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tf_efficientnetv2_s_in1k

Image Classification
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PyTorch
JAX
TensorFlow
zeromodels
efficientnetv2
backbone
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IMvision12 commited on
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Migrate to zeromodels (rename kf_*.json -> zm_*.json, fix refs in config + README, ensure tag + badge)

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  1. README.md +28 -28
  2. kf_config.json → zm_config.json +19 -19
README.md CHANGED
@@ -2,10 +2,10 @@
2
  pipeline_tag: image-classification
3
  license: apache-2.0
4
  base_model: timm/tf_efficientnetv2_s.in1k
5
- library_name: kerasformers
6
  tags:
7
  - keras
8
- - kerasformers
9
  - image-classification
10
  - efficientnetv2
11
  - backbone
@@ -15,13 +15,13 @@ tags:
15
  - tf
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  ---
17
 
18
- ## ***See [our collection](https://huggingface.co/collections/kerasformers/efficientnetv2-6a6d11b8c2748868ecfd4f9e) for all versions of EfficientNetV2.***
19
 
20
  # Run EfficientNetV2 with Keras 3: JAX, PyTorch, or TensorFlow
21
 
22
- [![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)
23
 
24
- # kerasformers/tf_efficientnetv2_s_in1k
25
 
26
  Paper: [EfficientNetV2: Smaller Models and Faster Training (arXiv:2104.00298)](https://arxiv.org/abs/2104.00298) · [HF Papers](https://huggingface.co/papers/2104.00298)
27
 
@@ -29,7 +29,7 @@ EfficientNetV2 trains faster with Fused-MBConv and progressive learning. Same Im
29
 
30
  For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/tf_efficientnetv2_s.in1k).
31
 
32
- Pure-**Keras 3** conversion of [`timm/tf_efficientnetv2_s.in1k`](https://huggingface.co/timm/tf_efficientnetv2_s.in1k) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
33
 
34
  This is an **image-classification / backbone** checkpoint (`EfficientNetV2ImageClassify` / `EfficientNetV2Model`).
35
 
@@ -41,11 +41,11 @@ os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
41
 
42
  from PIL import Image
43
  import numpy as np
44
- from kerasformers.models.efficientnetv2 import EfficientNetV2ImageClassify, EfficientNetV2Model
45
 
46
- model = EfficientNetV2ImageClassify.from_weights("kerasformers/tf_efficientnetv2_s_in1k")
47
  backbone = EfficientNetV2Model.from_weights(
48
- "kerasformers/tf_efficientnetv2_s_in1k", as_backbone=True
49
  )
50
 
51
  image = Image.open("your_image.jpg").convert("RGB")
@@ -56,32 +56,32 @@ feats = backbone(x)
56
  print(len(feats), [tuple(f.shape) for f in feats])
57
  ```
58
 
59
- Load any EfficientNetV2 variant the same way with `from_weights("kerasformers/<variant>")`:
60
 
61
  | Variant | Hub |
62
  |---|---|
63
- | `tf_efficientnetv2_b0_in1k` | [`kerasformers/tf_efficientnetv2_b0_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b0_in1k) |
64
- | `tf_efficientnetv2_b1_in1k` | [`kerasformers/tf_efficientnetv2_b1_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b1_in1k) |
65
- | `tf_efficientnetv2_b2_in1k` | [`kerasformers/tf_efficientnetv2_b2_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b2_in1k) |
66
- | `tf_efficientnetv2_b3_in1k` | [`kerasformers/tf_efficientnetv2_b3_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b3_in1k) |
67
- | `tf_efficientnetv2_b3_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_b3_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_b3_in21k_ft_in1k) |
68
- | `tf_efficientnetv2_l_in1k` | [`kerasformers/tf_efficientnetv2_l_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_l_in1k) |
69
- | `tf_efficientnetv2_l_in21k` | [`kerasformers/tf_efficientnetv2_l_in21k`](https://huggingface.co/kerasformers/tf_efficientnetv2_l_in21k) |
70
- | `tf_efficientnetv2_l_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_l_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_l_in21k_ft_in1k) |
71
- | `tf_efficientnetv2_m_in1k` | [`kerasformers/tf_efficientnetv2_m_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_m_in1k) |
72
- | `tf_efficientnetv2_m_in21k` | [`kerasformers/tf_efficientnetv2_m_in21k`](https://huggingface.co/kerasformers/tf_efficientnetv2_m_in21k) |
73
- | `tf_efficientnetv2_m_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_m_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_m_in21k_ft_in1k) |
74
- | `tf_efficientnetv2_s_in1k` | [`kerasformers/tf_efficientnetv2_s_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_s_in1k) |
75
- | `tf_efficientnetv2_s_in21k` | [`kerasformers/tf_efficientnetv2_s_in21k`](https://huggingface.co/kerasformers/tf_efficientnetv2_s_in21k) |
76
- | `tf_efficientnetv2_s_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_s_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_s_in21k_ft_in1k) |
77
- | `tf_efficientnetv2_xl_in21k` | [`kerasformers/tf_efficientnetv2_xl_in21k`](https://huggingface.co/kerasformers/tf_efficientnetv2_xl_in21k) |
78
- | `tf_efficientnetv2_xl_in21k_ft_in1k` | [`kerasformers/tf_efficientnetv2_xl_in21k_ft_in1k`](https://huggingface.co/kerasformers/tf_efficientnetv2_xl_in21k_ft_in1k) |
79
 
80
  ## Tips
81
 
82
- - Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
83
  - `EfficientNetV2ImageClassify` returns class logits; `EfficientNetV2Model` returns features (`as_backbone=True` for multi-scale stages).
84
- - See [docs](https://imvision12.github.io/KerasFormers/classification_backbones/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
85
  - Upstream / timm checkpoints: `EfficientNetV2ImageClassify.from_weights("hf:timm/tf_efficientnetv2_s.in1k")`.
86
 
87
  ## Special Thanks
 
2
  pipeline_tag: image-classification
3
  license: apache-2.0
4
  base_model: timm/tf_efficientnetv2_s.in1k
5
+ library_name: zeromodels
6
  tags:
7
  - keras
8
+ - zeromodels
9
  - image-classification
10
  - efficientnetv2
11
  - backbone
 
15
  - tf
16
  ---
17
 
18
+ ## ***See [our collection](https://huggingface.co/collections/zeromodels/efficientnetv2-6a6d11b8c2748868ecfd4f9e) for all versions of EfficientNetV2.***
19
 
20
  # Run EfficientNetV2 with Keras 3: JAX, PyTorch, or TensorFlow
21
 
22
+ [![GitHub](https://img.shields.io/badge/GitHub-ZeroModels-black?logo=github)](https://github.com/IMvision12/ZeroModels) [![Docs](https://img.shields.io/badge/Docs-EfficientNetV2-blue)](https://imvision12.github.io/ZeroModels/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-EfficientNetV2%20collection-yellow)](https://huggingface.co/collections/zeromodels/efficientnetv2-6a6d11b8c2748868ecfd4f9e)
23
 
24
+ # zeromodels/tf_efficientnetv2_s_in1k
25
 
26
  Paper: [EfficientNetV2: Smaller Models and Faster Training (arXiv:2104.00298)](https://arxiv.org/abs/2104.00298) · [HF Papers](https://huggingface.co/papers/2104.00298)
27
 
 
29
 
30
  For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/tf_efficientnetv2_s.in1k).
31
 
32
+ Pure-**Keras 3** conversion of [`timm/tf_efficientnetv2_s.in1k`](https://huggingface.co/timm/tf_efficientnetv2_s.in1k) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
33
 
34
  This is an **image-classification / backbone** checkpoint (`EfficientNetV2ImageClassify` / `EfficientNetV2Model`).
35
 
 
41
 
42
  from PIL import Image
43
  import numpy as np
44
+ from zeromodels.models.efficientnetv2 import EfficientNetV2ImageClassify, EfficientNetV2Model
45
 
46
+ model = EfficientNetV2ImageClassify.from_weights("zeromodels/tf_efficientnetv2_s_in1k")
47
  backbone = EfficientNetV2Model.from_weights(
48
+ "zeromodels/tf_efficientnetv2_s_in1k", as_backbone=True
49
  )
50
 
51
  image = Image.open("your_image.jpg").convert("RGB")
 
56
  print(len(feats), [tuple(f.shape) for f in feats])
57
  ```
58
 
59
+ Load any EfficientNetV2 variant the same way with `from_weights("zeromodels/<variant>")`:
60
 
61
  | Variant | Hub |
62
  |---|---|
63
+ | `tf_efficientnetv2_b0_in1k` | [`zeromodels/tf_efficientnetv2_b0_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_b0_in1k) |
64
+ | `tf_efficientnetv2_b1_in1k` | [`zeromodels/tf_efficientnetv2_b1_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_b1_in1k) |
65
+ | `tf_efficientnetv2_b2_in1k` | [`zeromodels/tf_efficientnetv2_b2_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_b2_in1k) |
66
+ | `tf_efficientnetv2_b3_in1k` | [`zeromodels/tf_efficientnetv2_b3_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_b3_in1k) |
67
+ | `tf_efficientnetv2_b3_in21k_ft_in1k` | [`zeromodels/tf_efficientnetv2_b3_in21k_ft_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_b3_in21k_ft_in1k) |
68
+ | `tf_efficientnetv2_l_in1k` | [`zeromodels/tf_efficientnetv2_l_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_l_in1k) |
69
+ | `tf_efficientnetv2_l_in21k` | [`zeromodels/tf_efficientnetv2_l_in21k`](https://huggingface.co/zeromodels/tf_efficientnetv2_l_in21k) |
70
+ | `tf_efficientnetv2_l_in21k_ft_in1k` | [`zeromodels/tf_efficientnetv2_l_in21k_ft_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_l_in21k_ft_in1k) |
71
+ | `tf_efficientnetv2_m_in1k` | [`zeromodels/tf_efficientnetv2_m_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_m_in1k) |
72
+ | `tf_efficientnetv2_m_in21k` | [`zeromodels/tf_efficientnetv2_m_in21k`](https://huggingface.co/zeromodels/tf_efficientnetv2_m_in21k) |
73
+ | `tf_efficientnetv2_m_in21k_ft_in1k` | [`zeromodels/tf_efficientnetv2_m_in21k_ft_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_m_in21k_ft_in1k) |
74
+ | `tf_efficientnetv2_s_in1k` | [`zeromodels/tf_efficientnetv2_s_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_s_in1k) |
75
+ | `tf_efficientnetv2_s_in21k` | [`zeromodels/tf_efficientnetv2_s_in21k`](https://huggingface.co/zeromodels/tf_efficientnetv2_s_in21k) |
76
+ | `tf_efficientnetv2_s_in21k_ft_in1k` | [`zeromodels/tf_efficientnetv2_s_in21k_ft_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_s_in21k_ft_in1k) |
77
+ | `tf_efficientnetv2_xl_in21k` | [`zeromodels/tf_efficientnetv2_xl_in21k`](https://huggingface.co/zeromodels/tf_efficientnetv2_xl_in21k) |
78
+ | `tf_efficientnetv2_xl_in21k_ft_in1k` | [`zeromodels/tf_efficientnetv2_xl_in21k_ft_in1k`](https://huggingface.co/zeromodels/tf_efficientnetv2_xl_in21k_ft_in1k) |
79
 
80
  ## Tips
81
 
82
+ - Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
83
  - `EfficientNetV2ImageClassify` returns class logits; `EfficientNetV2Model` returns features (`as_backbone=True` for multi-scale stages).
84
+ - See [docs](https://imvision12.github.io/ZeroModels/classification_backbones/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
85
  - Upstream / timm checkpoints: `EfficientNetV2ImageClassify.from_weights("hf:timm/tf_efficientnetv2_s.in1k")`.
86
 
87
  ## Special Thanks
kf_config.json → zm_config.json RENAMED
@@ -1,20 +1,20 @@
1
- {
2
- "library_name": "kerasformers",
3
- "kerasformers_version": "1.2.1",
4
- "model_module": "kerasformers.models.efficientnetv2",
5
- "model_class": "EfficientNetV2ImageClassify",
6
- "variant": "tf_efficientnetv2_s_in1k",
7
- "weights": "model.weights.h5",
8
- "schema_version": 2,
9
- "weight_dtype": "float32",
10
- "model_type": "efficientnetv2",
11
- "vision_config": {
12
- "width_coefficient": 1.0,
13
- "depth_coefficient": 1.0,
14
- "default_size": 300,
15
- "block_arch": "EfficientNetV2S",
16
- "head_filters": 1280,
17
- "image_size": 300,
18
- "num_classes": 1000
19
- }
20
  }
 
1
+ {
2
+ "library_name": "zeromodels",
3
+ "zeromodels_version": "1.2.1",
4
+ "model_module": "zeromodels.models.efficientnetv2",
5
+ "model_class": "EfficientNetV2ImageClassify",
6
+ "variant": "tf_efficientnetv2_s_in1k",
7
+ "weights": "model.weights.h5",
8
+ "schema_version": 2,
9
+ "weight_dtype": "float32",
10
+ "model_type": "efficientnetv2",
11
+ "vision_config": {
12
+ "width_coefficient": 1.0,
13
+ "depth_coefficient": 1.0,
14
+ "default_size": 300,
15
+ "block_arch": "EfficientNetV2S",
16
+ "head_filters": 1280,
17
+ "image_size": 300,
18
+ "num_classes": 1000
19
+ }
20
  }