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resnet101_tv_in1k

Image Classification
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PyTorch
JAX
TensorFlow
resnet
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 +21 -21
  2. kf_config.json → zm_config.json +25 -25
README.md CHANGED
@@ -2,10 +2,10 @@
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  pipeline_tag: image-classification
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  license: bsd-3-clause
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  base_model: timm/resnet101.tv_in1k
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- library_name: kerasformers
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  tags:
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  - keras
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- - kerasformers
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  - image-classification
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  - resnet
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  - backbone
@@ -15,13 +15,13 @@ tags:
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  - tf
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  ---
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- ## ***See [our collection](https://huggingface.co/collections/kerasformers/resnet-6a6bdb1828cbb9f69b42cde0) for all versions of ResNet.***
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  # Run ResNet 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-ResNet-blue)](https://imvision12.github.io/KerasFormers/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-ResNet%20collection-yellow)](https://huggingface.co/collections/kerasformers/resnet-6a6bdb1828cbb9f69b42cde0)
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- # kerasformers/resnet101_tv_in1k
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  Paper: [Deep Residual Learning for Image Recognition (arXiv:1512.03385)](https://arxiv.org/abs/1512.03385) · [HF Papers](https://huggingface.co/papers/1512.03385)
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@@ -29,7 +29,7 @@ ResNet is the residual CNN backbone that introduced skip connections. Use `ResNe
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  For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/resnet101.tv_in1k).
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- Pure-**Keras 3** conversion of [`timm/resnet101.tv_in1k`](https://huggingface.co/timm/resnet101.tv_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 (`ResNetImageClassify` / `ResNetModel`).
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@@ -41,11 +41,11 @@ os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
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  from PIL import Image
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  import numpy as np
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- from kerasformers.models.resnet import ResNetImageClassify, ResNetModel
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46
- model = ResNetImageClassify.from_weights("kerasformers/resnet101_tv_in1k")
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  backbone = ResNetModel.from_weights(
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- "kerasformers/resnet101_tv_in1k", as_backbone=True
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  )
50
 
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  image = Image.open("your_image.jpg").convert("RGB")
@@ -56,25 +56,25 @@ feats = backbone(x)
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  print(len(feats), [tuple(f.shape) for f in feats])
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  ```
58
 
59
- Load any ResNet variant the same way with `from_weights("kerasformers/<variant>")`:
60
 
61
  | Variant | Hub |
62
  |---|---|
63
- | `resnet101_a1_in1k` | [`kerasformers/resnet101_a1_in1k`](https://huggingface.co/kerasformers/resnet101_a1_in1k) |
64
- | `resnet101_gluon_in1k` | [`kerasformers/resnet101_gluon_in1k`](https://huggingface.co/kerasformers/resnet101_gluon_in1k) |
65
- | `resnet101_tv_in1k` | [`kerasformers/resnet101_tv_in1k`](https://huggingface.co/kerasformers/resnet101_tv_in1k) |
66
- | `resnet152_a1_in1k` | [`kerasformers/resnet152_a1_in1k`](https://huggingface.co/kerasformers/resnet152_a1_in1k) |
67
- | `resnet152_gluon_in1k` | [`kerasformers/resnet152_gluon_in1k`](https://huggingface.co/kerasformers/resnet152_gluon_in1k) |
68
- | `resnet152_tv_in1k` | [`kerasformers/resnet152_tv_in1k`](https://huggingface.co/kerasformers/resnet152_tv_in1k) |
69
- | `resnet50_a1_in1k` | [`kerasformers/resnet50_a1_in1k`](https://huggingface.co/kerasformers/resnet50_a1_in1k) |
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- | `resnet50_gluon_in1k` | [`kerasformers/resnet50_gluon_in1k`](https://huggingface.co/kerasformers/resnet50_gluon_in1k) |
71
- | `resnet50_tv_in1k` | [`kerasformers/resnet50_tv_in1k`](https://huggingface.co/kerasformers/resnet50_tv_in1k) |
72
 
73
  ## Tips
74
 
75
- - Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
76
  - `ResNetImageClassify` returns class logits; `ResNetModel` returns features (`as_backbone=True` for multi-scale stages).
77
- - See [docs](https://imvision12.github.io/KerasFormers/classification_backbones/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
78
  - Upstream / timm checkpoints: `ResNetImageClassify.from_weights("hf:timm/resnet101.tv_in1k")`.
79
 
80
  ## Special Thanks
 
2
  pipeline_tag: image-classification
3
  license: bsd-3-clause
4
  base_model: timm/resnet101.tv_in1k
5
+ library_name: zeromodels
6
  tags:
7
  - keras
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+ - zeromodels
9
  - image-classification
10
  - resnet
11
  - backbone
 
15
  - tf
16
  ---
17
 
18
+ ## ***See [our collection](https://huggingface.co/collections/zeromodels/resnet-6a6bdb1828cbb9f69b42cde0) for all versions of ResNet.***
19
 
20
  # Run ResNet 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-ResNet-blue)](https://imvision12.github.io/ZeroModels/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-ResNet%20collection-yellow)](https://huggingface.co/collections/zeromodels/resnet-6a6bdb1828cbb9f69b42cde0)
23
 
24
+ # zeromodels/resnet101_tv_in1k
25
 
26
  Paper: [Deep Residual Learning for Image Recognition (arXiv:1512.03385)](https://arxiv.org/abs/1512.03385) · [HF Papers](https://huggingface.co/papers/1512.03385)
27
 
 
29
 
30
  For more details on the model, please go to the upstream [model card](https://huggingface.co/timm/resnet101.tv_in1k).
31
 
32
+ Pure-**Keras 3** conversion of [`timm/resnet101.tv_in1k`](https://huggingface.co/timm/resnet101.tv_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 (`ResNetImageClassify` / `ResNetModel`).
35
 
 
41
 
42
  from PIL import Image
43
  import numpy as np
44
+ from zeromodels.models.resnet import ResNetImageClassify, ResNetModel
45
 
46
+ model = ResNetImageClassify.from_weights("zeromodels/resnet101_tv_in1k")
47
  backbone = ResNetModel.from_weights(
48
+ "zeromodels/resnet101_tv_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 ResNet variant the same way with `from_weights("zeromodels/<variant>")`:
60
 
61
  | Variant | Hub |
62
  |---|---|
63
+ | `resnet101_a1_in1k` | [`zeromodels/resnet101_a1_in1k`](https://huggingface.co/zeromodels/resnet101_a1_in1k) |
64
+ | `resnet101_gluon_in1k` | [`zeromodels/resnet101_gluon_in1k`](https://huggingface.co/zeromodels/resnet101_gluon_in1k) |
65
+ | `resnet101_tv_in1k` | [`zeromodels/resnet101_tv_in1k`](https://huggingface.co/zeromodels/resnet101_tv_in1k) |
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+ | `resnet152_a1_in1k` | [`zeromodels/resnet152_a1_in1k`](https://huggingface.co/zeromodels/resnet152_a1_in1k) |
67
+ | `resnet152_gluon_in1k` | [`zeromodels/resnet152_gluon_in1k`](https://huggingface.co/zeromodels/resnet152_gluon_in1k) |
68
+ | `resnet152_tv_in1k` | [`zeromodels/resnet152_tv_in1k`](https://huggingface.co/zeromodels/resnet152_tv_in1k) |
69
+ | `resnet50_a1_in1k` | [`zeromodels/resnet50_a1_in1k`](https://huggingface.co/zeromodels/resnet50_a1_in1k) |
70
+ | `resnet50_gluon_in1k` | [`zeromodels/resnet50_gluon_in1k`](https://huggingface.co/zeromodels/resnet50_gluon_in1k) |
71
+ | `resnet50_tv_in1k` | [`zeromodels/resnet50_tv_in1k`](https://huggingface.co/zeromodels/resnet50_tv_in1k) |
72
 
73
  ## Tips
74
 
75
+ - Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
76
  - `ResNetImageClassify` returns class logits; `ResNetModel` returns features (`as_backbone=True` for multi-scale stages).
77
+ - See [docs](https://imvision12.github.io/ZeroModels/classification_backbones/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
78
  - Upstream / timm checkpoints: `ResNetImageClassify.from_weights("hf:timm/resnet101.tv_in1k")`.
79
 
80
  ## Special Thanks
kf_config.json → zm_config.json RENAMED
@@ -1,26 +1,26 @@
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- {
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- "library_name": "kerasformers",
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- "kerasformers_version": "1.2.1",
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- "model_module": "kerasformers.models.resnet",
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- "model_class": "ResNetImageClassify",
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- "variant": "resnet101_tv_in1k",
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- "weights": "model.weights.h5",
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- "schema_version": 2,
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- "weight_dtype": "float32",
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- "model_type": "resnet",
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- "vision_config": {
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- "depths": [
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- 3,
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- 4,
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- 23,
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- 3
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- ],
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- "filters": [
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- 64,
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- 128,
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- 256,
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- 512
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- ],
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- "num_classes": 1000
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- }
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  }
 
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+ {
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+ "library_name": "zeromodels",
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+ "zeromodels_version": "1.2.1",
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+ "model_module": "zeromodels.models.resnet",
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+ "model_class": "ResNetImageClassify",
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+ "variant": "resnet101_tv_in1k",
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+ "weights": "model.weights.h5",
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+ "schema_version": 2,
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+ "weight_dtype": "float32",
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+ "model_type": "resnet",
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+ "vision_config": {
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+ "depths": [
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+ 3,
14
+ 4,
15
+ 23,
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+ 3
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+ ],
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+ "filters": [
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+ 64,
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+ 128,
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+ 256,
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+ 512
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+ ],
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+ "num_classes": 1000
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+ }
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  }