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maxvit_large_tf_512_in21k_ft_in1k

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1
- ---
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- pipeline_tag: image-classification
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- license: apache-2.0
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- base_model: timm/maxvit_large_tf_512.in21k_ft_in1k
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- library_name: zeromodels
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- tags:
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- - keras
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- - zeromodels
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- - image-classification
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- - maxvit
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- - backbone
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- - arxiv:2204.01697
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- - pytorch
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- - jax
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- - tf
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- ---
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-
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- ## ***See [our collection](https://huggingface.co/collections/zeromodels/maxvit-6a8eaee0e62bbd085eb81b76) for all versions of MaxViT.***
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-
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- # Run MaxViT with Keras 3: JAX, PyTorch, or TensorFlow
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-
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- [![GitHub](https://img.shields.io/badge/GitHub-ZeroModels-black?logo=github)](https://github.com/IMvision12/ZeroModels) [![Docs](https://img.shields.io/badge/Docs-MaxViT-blue)](https://imvision12.github.io/ZeroModels/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-MaxViT%20collection-yellow)](https://huggingface.co/collections/zeromodels/maxvit-6a8eaee0e62bbd085eb81b76)
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-
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- # zeromodels/maxvit_large_tf_512_in21k_ft_in1k
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-
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- Paper: [MaxViT: Multi-Axis Vision Transformer (arXiv:2204.01697)](https://arxiv.org/abs/2204.01697) · [HF Papers](https://huggingface.co/papers/2204.01697)
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-
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- MaxViT combines blocked local and dilated global attention (multi-axis) in a hierarchical CNN/Transformer hybrid.
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-
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- For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k).
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-
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- Pure-**Keras 3** conversion of [`timm/maxvit_large_tf_512.in21k_ft_in1k`](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
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-
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- This is an **image-classification / backbone** checkpoint (`MaxViTImageClassify` / `MaxViTModel`).
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-
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- ## ✨ Quick start
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-
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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 zeromodels.models.maxvit import MaxViTImageClassify, MaxViTModel
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-
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- model = MaxViTImageClassify.from_weights("zeromodels/maxvit_large_tf_512_in21k_ft_in1k")
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- backbone = MaxViTModel.from_weights(
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- "zeromodels/maxvit_large_tf_512_in21k_ft_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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-
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- Load any MaxViT variant the same way with `from_weights("zeromodels/<variant>")`:
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-
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- | Variant | Hub |
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- |---|---|
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- | `maxvit_base_tf_224_in1k` | [`zeromodels/maxvit_base_tf_224_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_224_in1k) |
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- | `maxvit_base_tf_224_in21k` | [`zeromodels/maxvit_base_tf_224_in21k`](https://huggingface.co/zeromodels/maxvit_base_tf_224_in21k) |
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- | `maxvit_base_tf_384_in1k` | [`zeromodels/maxvit_base_tf_384_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_384_in1k) |
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- | `maxvit_base_tf_384_in21k_ft_in1k` | [`zeromodels/maxvit_base_tf_384_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_384_in21k_ft_in1k) |
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- | `maxvit_base_tf_512_in1k` | [`zeromodels/maxvit_base_tf_512_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_512_in1k) |
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- | `maxvit_base_tf_512_in21k_ft_in1k` | [`zeromodels/maxvit_base_tf_512_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_512_in21k_ft_in1k) |
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- | `maxvit_large_tf_224_in1k` | [`zeromodels/maxvit_large_tf_224_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_224_in1k) |
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- | `maxvit_large_tf_224_in21k` | [`zeromodels/maxvit_large_tf_224_in21k`](https://huggingface.co/zeromodels/maxvit_large_tf_224_in21k) |
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- | `maxvit_large_tf_384_in1k` | [`zeromodels/maxvit_large_tf_384_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_384_in1k) |
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- | `maxvit_large_tf_384_in21k_ft_in1k` | [`zeromodels/maxvit_large_tf_384_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_384_in21k_ft_in1k) |
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- | `maxvit_large_tf_512_in1k` | [`zeromodels/maxvit_large_tf_512_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_512_in1k) |
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- | `maxvit_large_tf_512_in21k_ft_in1k` | [`zeromodels/maxvit_large_tf_512_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_512_in21k_ft_in1k) |
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- | `maxvit_small_tf_224_in1k` | [`zeromodels/maxvit_small_tf_224_in1k`](https://huggingface.co/zeromodels/maxvit_small_tf_224_in1k) |
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- | `maxvit_small_tf_384_in1k` | [`zeromodels/maxvit_small_tf_384_in1k`](https://huggingface.co/zeromodels/maxvit_small_tf_384_in1k) |
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- | `maxvit_small_tf_512_in1k` | [`zeromodels/maxvit_small_tf_512_in1k`](https://huggingface.co/zeromodels/maxvit_small_tf_512_in1k) |
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- | `maxvit_tiny_tf_224_in1k` | [`zeromodels/maxvit_tiny_tf_224_in1k`](https://huggingface.co/zeromodels/maxvit_tiny_tf_224_in1k) |
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- | `maxvit_tiny_tf_384_in1k` | [`zeromodels/maxvit_tiny_tf_384_in1k`](https://huggingface.co/zeromodels/maxvit_tiny_tf_384_in1k) |
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- | `maxvit_tiny_tf_512_in1k` | [`zeromodels/maxvit_tiny_tf_512_in1k`](https://huggingface.co/zeromodels/maxvit_tiny_tf_512_in1k) |
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- | `maxvit_xlarge_tf_224_in21k` | [`zeromodels/maxvit_xlarge_tf_224_in21k`](https://huggingface.co/zeromodels/maxvit_xlarge_tf_224_in21k) |
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- | `maxvit_xlarge_tf_384_in21k_ft_in1k` | [`zeromodels/maxvit_xlarge_tf_384_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_xlarge_tf_384_in21k_ft_in1k) |
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- | `maxvit_xlarge_tf_512_in21k_ft_in1k` | [`zeromodels/maxvit_xlarge_tf_512_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_xlarge_tf_512_in21k_ft_in1k) |
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-
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- ## Tips
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-
87
- - Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
88
- - `MaxViTImageClassify` returns class logits; `MaxViTModel` returns features (`as_backbone=True` for multi-scale stages).
89
- - See [docs](https://imvision12.github.io/ZeroModels/classification_backbones/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
90
- - Upstream / timm checkpoints: `MaxViTImageClassify.from_weights("hf:timm/maxvit_large_tf_512.in21k_ft_in1k")`.
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-
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- ## Special Thanks
93
-
94
- A huge thank you to the MaxViT authors and the timm / Hub communities for creating and releasing these models.
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-
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- License: see YAML `license` (usually matches the upstream checkpoint).
 
1
+ ---
2
+ pipeline_tag: image-classification
3
+ license: apache-2.0
4
+ base_model: timm/maxvit_large_tf_512.in21k_ft_in1k
5
+ library_name: zeromodels
6
+ tags:
7
+ - keras
8
+ - zeromodels
9
+ - image-classification
10
+ - maxvit
11
+ - backbone
12
+ - arxiv:2204.01697
13
+ - pytorch
14
+ - jax
15
+ - tf
16
+ ---
17
+
18
+ ## ***See [our collection](https://huggingface.co/collections/zeromodels/maxvit-6a8eaee0e62bbd085eb81b76) for all versions of MaxViT.***
19
+
20
+ # Run MaxViT 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-MaxViT-blue)](https://imvision12.github.io/ZeroModels/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-MaxViT%20collection-yellow)](https://huggingface.co/collections/zeromodels/maxvit-6a8eaee0e62bbd085eb81b76)
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+
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+ # zeromodels/maxvit_large_tf_512_in21k_ft_in1k
25
+
26
+ Paper: [MaxViT: Multi-Axis Vision Transformer (arXiv:2204.01697)](https://arxiv.org/abs/2204.01697) · [HF Papers](https://huggingface.co/papers/2204.01697)
27
+
28
+ MaxViT combines blocked local and dilated global attention (multi-axis) in a hierarchical CNN/Transformer hybrid.
29
+
30
+ For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k).
31
+
32
+ Pure-**Keras 3** conversion of [`timm/maxvit_large_tf_512.in21k_ft_in1k`](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_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 (`MaxViTImageClassify` / `MaxViTModel`).
35
+
36
+ ## ✨ Quick start
37
+
38
+ ```python
39
+ import os
40
+
41
+ os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
42
+
43
+ from PIL import Image
44
+ from zeromodels.models.maxvit import MaxViTImageClassify, MaxViTModel, MaxViTImageProcessor
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+
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+ model = MaxViTImageClassify.from_weights("zeromodels/maxvit_large_tf_512_in21k_ft_in1k")
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+ processor = MaxViTImageProcessor.from_weights("zeromodels/maxvit_large_tf_512_in21k_ft_in1k")
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+
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+ image = Image.open("your_image.jpg").convert("RGB")
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+ pixels = processor(image) # resize + normalize (normalization lives in the processor)
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+ logits = model(pixels, training=False)
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+ print(logits.shape) # (1, num_classes)
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+
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+ # Feature extraction: the backbone without the classifier head
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+ backbone = MaxViTModel.from_weights("zeromodels/maxvit_large_tf_512_in21k_ft_in1k", as_backbone=True)
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+ features = backbone(pixels, training=False)
57
+ ```
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+
59
+ Load any MaxViT variant the same way with `from_weights("zeromodels/<variant>")`:
60
+
61
+ | Variant | Hub |
62
+ |---|---|
63
+ | `maxvit_base_tf_224_in1k` | [`zeromodels/maxvit_base_tf_224_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_224_in1k) |
64
+ | `maxvit_base_tf_224_in21k` | [`zeromodels/maxvit_base_tf_224_in21k`](https://huggingface.co/zeromodels/maxvit_base_tf_224_in21k) |
65
+ | `maxvit_base_tf_384_in1k` | [`zeromodels/maxvit_base_tf_384_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_384_in1k) |
66
+ | `maxvit_base_tf_384_in21k_ft_in1k` | [`zeromodels/maxvit_base_tf_384_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_384_in21k_ft_in1k) |
67
+ | `maxvit_base_tf_512_in1k` | [`zeromodels/maxvit_base_tf_512_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_512_in1k) |
68
+ | `maxvit_base_tf_512_in21k_ft_in1k` | [`zeromodels/maxvit_base_tf_512_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_base_tf_512_in21k_ft_in1k) |
69
+ | `maxvit_large_tf_224_in1k` | [`zeromodels/maxvit_large_tf_224_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_224_in1k) |
70
+ | `maxvit_large_tf_224_in21k` | [`zeromodels/maxvit_large_tf_224_in21k`](https://huggingface.co/zeromodels/maxvit_large_tf_224_in21k) |
71
+ | `maxvit_large_tf_384_in1k` | [`zeromodels/maxvit_large_tf_384_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_384_in1k) |
72
+ | `maxvit_large_tf_384_in21k_ft_in1k` | [`zeromodels/maxvit_large_tf_384_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_384_in21k_ft_in1k) |
73
+ | `maxvit_large_tf_512_in1k` | [`zeromodels/maxvit_large_tf_512_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_512_in1k) |
74
+ | `maxvit_large_tf_512_in21k_ft_in1k` | [`zeromodels/maxvit_large_tf_512_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_large_tf_512_in21k_ft_in1k) |
75
+ | `maxvit_small_tf_224_in1k` | [`zeromodels/maxvit_small_tf_224_in1k`](https://huggingface.co/zeromodels/maxvit_small_tf_224_in1k) |
76
+ | `maxvit_small_tf_384_in1k` | [`zeromodels/maxvit_small_tf_384_in1k`](https://huggingface.co/zeromodels/maxvit_small_tf_384_in1k) |
77
+ | `maxvit_small_tf_512_in1k` | [`zeromodels/maxvit_small_tf_512_in1k`](https://huggingface.co/zeromodels/maxvit_small_tf_512_in1k) |
78
+ | `maxvit_tiny_tf_224_in1k` | [`zeromodels/maxvit_tiny_tf_224_in1k`](https://huggingface.co/zeromodels/maxvit_tiny_tf_224_in1k) |
79
+ | `maxvit_tiny_tf_384_in1k` | [`zeromodels/maxvit_tiny_tf_384_in1k`](https://huggingface.co/zeromodels/maxvit_tiny_tf_384_in1k) |
80
+ | `maxvit_tiny_tf_512_in1k` | [`zeromodels/maxvit_tiny_tf_512_in1k`](https://huggingface.co/zeromodels/maxvit_tiny_tf_512_in1k) |
81
+ | `maxvit_xlarge_tf_224_in21k` | [`zeromodels/maxvit_xlarge_tf_224_in21k`](https://huggingface.co/zeromodels/maxvit_xlarge_tf_224_in21k) |
82
+ | `maxvit_xlarge_tf_384_in21k_ft_in1k` | [`zeromodels/maxvit_xlarge_tf_384_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_xlarge_tf_384_in21k_ft_in1k) |
83
+ | `maxvit_xlarge_tf_512_in21k_ft_in1k` | [`zeromodels/maxvit_xlarge_tf_512_in21k_ft_in1k`](https://huggingface.co/zeromodels/maxvit_xlarge_tf_512_in21k_ft_in1k) |
84
+
85
+ ## Tips
86
+
87
+ - Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
88
+ - `MaxViTImageClassify` returns class logits; `MaxViTModel` returns features (`as_backbone=True` for multi-scale stages).
89
+ - See [docs](https://imvision12.github.io/ZeroModels/classification_backbones/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
90
+ - Upstream / timm checkpoints: `MaxViTImageClassify.from_weights("hf:timm/maxvit_large_tf_512.in21k_ft_in1k")`.
91
+
92
+ ## Special Thanks
93
+
94
+ A huge thank you to the MaxViT authors and the timm / Hub communities for creating and releasing these models.
95
+
96
+ License: see YAML `license` (usually matches the upstream checkpoint).