Add zeromodels PVT weights + zm_config + model card

README.md

@@ -0,0 +1,85 @@
+---
+pipeline_tag: image-classification
+license: apache-2.0
+base_model: OpenGVLab/pvt_v2_b5
+library_name: zeromodels
+tags:
+- keras
+- zeromodels
+- image-classification
+- pvt-v2
+- backbone
+- arxiv:2106.13797
+- pytorch
+- jax
+- tf
+---
+
+## ***See [our collection](https://huggingface.co/collections/zeromodels/pvt-and-pvtv2-6a90e9dd0a2b03a982d0b876) for all PVT and PVTv2 versions.***
+
+# Run PVTv2 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-PVTv2-blue)](https://imvision12.github.io/ZeroModels/pvt_v2/) [![Collection](https://img.shields.io/badge/HF-PVTv2%20collection-yellow)](https://huggingface.co/collections/zeromodels/pvt-and-pvtv2-6a90e9dd0a2b03a982d0b876)
+
+# zeromodels/pvt-v2-b5
+
+Paper: [PVTv2: Improved Baselines with Pyramid Vision Transformer (arXiv:2106.13797)](https://arxiv.org/abs/2106.13797) · [HF Papers](https://huggingface.co/papers/2106.13797)
+
+PVTv2 improves PVT with overlapping patch embeddings, a convolutional feed-forward network, and no position embeddings (so any input resolution works), plus an optional linear-attention variant. Use `PvtV2ImageClassify` for logits or `PvtV2Model` for tokens / per-stage features via `as_backbone=True`.
+
+- Parameters: ~82.0M
+- ImageNet-1k top-1: **83.8%**
+
+For more details on the model, see the upstream [model card](https://huggingface.co/OpenGVLab/pvt_v2_b5).
+
+Pure-**Keras 3** conversion of [`OpenGVLab/pvt_v2_b5`](https://huggingface.co/OpenGVLab/pvt_v2_b5) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is an **image-classification / backbone** checkpoint (`PvtV2ImageClassify` / `PvtV2Model`).
+
+## ✨ Quick start
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from PIL import Image
+import numpy as np
+from zeromodels.models.pvt_v2 import PvtV2ImageClassify, PvtV2Model
+
+model = PvtV2ImageClassify.from_weights("zeromodels/pvt-v2-b5")
+backbone = PvtV2Model.from_weights("zeromodels/pvt-v2-b5", as_backbone=True)
+
+image = Image.open("your_image.jpg").convert("RGB").resize((224, 224))
+x = np.asarray(image, dtype="float32")[None]  # (1, H, W, 3), raw [0, 255]
+print(model(x).shape)  # (1, num_classes)
+feats = backbone(x)
+print(len(feats), [tuple(f.shape) for f in feats])  # 4-stage feature pyramid
+```
+
+Normalization is baked into the graph, so pass raw `[0, 255]` pixels. Load any PVTv2
+variant the same way with `from_weights("zeromodels/<variant>")`:
+
+| Variant | ImageNet-1k top-1 | Hub |
+|---|---|---|
+| `pvt-v2-b0` | 70.5% | [`zeromodels/pvt-v2-b0`](https://huggingface.co/zeromodels/pvt-v2-b0) |
+| `pvt-v2-b1` | 78.7% | [`zeromodels/pvt-v2-b1`](https://huggingface.co/zeromodels/pvt-v2-b1) |
+| `pvt-v2-b2` | 82.0% | [`zeromodels/pvt-v2-b2`](https://huggingface.co/zeromodels/pvt-v2-b2) |
+| `pvt-v2-b2-linear` | 82.1% | [`zeromodels/pvt-v2-b2-linear`](https://huggingface.co/zeromodels/pvt-v2-b2-linear) |
+| `pvt-v2-b3` | 83.1% | [`zeromodels/pvt-v2-b3`](https://huggingface.co/zeromodels/pvt-v2-b3) |
+| `pvt-v2-b4` | 83.6% | [`zeromodels/pvt-v2-b4`](https://huggingface.co/zeromodels/pvt-v2-b4) |
+| `pvt-v2-b5` | 83.8% | [`zeromodels/pvt-v2-b5`](https://huggingface.co/zeromodels/pvt-v2-b5) |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
+- `PvtV2ImageClassify` returns class logits; `PvtV2Model` returns features (`as_backbone=True` for the four-stage pyramid).
+- Both the model and its data format (`channels_last` / `channels_first`) are supported and bit-exact.
+- See the [docs](https://imvision12.github.io/ZeroModels/pvt_v2/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
+- Upstream checkpoints load directly: `PvtV2ImageClassify.from_weights("hf:OpenGVLab/pvt_v2_b5")`.
+
+## Special Thanks
+
+A huge thank you to the PVT authors ([whai362/PVT](https://github.com/whai362/PVT)) and the Hugging Face
+community for creating and releasing these models.
+
+License: see the YAML `license` above (matches the upstream checkpoint).

model.weights.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9036862130b567fd1709604d9591cc6d6b59af5dcba66de87b0b4054b473eb84
+size 330511008

zm_config.json

@@ -0,0 +1,45 @@
+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.2.6",
+  "model_module": "zeromodels.models.pvt_v2",
+  "model_class": "PvtV2ImageClassify",
+  "variant": "pvt-v2-b5",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "model_type": "pvt_v2",
+  "vision_config": {
+    "hidden_sizes": [
+      64,
+      128,
+      320,
+      512
+    ],
+    "depths": [
+      3,
+      6,
+      40,
+      3
+    ],
+    "num_attention_heads": [
+      1,
+      2,
+      5,
+      8
+    ],
+    "sr_ratios": [
+      8,
+      4,
+      2,
+      1
+    ],
+    "mlp_ratios": [
+      4,
+      4,
+      4,
+      4
+    ],
+    "linear_attention": false,
+    "image_size": 224,
+    "num_classes": 1000
+  }
+}