Model card for eca_botnext26ts_256.c1_in1kA BotNet image... | Model card for eca_botnext26ts_256.c1_in1kA BotNet image...
Model card for eca_botnext26ts_256.c1_in1k
A BotNet image classification model (with Efficient channel attention, based on ResNeXt architecture). Trained on ImageNet-1k in timm by Ross Wightman.

NOTE: this model did not adhere to any specific paper configuration, it was tuned for reasonable training times and reduced frequency of self-attention blocks.

Recipe details:

Based on ResNet Strikes Back C recipes
SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping).
Cosine LR schedule with warmup
This model architecture is implemented using timm's flexible BYOBNet (Bring-Your-Own-Blocks Network).

BYOB (with BYOANet attention specific blocks) allows configuration of:

block / stage layout
block-type interleaving
stem layout
output stride (dilation)
activation and norm layers
channel and spatial / self-attention layers
...and also includes timm features common to many other architectures, including:

stochastic depth
gradient checkpointing
layer-wise LR decay
per-stage feature extraction

https://huggingface.co/timm/eca_botnext26ts_256.c1_in1k timm/eca_botnext26ts_256.c1_in1k · Hugging Face