CSPNet (CSPResNet, CSPDarkNet, DarkNet)

Category: CNN · Code: timm/models/cspnet.py · Architectures: 23 · Pretrained weights: 15 (15 with ImageNet-1k results)

Cross Stage Partial Networks split the features of each stage into two parts, one passing through the stage blocks and one merged back at the end of the stage. This module holds CSPResNet, CSPResNeXt, CSPDarkNet, DarkNet and the timm cs3* variants.

Papers

• CSPNet: A New Backbone that can Enhance Learning Capability of CNN
• YOLOv3: An Incremental Improvement

Usage

See the Quickstart for loading pretrained weights, image preprocessing and fine-tuning, and Feature Extraction for using the models as backbones.

>>> import timm
>>> model = timm.create_model('cspdarknet53.ra_in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)

Model API support

✓ supported, ✓ (square) works for square sizes only, ✗ not supported, – not needed (the model already accepts any input size). Checked by running each feature on a randomly initialized model (CspNet with cs3darknet_focus_s at 256), other architectures of the same class are expected to behave the same. See the Model API page for details.

Architectures

GMACs and parameter counts are for the default input size and classifier of each architecture, from the benchmark results.

Pretrained weights

Top-1 is ImageNet-1k validation accuracy from results-imagenet.csv at the listed eval size, - if the weight has no ImageNet-1k result (e.g. pretraining or image-text weights). Classes is the size of the pretrained classifier, none for weights without one.

Weights are grouped by source: the paper or origin given in the pretrained config, else a Hub organization other than timm, else the first part of the pretrained tag (architecture.tag). Sources with a single weight are listed under other.

c2ns tags

ra tags

Other