# DPN

**Category:** CNN · **Code:**
[`timm/models/dpn.py`](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/dpn.py) ·
**Architectures:** 7 · **Pretrained weights:** 7 (7 with ImageNet-1k results)

Dual Path Networks combine a residual path (feature re-use) and a densely connected path (new feature exploration) in
each block. Compatible with the weights of the original MXNet implementation.

## Papers

* [Dual Path Networks](https://arxiv.org/abs/1707.01629)

## Usage

See the [Quickstart](../quickstart) for loading pretrained weights, image preprocessing and fine-tuning, and [Feature
Extraction](../feature_extraction) for using the models as backbones.

```py
>>> import timm
>>> model = timm.create_model('dpn92.mx_in1k', pretrained=True)
>>> data_config = timm.data.resolve_model_data_config(model)
```

## Model API support

| Feature | `DPN` |
|---|---|
| [`features_only=True` feature maps](../model_api#intermediate-features) | ✓ (strides 2, 4, 8, 16, 32) |
| [`forward_intermediates()`](../model_api#intermediate-features) | ✗ |
| [`prune_intermediate_layers()`](../model_api#intermediate-features) | ✗ |
| [Any input size, no changes needed](../model_api#input-size) | ✓ |
| [`set_input_size()`](../model_api#input-size) | – |
| [`dynamic_img_size=True`](../model_api#input-size) | – |
| [`set_grad_checkpointing()`](../model_api#training-hooks) | ✗ |
| [Layer-wise LR decay (`group_matcher()`)](../model_api#training-hooks) | ✓ |
| [`reset_classifier()`](../model_api#classifier) | ✓ |

✓ 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 (`DPN` with `dpn48b` at 224), other architectures
of the same class are expected to behave the same. See the [Model API](../model_api) page for details.

## Architectures

| Architecture | Params (M) | GMACs | Input size | Pretrained weights |
|---|---:|---:|---:|---:|
| `dpn48b` | 9.13 | 1.69 | 224 | 0 |
| `dpn68` | 12.61 | 2.35 | 224 | 1 |
| `dpn68b` | 12.61 | 2.35 | 224 | 2 |
| `dpn92` | 37.67 | 6.54 | 224 | 1 |
| `dpn98` | 61.57 | 11.73 | 224 | 1 |
| `dpn107` | 86.92 | 18.38 | 224 | 1 |
| `dpn131` | 79.25 | 16.09 | 224 | 1 |

GMACs and parameter counts are for the default input size and classifier of each architecture, from the [benchmark
results](https://github.com/huggingface/pytorch-image-models/tree/main/results).

## Pretrained weights

Top-1 is ImageNet-1k validation accuracy from
[`results-imagenet.csv`](https://github.com/huggingface/pytorch-image-models/blob/main/results/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.

| Weight | Input size | Top-1 (%) | Eval size | Classes | License | Hub |
|---|---:|---:|---:|---:|---|---|
| `dpn68.mx_in1k` | 224 | 76.304 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/dpn68.mx_in1k) |
| `dpn68b.mx_in1k` | 224 | 77.494 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/dpn68b.mx_in1k) |
| `dpn68b.ra_in1k` | 224 (test 288) | 79.342 | 288 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/dpn68b.ra_in1k) |
| `dpn92.mx_in1k` | 224 | 80.032 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/dpn92.mx_in1k) |
| `dpn98.mx_in1k` | 224 | 79.664 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/dpn98.mx_in1k) |
| `dpn107.mx_in1k` | 224 | 80.166 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/dpn107.mx_in1k) |
| `dpn131.mx_in1k` | 224 | 79.836 | 224 | 1000 | apache-2.0 | [timm](https://huggingface.co/timm/dpn131.mx_in1k) |