Add regnet-x-004

README.md

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+---
+pipeline_tag: image-classification
+license: apache-2.0
+base_model: facebook/regnet-x-004
+library_name: zeromodels
+tags:
+- keras
+- zeromodels
+- image-classification
+- regnet
+- backbone
+- arxiv:2003.13678
+- pytorch
+- jax
+- tf
+---
+
+## ***See [our collection](https://huggingface.co/collections/zeromodels/regnet-6a9270a4e723a861ea988d0b) for all versions of RegNet.***
+
+# Run RegNet with Keras 3: JAX, PyTorch, or TensorFlow
+
+[![GitHub](https://img.shields.io/badge/GitHub-ZeroModels-black?logo=github)](https://github.com/IMvision12/ZeroModels) [![Docs](https://img.shields.io/badge/Docs-RegNet-blue)](https://imvision12.github.io/ZeroModels/regnet/) [![Collection](https://img.shields.io/badge/HF-RegNet%20collection-yellow)](https://huggingface.co/collections/zeromodels/regnet-6a9270a4e723a861ea988d0b)
+
+# zeromodels/regnet-x-004
+
+Paper: [Designing Network Design Spaces (arXiv:2003.13678)](https://arxiv.org/abs/2003.13678) · [HF Papers](https://huggingface.co/papers/2003.13678)
+
+RegNet is a family of ConvNets whose per-stage widths and depths follow a simple quantized-linear rule. This is the **X** variant: a 3x3 stride-2 stem then four stages of `1x1 -> 3x3 grouped -> [SE] -> 1x1` residual blocks. Use as an ImageNet classifier or a 4-stage backbone.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/facebook/regnet-x-004).
+
+Pure-**Keras 3** conversion of [`facebook/regnet-x-004`](https://huggingface.co/facebook/regnet-x-004) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is an **image-classification / backbone** checkpoint (`RegNetImageClassify` / `RegNetModel`).
+
+## ✨ Quick start
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from PIL import Image
+import numpy as np
+from zeromodels.models.regnet import RegNetImageClassify, RegNetModel
+
+model = RegNetImageClassify.from_weights("zeromodels/regnet-x-004")
+backbone = RegNetModel.from_weights("zeromodels/regnet-x-004", 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 RegNet variant the same way with `from_weights("zeromodels/<variant>")`:
+
+| Variant | Hub |
+|---|---|
+| `regnet-x-002` | [`zeromodels/regnet-x-002`](https://huggingface.co/zeromodels/regnet-x-002) |
+| `regnet-x-004` | [`zeromodels/regnet-x-004`](https://huggingface.co/zeromodels/regnet-x-004) |
+| `regnet-x-006` | [`zeromodels/regnet-x-006`](https://huggingface.co/zeromodels/regnet-x-006) |
+| `regnet-x-008` | [`zeromodels/regnet-x-008`](https://huggingface.co/zeromodels/regnet-x-008) |
+| `regnet-x-016` | [`zeromodels/regnet-x-016`](https://huggingface.co/zeromodels/regnet-x-016) |
+| `regnet-x-032` | [`zeromodels/regnet-x-032`](https://huggingface.co/zeromodels/regnet-x-032) |
+| `regnet-x-040` | [`zeromodels/regnet-x-040`](https://huggingface.co/zeromodels/regnet-x-040) |
+| `regnet-x-064` | [`zeromodels/regnet-x-064`](https://huggingface.co/zeromodels/regnet-x-064) |
+| `regnet-x-080` | [`zeromodels/regnet-x-080`](https://huggingface.co/zeromodels/regnet-x-080) |
+| `regnet-x-120` | [`zeromodels/regnet-x-120`](https://huggingface.co/zeromodels/regnet-x-120) |
+| `regnet-x-160` | [`zeromodels/regnet-x-160`](https://huggingface.co/zeromodels/regnet-x-160) |
+| `regnet-x-320` | [`zeromodels/regnet-x-320`](https://huggingface.co/zeromodels/regnet-x-320) |
+| `regnet-y-002` | [`zeromodels/regnet-y-002`](https://huggingface.co/zeromodels/regnet-y-002) |
+| `regnet-y-004` | [`zeromodels/regnet-y-004`](https://huggingface.co/zeromodels/regnet-y-004) |
+| `regnet-y-006` | [`zeromodels/regnet-y-006`](https://huggingface.co/zeromodels/regnet-y-006) |
+| `regnet-y-008` | [`zeromodels/regnet-y-008`](https://huggingface.co/zeromodels/regnet-y-008) |
+| `regnet-y-016` | [`zeromodels/regnet-y-016`](https://huggingface.co/zeromodels/regnet-y-016) |
+| `regnet-y-032` | [`zeromodels/regnet-y-032`](https://huggingface.co/zeromodels/regnet-y-032) |
+| `regnet-y-040` | [`zeromodels/regnet-y-040`](https://huggingface.co/zeromodels/regnet-y-040) |
+| `regnet-y-064` | [`zeromodels/regnet-y-064`](https://huggingface.co/zeromodels/regnet-y-064) |
+| `regnet-y-080` | [`zeromodels/regnet-y-080`](https://huggingface.co/zeromodels/regnet-y-080) |
+| `regnet-y-120` | [`zeromodels/regnet-y-120`](https://huggingface.co/zeromodels/regnet-y-120) |
+| `regnet-y-160` | [`zeromodels/regnet-y-160`](https://huggingface.co/zeromodels/regnet-y-160) |
+| `regnet-y-320` | [`zeromodels/regnet-y-320`](https://huggingface.co/zeromodels/regnet-y-320) |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
+- `RegNetImageClassify` returns class logits; `RegNetModel` returns features (`as_backbone=True` for multi-scale stages at strides 4, 8, 16, 32).
+- Normalization is baked in (`include_normalization=True`): pass raw `[0, 255]` pixels.
+- Both `channels_last` and `channels_first` are supported (bit-exact); set the format before building.
+- See [docs](https://imvision12.github.io/ZeroModels/regnet/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
+- Upstream checkpoints: `RegNetImageClassify.from_weights("hf:facebook/regnet-x-004")`.
+
+## Special Thanks
+
+A huge thank you to the RegNet authors and the transformers / Hub communities for creating and releasing these models.
+
+License: see YAML `license` (matches the upstream checkpoint: apache-2.0).

model.weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9fe1f9f09fc88f2a766c2aaa02a88719ae088d90eb43b7339ddb487170c4003f
+size 21433656

zm_config.json

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+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.2.7",
+  "model_module": "zeromodels.models.regnet",
+  "model_class": "RegNetImageClassify",
+  "variant": "regnet-x-004",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "model_type": "regnet",
+  "vision_config": {
+    "embedding_size": 32,
+    "hidden_sizes": [
+      32,
+      64,
+      160,
+      384
+    ],
+    "depths": [
+      1,
+      2,
+      7,
+      12
+    ],
+    "groups_width": 16,
+    "layer_type": "x",
+    "downsample_in_first_stage": true,
+    "num_classes": 1000
+  }
+}