Adapt and Export Models

Input Channels

Set in_chans to the number of channels your model will receive. For a non-RGB checkpoint, its pretrained configuration’s input_size[0] must describe the source checkpoint’s channel count. For example, to use two stacked 13-band images with a matching 13-band ResNet-18 checkpoint:

import timm

model = timm.create_model(
    'resnet18',
    pretrained=True,
    in_chans=26,
    num_classes=10,
    pretrained_cfg_overlay={
        'file': 'path/to/your_13_band_resnet18.pth',
        'input_size': (13, 224, 224),
        'num_classes': 10,
    },
)

This example requires your own checkpoint with the matching architecture and source class count. Input convolution weights are repeated or truncated in channel order, then multiplied by the source-to-target channel ratio. Thus a 13-to-26 conversion repeats the weights twice and scales them by 1/2; repeated copies of the same frame preserve the input convolution’s response. A 13-to-11 conversion keeps the first 11 channels and scales by 13/11. Matching channel counts load unchanged. Conversion to one channel sums the source-channel weights.

For direct weight conversion, specify the same source information explicitly:

from timm.models import adapt_input_conv

temporal_weights = adapt_input_conv(26, pretrained_weights, base_chans=13)

base_chans describes physical image channels, which can differ from a space-to-depth stem’s convolution input dimension. The existing RGB default and space-to-depth grayscale conversion remain supported; other space-to-depth channel conversions still use the unsupported-format fallback during loading. Band-specific selection, semantic band matching, and normalization are not inferred: arrange the bands and preprocessing for your sensor and task.

Save Adapted Models

model.pretrained_cfg continues to describe the source checkpoint after adaptation. The current model exposes in_chans and num_classes. An instance-owned _traits dictionary records its nominal img_size, fixed_input_size constraint, and first_conv path. The separate _model_args dictionary records construction arguments for export. Supported set_input_size() methods update the traits and reconstruction arguments together.

Use timm.models.get_model_traits(model) to inspect a copy of the traits plus the current in_chans and num_classes attributes. Those counts are read from the attributes instead of duplicated in _traits, so classifier resets stay reflected. timm.models.get_model_input_config(model) resolves the input configuration from these traits, and timm.models.get_model_args(model) returns reconstruction arguments.

Hub export writes a new configuration describing the saved weights without changing the live source configuration. Pass the preprocessing used for the adapted model:

from timm.models import save_for_hf

save_for_hf(model, 'my_model', data_config=data_config)
restored = timm.create_model('local-dir:my_model', pretrained=True)

The built-in training, validation, inference, and benchmark scripts resolve input settings against the current model. The existing resolve_data_config and resolve_model_data_config helpers retain their source-config behavior.

Exported Construction Arguments

The factory records construction arguments such as activation, normalization, and embedding dimensions. Export excludes device/dtype placement and captured training regularization: drop_rate, drop_path_rate, drop_block_rate, and *_drop_rate. Reloading therefore uses the architecture’s regularization defaults unless the caller supplies new values. Explicit export model_args can retain a rate when that is intentional.

Callable arguments cannot be serialized reliably. Prefer registered string names when constructing a model, or supply an equivalent name when exporting:

import torch.nn as nn

model = timm.create_model('resnet18', act_layer=nn.SiLU)
save_for_hf(model, 'my_silu_resnet', model_args={'act_layer': 'silu'})

Without a serializable replacement, export raises an error naming the argument. Dropping it silently could reconstruct a different model even when its weights load successfully.