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pipeline_tag: image-classification
license: apache-2.0
base_model: timm/tf_efficientnet_lite1.in1k
library_name: kerasformers
tags:
- keras
- kerasformers
- image-classification
- efficientnet-lite
- backbone
- arxiv:1905.11946
- pytorch
- jax
- tf
---
## ***See [our collection](https://huggingface.co/collections/kerasformers/efficientnet-lite-6a6d117aa2e14494d3aa6a58) for all versions of EfficientNet-Lite.***
# Run EfficientNet-Lite with Keras 3: JAX, PyTorch, or TensorFlow
[](https://github.com/IMvision12/KerasFormers) [](https://imvision12.github.io/KerasFormers/classification_backbones/) [](https://huggingface.co/collections/kerasformers/efficientnet-lite-6a6d117aa2e14494d3aa6a58)
# kerasformers/tf_efficientnet_lite1_in1k
Paper: [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (arXiv:1905.11946)](https://arxiv.org/abs/1905.11946) · [HF Papers](https://huggingface.co/papers/1905.11946)
EfficientNet-Lite is the mobile/EdgeTPU-friendly EfficientNet family (no squeeze-excite, ReLU6). Classifier or backbone.
For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/tf_efficientnet_lite1.in1k).
Pure-**Keras 3** conversion of [`timm/tf_efficientnet_lite1.in1k`](https://huggingface.co/timm/tf_efficientnet_lite1.in1k) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
This is an **image-classification / backbone** checkpoint (`EfficientNetLiteImageClassify` / `EfficientNetLiteModel`).
## ✨ Quick start
```python
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
import numpy as np
from kerasformers.models.efficientnet_lite import EfficientNetLiteImageClassify, EfficientNetLiteModel
model = EfficientNetLiteImageClassify.from_weights("kerasformers/tf_efficientnet_lite1_in1k")
backbone = EfficientNetLiteModel.from_weights(
"kerasformers/tf_efficientnet_lite1_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 EfficientNet-Lite variant the same way with `from_weights("kerasformers/<variant>")`:
| Variant | Hub |
|---|---|
| `tf_efficientnet_lite0_in1k` | [`kerasformers/tf_efficientnet_lite0_in1k`](https://huggingface.co/kerasformers/tf_efficientnet_lite0_in1k) |
| `tf_efficientnet_lite1_in1k` | [`kerasformers/tf_efficientnet_lite1_in1k`](https://huggingface.co/kerasformers/tf_efficientnet_lite1_in1k) |
| `tf_efficientnet_lite2_in1k` | [`kerasformers/tf_efficientnet_lite2_in1k`](https://huggingface.co/kerasformers/tf_efficientnet_lite2_in1k) |
| `tf_efficientnet_lite3_in1k` | [`kerasformers/tf_efficientnet_lite3_in1k`](https://huggingface.co/kerasformers/tf_efficientnet_lite3_in1k) |
| `tf_efficientnet_lite4_in1k` | [`kerasformers/tf_efficientnet_lite4_in1k`](https://huggingface.co/kerasformers/tf_efficientnet_lite4_in1k) |
## Tips
- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
- `EfficientNetLiteImageClassify` returns class logits; `EfficientNetLiteModel` returns features (`as_backbone=True` for multi-scale stages).
- See [docs](https://imvision12.github.io/KerasFormers/classification_backbones/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
- Upstream / timm checkpoints: `EfficientNetLiteImageClassify.from_weights("hf:timm/tf_efficientnet_lite1.in1k")`.
## Special Thanks
A huge thank you to the EfficientNet-Lite authors and the timm / Hub communities for creating and releasing these models.
License: see YAML `license` (usually matches the upstream checkpoint).
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