yolox-backbone 0.0.1.3

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 is a 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()

List Supported Models

import yolox_backbone
from pprint import pprint

model_names = yolox_backbone.list_models()
pprint(model_names)

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

Select specific feature levels

There is on creation argument impacting the output features.

• out_features selects which FPN features to output

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])
--------------------------------------------------