['yolox-s', 'yolox-m', 'yolox-l', 'yolox-x', 'yolox-nano', 'yolox-tiny', 'yolox-darknet53']
model_name:  yolox-s
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 128, 80, 80])
p4.shape:  torch.Size([1, 256, 40, 40])
p5.shape:  torch.Size([1, 512, 20, 20])
--------------------------------------------------
model_name:  yolox-m
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 192, 80, 80])
p4.shape:  torch.Size([1, 384, 40, 40])
p5.shape:  torch.Size([1, 768, 20, 20])
--------------------------------------------------
model_name:  yolox-l
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 256, 80, 80])
p4.shape:  torch.Size([1, 512, 40, 40])
p5.shape:  torch.Size([1, 1024, 20, 20])
--------------------------------------------------
model_name:  yolox-x
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 320, 80, 80])
p4.shape:  torch.Size([1, 640, 40, 40])
p5.shape:  torch.Size([1, 1280, 20, 20])
--------------------------------------------------
model_name:  yolox-nano
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 64, 80, 80])
p4.shape:  torch.Size([1, 128, 40, 40])
p5.shape:  torch.Size([1, 256, 20, 20])
--------------------------------------------------
model_name:  yolox-tiny
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 96, 80, 80])
p4.shape:  torch.Size([1, 192, 40, 40])
p5.shape:  torch.Size([1, 384, 20, 20])
--------------------------------------------------
model_name:  yolox-darknet53
input_tensor.shape:  torch.Size([1, 3, 640, 640])
p3.shape:  torch.Size([1, 128, 80, 80])
p4.shape:  torch.Size([1, 256, 40, 40])
p5.shape:  torch.Size([1, 512, 20, 20])
--------------------------------------------------

Acknowledgement

The docs are heavily based on timm docs. Thanks for their awesome works.