Migrate to zeromodels (rename kf_*.json -> zm_*.json, fix refs in config + README, ensure tag + badge)

README.md

@@ -2,10 +2,10 @@
 pipeline_tag: image-classification
 license: other
 base_model: nvidia/mit-b1
-library_name: kerasformers
+library_name: zeromodels
 tags:
 - keras
-- kerasformers
+- zeromodels
 - image-classification
 - mit
 - backbone
@@ -15,13 +15,13 @@ tags:
 - tf
 ---
 
-## ***See [our collection](https://huggingface.co/collections/kerasformers/mit-segformer-encoder-6a6e81367fda42bf79b426e8) for all versions of MiT.***
+## ***See [our collection](https://huggingface.co/collections/zeromodels/mit-segformer-encoder-6a6e81367fda42bf79b426e8) for all versions of MiT.***
 
 # Run MiT with Keras 3: JAX, PyTorch, or TensorFlow
 
-[![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-black?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-MiT-blue)](https://imvision12.github.io/KerasFormers/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-MiT%20collection-yellow)](https://huggingface.co/collections/kerasformers/mit-segformer-encoder-6a6e81367fda42bf79b426e8)
+[![GitHub](https://img.shields.io/badge/GitHub-ZeroModels-black?logo=github)](https://github.com/IMvision12/ZeroModels) [![Docs](https://img.shields.io/badge/Docs-MiT-blue)](https://imvision12.github.io/ZeroModels/classification_backbones/) [![Collection](https://img.shields.io/badge/HF-MiT%20collection-yellow)](https://huggingface.co/collections/zeromodels/mit-segformer-encoder-6a6e81367fda42bf79b426e8)
 
-# kerasformers/mit_b1_in1k
+# zeromodels/mit_b1_in1k
 
 Paper: [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers (arXiv:2105.15203)](https://arxiv.org/abs/2105.15203) · [HF Papers](https://huggingface.co/papers/2105.15203)
 
@@ -29,7 +29,7 @@ MiT is the hierarchical Mix Transformer encoder from SegFormer, also usable for
 
 For more details on the model, please go to the upstream [model card](https://huggingface.co/nvidia/mit-b1).
 
-Pure-**Keras 3** conversion of [`nvidia/mit-b1`](https://huggingface.co/nvidia/mit-b1) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+Pure-**Keras 3** conversion of [`nvidia/mit-b1`](https://huggingface.co/nvidia/mit-b1) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
 
 This is an **image-classification / backbone** checkpoint (`MiTImageClassify` / `MiTModel`).
 
@@ -41,11 +41,11 @@ os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
 
 from PIL import Image
 import numpy as np
-from kerasformers.models.mit import MiTImageClassify, MiTModel
+from zeromodels.models.mit import MiTImageClassify, MiTModel
 
-model = MiTImageClassify.from_weights("kerasformers/mit_b1_in1k")
+model = MiTImageClassify.from_weights("zeromodels/mit_b1_in1k")
 backbone = MiTModel.from_weights(
-    "kerasformers/mit_b1_in1k", as_backbone=True
+    "zeromodels/mit_b1_in1k", as_backbone=True
 )
 
 image = Image.open("your_image.jpg").convert("RGB")
@@ -56,22 +56,22 @@ feats = backbone(x)
 print(len(feats), [tuple(f.shape) for f in feats])
 ```
 
-Load any MiT variant the same way with `from_weights("kerasformers/<variant>")`:
+Load any MiT variant the same way with `from_weights("zeromodels/<variant>")`:
 
 | Variant | Hub |
 |---|---|
-| `mit_b0_in1k` | [`kerasformers/mit_b0_in1k`](https://huggingface.co/kerasformers/mit_b0_in1k) |
-| `mit_b1_in1k` | [`kerasformers/mit_b1_in1k`](https://huggingface.co/kerasformers/mit_b1_in1k) |
-| `mit_b2_in1k` | [`kerasformers/mit_b2_in1k`](https://huggingface.co/kerasformers/mit_b2_in1k) |
-| `mit_b3_in1k` | [`kerasformers/mit_b3_in1k`](https://huggingface.co/kerasformers/mit_b3_in1k) |
-| `mit_b4_in1k` | [`kerasformers/mit_b4_in1k`](https://huggingface.co/kerasformers/mit_b4_in1k) |
-| `mit_b5_in1k` | [`kerasformers/mit_b5_in1k`](https://huggingface.co/kerasformers/mit_b5_in1k) |
+| `mit_b0_in1k` | [`zeromodels/mit_b0_in1k`](https://huggingface.co/zeromodels/mit_b0_in1k) |
+| `mit_b1_in1k` | [`zeromodels/mit_b1_in1k`](https://huggingface.co/zeromodels/mit_b1_in1k) |
+| `mit_b2_in1k` | [`zeromodels/mit_b2_in1k`](https://huggingface.co/zeromodels/mit_b2_in1k) |
+| `mit_b3_in1k` | [`zeromodels/mit_b3_in1k`](https://huggingface.co/zeromodels/mit_b3_in1k) |
+| `mit_b4_in1k` | [`zeromodels/mit_b4_in1k`](https://huggingface.co/zeromodels/mit_b4_in1k) |
+| `mit_b5_in1k` | [`zeromodels/mit_b5_in1k`](https://huggingface.co/zeromodels/mit_b5_in1k) |
 
 ## Tips
 
-- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
 - `MiTImageClassify` returns class logits; `MiTModel` returns features (`as_backbone=True` for multi-scale stages).
-- See [docs](https://imvision12.github.io/KerasFormers/classification_backbones/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- See [docs](https://imvision12.github.io/ZeroModels/classification_backbones/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
 - Upstream / timm checkpoints: `MiTImageClassify.from_weights("hf:nvidia/mit-b1")`.
 
 ## Special Thanks

kf_config.json → zm_config.json

@@ -1,27 +1,27 @@
-{
-  "library_name": "kerasformers",
-  "kerasformers_version": "1.2.1",
-  "model_module": "kerasformers.models.mit",
-  "model_class": "MiTImageClassify",
-  "variant": "mit_b1_in1k",
-  "weights": "model.weights.h5",
-  "schema_version": 2,
-  "weight_dtype": "float32",
-  "model_type": "mit",
-  "vision_config": {
-    "embed_dim": [
-      64,
-      128,
-      320,
-      512
-    ],
-    "depths": [
-      2,
-      2,
-      2,
-      2
-    ],
-    "image_size": 224,
-    "num_classes": 1000
-  }
+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.2.1",
+  "model_module": "zeromodels.models.mit",
+  "model_class": "MiTImageClassify",
+  "variant": "mit_b1_in1k",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "mit",
+  "vision_config": {
+    "embed_dim": [
+      64,
+      128,
+      320,
+      512
+    ],
+    "depths": [
+      2,
+      2,
+      2,
+      2
+    ],
+    "image_size": 224,
+    "num_classes": 1000
+  }
 }