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tf_efficientnetv2_b2_in1k

Image Classification
Keras
PyTorch
JAX
TensorFlow
zeromodels
efficientnetv2
backbone
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  - kerasformers
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  - image-classification
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  - efficientnetv2
 
 
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  - pytorch
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  - jax
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  - tf
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  ---
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- # tf_efficientnetv2_b2_in1k
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- Pure-Keras 3 image-classification weight for [kerasformers](https://github.com/IMvision12/KerasFormers), converted from [timm/tf_efficientnetv2_b2.in1k](https://huggingface.co/timm/tf_efficientnetv2_b2.in1k).
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- ## Usage
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```python
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- from kerasformers.models.efficientnetv2 import EfficientNetV2ImageClassify
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- model = EfficientNetV2ImageClassify.from_weights("tf_efficientnetv2_b2_in1k")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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- License: **apache-2.0**, inherited from the upstream source.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - kerasformers
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  - image-classification
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  - efficientnetv2
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+ - backbone
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+ - arxiv:2104.00298
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  - pytorch
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  - jax
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  - tf
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  ---
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+ ## ***See [our collection](https://huggingface.co/collections/kerasformers/efficientnetv2-6a6d11b8c2748868ecfd4f9e) for all versions of EfficientNetV2.***
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+ # Run EfficientNetV2 with Keras 3: JAX, PyTorch, or TensorFlow
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+ [![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-black?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-EfficientNetV2-blue)](https://imvision12.github.io/KerasFormers/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-EfficientNetV2%20collection-yellow)](https://huggingface.co/collections/kerasformers/efficientnetv2-6a6d11b8c2748868ecfd4f9e)
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+
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+ # kerasformers/tf_efficientnetv2_b2_in1k
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+
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+ Paper: [EfficientNetV2: Smaller Models and Faster Training (arXiv:2104.00298)](https://arxiv.org/abs/2104.00298) · [HF Papers](https://huggingface.co/papers/2104.00298)
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+ EfficientNetV2 trains faster with Fused-MBConv and progressive learning. Same ImageClassify / Model API as EfficientNet.
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+ For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/tf_efficientnetv2_b2.in1k).
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+ Pure-**Keras 3** conversion of [`timm/tf_efficientnetv2_b2.in1k`](https://huggingface.co/timm/tf_efficientnetv2_b2.in1k) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
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+ This is an **image-classification / backbone** checkpoint (`EfficientNetV2ImageClassify` / `EfficientNetV2Model`).
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+ ## ✨ Quick start
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  ```python
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+ import os
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+ os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
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+
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+ from PIL import Image
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+ import numpy as np
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+ from kerasformers.models.efficientnetv2 import EfficientNetV2ImageClassify, EfficientNetV2Model
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+
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+ model = EfficientNetV2ImageClassify.from_weights("kerasformers/tf_efficientnetv2_b2_in1k")
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+ backbone = EfficientNetV2Model.from_weights(
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+ "kerasformers/tf_efficientnetv2_b2_in1k", as_backbone=True
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+ )
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+
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+ image = Image.open("your_image.jpg").convert("RGB")
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+ image = image.resize((224, 224))
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+ x = np.asarray(image, dtype="float32")[None] # (1, H, W, 3)
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+ print(model(x).shape) # (1, num_classes)
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+ feats = backbone(x)
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+ print(len(feats), [tuple(f.shape) for f in feats])
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  ```
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+ Load any EfficientNetV2 variant the same way with `from_weights("kerasformers/<variant>")`:
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+
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+ | Variant | Hub |
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+ |---|---|
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+ | `tf_efficientnetv2_b0_in1k` | [`kerasformers/tf_efficientnetv2_b0_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b0_in1k) |
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+ | `tf_efficientnetv2_b1_in1k` | [`kerasformers/tf_efficientnetv2_b1_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b1_in1k) |
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+ | `tf_efficientnetv2_b2_in1k` | [`kerasformers/tf_efficientnetv2_b2_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b2_in1k) |
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+ | `tf_efficientnetv2_b3_in1k` | [`kerasformers/tf_efficientnetv2_b3_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b3_in1k) |
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+ | `tf_efficientnetv2_b3_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_b3_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b3_in21k_ft_in1k) |
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+ | `tf_efficientnetv2_l_in1k` | [`kerasformers/tf_efficientnetv2_l_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_l_in1k) |
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+ | `tf_efficientnetv2_l_in21k` | [`kerasformers/tf_efficientnetv2_l_in21k`](https://huggingface.co/kerasformers/tf_efficientnetv2_l_in21k) |
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+ | `tf_efficientnetv2_l_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_l_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_l_in21k_ft_in1k) |
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+ | `tf_efficientnetv2_m_in1k` | [`kerasformers/tf_efficientnetv2_m_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_m_in1k) |
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+ | `tf_efficientnetv2_m_in21k` | [`kerasformers/tf_efficientnetv2_m_in21k`](https://huggingface.co/kerasformers/tf_efficientnetv2_m_in21k) |
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+ | `tf_efficientnetv2_m_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_m_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_m_in21k_ft_in1k) |
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+ | `tf_efficientnetv2_s_in1k` | [`kerasformers/tf_efficientnetv2_s_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_s_in1k) |
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+ | `tf_efficientnetv2_s_in21k` | [`kerasformers/tf_efficientnetv2_s_in21k`](https://huggingface.co/kerasformers/tf_efficientnetv2_s_in21k) |
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+ | `tf_efficientnetv2_s_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_s_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_s_in21k_ft_in1k) |
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+ | `tf_efficientnetv2_xl_in21k` | [`kerasformers/tf_efficientnetv2_xl_in21k`](https://huggingface.co/kerasformers/tf_efficientnetv2_xl_in21k) |
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+ | `tf_efficientnetv2_xl_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_xl_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_xl_in21k_ft_in1k) |
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+ ## Tips
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+ - Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
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+ - `EfficientNetV2ImageClassify` returns class logits; `EfficientNetV2Model` returns features (`as_backbone=True` for multi-scale stages).
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+ - See [docs](https://imvision12.github.io/KerasFormers/classification_backbones/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
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+ - Upstream / timm checkpoints: `EfficientNetV2ImageClassify.from_weights("hf:timm/tf_efficientnetv2_b2.in1k")`.
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+ ## Special Thanks
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+ A huge thank you to the EfficientNetV2 authors and the timm / Hub communities for creating and releasing these models.
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+ License: see YAML `license` (usually matches the upstream checkpoint).