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kerasformers/
efficientformer_l1_snap_dist_in1k

Name: efficientformer_l1_snap_dist_in1k
Rating: 2.5 (36 reviews)

timm/efficientformer_l1.snap_dist_in1k

finetune

apache-2.0

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Run EfficientFormer with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

kerasformers/efficientformer_l1_snap_dist_in1k

Paper: EfficientFormer: Vision Transformers at MobileNet Speed (arXiv:2206.01191) · HF Papers

EfficientFormer redesigns transformers for MobileNet-level latency on mobile devices. Classifier or 4-stage backbone.

For more details on the model, please go to the upstream model card.

Pure-Keras 3 conversion of timm/efficientformer_l1.snap_dist_in1k for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is an image-classification / backbone checkpoint (EfficientFormerImageClassify / EfficientFormerModel).

✨ Quick start

python

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from PIL import Image
import numpy as np
from kerasformers.models.efficientformer import EfficientFormerImageClassify, EfficientFormerModel

model = EfficientFormerImageClassify.from_weights("kerasformers/efficientformer_l1_snap_dist_in1k")
backbone = EfficientFormerModel.from_weights(
    "kerasformers/efficientformer_l1_snap_dist_in1k", as_backbone=True
)

image = Image.open("your_image.jpg").convert("RGB")
image = image.resize((224, 224))
x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3)
print(model(x).shape)  # (1, num_classes)
feats = backbone(x)
print(len(feats), [tuple(f.shape) for f in feats])

Load any EfficientFormer variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
`efficientformer_l1_snap_dist_in1k`	`kerasformers/efficientformer_l1_snap_dist_in1k`
`efficientformer_l3_snap_dist_in1k`	`kerasformers/efficientformer_l3_snap_dist_in1k`
`efficientformer_l7_snap_dist_in1k`	`kerasformers/efficientformer_l7_snap_dist_in1k`

Tips

Set KERAS_BACKEND before importing Keras / kerasformers.
EfficientFormerImageClassify returns class logits; EfficientFormerModel returns features (as_backbone=True for multi-scale stages).
See docs and Loading Weights.
Upstream / timm checkpoints: EfficientFormerImageClassify.from_weights("hf:timm/efficientformer_l1.snap_dist_in1k").

Special Thanks

A huge thank you to the EfficientFormer authors and the timm / Hub communities for creating and releasing these models.

License: see YAML license (usually matches the upstream checkpoint).