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

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Files changed (3) hide show

README.md +17 -17
kf_config.json → zm_config.json +35 -35
kf_preprocessor.json → zm_preprocessor.json +18 -18

README.md CHANGED Viewed

@@ -2,10 +2,10 @@
 pipeline_tag: image-segmentation
 license: cc-by-nc-4.0
 base_model: facebook/maskformer-swin-tiny-coco
-library_name: kerasformers
 tags:
 - keras
-- kerasformers
 - maskformer
 - universal-segmentation
 - image-segmentation
@@ -15,13 +15,13 @@ tags:
 - tf
 ---
-## ***See [our collection](https://huggingface.co/collections/kerasformers/maskformer-6a6a8ece1c77558c676dfb9d) for all versions of MaskFormer.***
 # Run MaskFormer 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-MaskFormer-blue)](https://imvision12.github.io/KerasFormers/maskformer/) [![Collection](https://img.shields.io/badge/HF-MaskFormer%20collection-yellow)](https://huggingface.co/collections/kerasformers/maskformer-6a6a8ece1c77558c676dfb9d)
-# kerasformers/maskformer-swin-tiny-coco
 Paper: [Per-Pixel Classification is Not All You Need for Semantic Segmentation (arXiv:2107.06278)](https://arxiv.org/abs/2107.06278) · [HF Papers](https://huggingface.co/papers/2107.06278)
@@ -29,7 +29,7 @@ MaskFormer reframes segmentation as mask classification: a backbone and pixel de
 For more details on the model, please go to the upstream [model card](https://huggingface.co/facebook/maskformer-swin-tiny-coco).
-Pure-**Keras 3** conversion of [`facebook/maskformer-swin-tiny-coco`](https://huggingface.co/facebook/maskformer-swin-tiny-coco) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
 This is a **universal segmentation** checkpoint (`MaskFormerUniversalSegment`) trained on COCO panoptic.
@@ -40,10 +40,10 @@ import os
 os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
 from PIL import Image
-from kerasformers.models.maskformer import MaskFormerUniversalSegment, MaskFormerImageProcessor
-model = MaskFormerUniversalSegment.from_weights("kerasformers/maskformer-swin-tiny-coco")
-processor = MaskFormerImageProcessor.from_weights("kerasformers/maskformer-swin-tiny-coco")
 image = Image.open("your_image.jpg").convert("RGB")
 output = model(processor(image)["pixel_values"], training=False)
@@ -53,21 +53,21 @@ result = processor.post_process_panoptic_segmentation(
 print(result["segmentation"].shape)
 ```
-Load any MaskFormer variant the same way with `from_weights("kerasformers/<variant>")`:
 | Variant | Hub | Dataset |
 |---|---|---|
-| `maskformer-swin-tiny-coco` | [`kerasformers/maskformer-swin-tiny-coco`](https://huggingface.co/kerasformers/maskformer-swin-tiny-coco) | COCO |
-| `maskformer-swin-small-coco` | [`kerasformers/maskformer-swin-small-coco`](https://huggingface.co/kerasformers/maskformer-swin-small-coco) | COCO |
-| `maskformer-swin-base-coco` | [`kerasformers/maskformer-swin-base-coco`](https://huggingface.co/kerasformers/maskformer-swin-base-coco) | COCO |
-| `maskformer-swin-tiny-ade` | [`kerasformers/maskformer-swin-tiny-ade`](https://huggingface.co/kerasformers/maskformer-swin-tiny-ade) | ADE20K |
-| `maskformer-swin-base-ade` | [`kerasformers/maskformer-swin-base-ade`](https://huggingface.co/kerasformers/maskformer-swin-base-ade) | ADE20K |
 ## Tips
-- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
 - Prefer `MaskFormerImageProcessor.from_weights(...)` so resolution matches the variant.
-- See [MaskFormer docs]({DOCS_URL}) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
 - Community / upstream weights: `MaskFormerUniversalSegment.from_weights("hf:facebook/maskformer-swin-tiny-coco")`.
 ## Special Thanks

 pipeline_tag: image-segmentation
 license: cc-by-nc-4.0
 base_model: facebook/maskformer-swin-tiny-coco
+library_name: zeromodels
 tags:
 - keras
+- zeromodels
 - maskformer
 - universal-segmentation
 - image-segmentation
 - tf
 ---
+## ***See [our collection](https://huggingface.co/collections/zeromodels/maskformer-6a6a8ece1c77558c676dfb9d) for all versions of MaskFormer.***
 # Run MaskFormer 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-MaskFormer-blue)](https://imvision12.github.io/ZeroModels/maskformer/) [![Collection](https://img.shields.io/badge/HF-MaskFormer%20collection-yellow)](https://huggingface.co/collections/zeromodels/maskformer-6a6a8ece1c77558c676dfb9d)
+# zeromodels/maskformer-swin-tiny-coco
 Paper: [Per-Pixel Classification is Not All You Need for Semantic Segmentation (arXiv:2107.06278)](https://arxiv.org/abs/2107.06278) · [HF Papers](https://huggingface.co/papers/2107.06278)
 For more details on the model, please go to the upstream [model card](https://huggingface.co/facebook/maskformer-swin-tiny-coco).
+Pure-**Keras 3** conversion of [`facebook/maskformer-swin-tiny-coco`](https://huggingface.co/facebook/maskformer-swin-tiny-coco) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
 This is a **universal segmentation** checkpoint (`MaskFormerUniversalSegment`) trained on COCO panoptic.
 os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
 from PIL import Image
+from zeromodels.models.maskformer import MaskFormerUniversalSegment, MaskFormerImageProcessor
+model = MaskFormerUniversalSegment.from_weights("zeromodels/maskformer-swin-tiny-coco")
+processor = MaskFormerImageProcessor.from_weights("zeromodels/maskformer-swin-tiny-coco")
 image = Image.open("your_image.jpg").convert("RGB")
 output = model(processor(image)["pixel_values"], training=False)
 print(result["segmentation"].shape)
 ```
+Load any MaskFormer variant the same way with `from_weights("zeromodels/<variant>")`:
 | Variant | Hub | Dataset |
 |---|---|---|
+| `maskformer-swin-tiny-coco` | [`zeromodels/maskformer-swin-tiny-coco`](https://huggingface.co/zeromodels/maskformer-swin-tiny-coco) | COCO |
+| `maskformer-swin-small-coco` | [`zeromodels/maskformer-swin-small-coco`](https://huggingface.co/zeromodels/maskformer-swin-small-coco) | COCO |
+| `maskformer-swin-base-coco` | [`zeromodels/maskformer-swin-base-coco`](https://huggingface.co/zeromodels/maskformer-swin-base-coco) | COCO |
+| `maskformer-swin-tiny-ade` | [`zeromodels/maskformer-swin-tiny-ade`](https://huggingface.co/zeromodels/maskformer-swin-tiny-ade) | ADE20K |
+| `maskformer-swin-base-ade` | [`zeromodels/maskformer-swin-base-ade`](https://huggingface.co/zeromodels/maskformer-swin-base-ade) | ADE20K |
 ## Tips
+- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
 - Prefer `MaskFormerImageProcessor.from_weights(...)` so resolution matches the variant.
+- See [MaskFormer docs]({DOCS_URL}) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
 - Community / upstream weights: `MaskFormerUniversalSegment.from_weights("hf:facebook/maskformer-swin-tiny-coco")`.
 ## Special Thanks

kf_config.json → zm_config.json RENAMED Viewed

@@ -1,36 +1,36 @@
-{
-  "library_name": "kerasformers",
-  "kerasformers_version": "1.2.1",
-  "model_module": "kerasformers.models.maskformer",
-  "model_class": "MaskFormerUniversalSegment",
-  "variant": "maskformer-swin-tiny-coco",
-  "weights": "model.weights.h5",
-  "schema_version": 2,
-  "weight_dtype": "float32",
-  "model_type": "maskformer",
-  "vision_config": {
-    "backbone_embed_dim": 96,
-    "backbone_depths": [
-      2,
-      2,
-      6,
-      2
-    ],
-    "backbone_num_heads": [
-      3,
-      6,
-      12,
-      24
-    ],
-    "backbone_window_size": 7,
-    "fpn_feature_size": 256,
-    "mask_feature_size": 256,
-    "decoder_d_model": 256,
-    "decoder_num_layers": 6,
-    "decoder_heads": 8,
-    "decoder_ffn_dim": 2048,
-    "num_queries": 100,
-    "num_classes": 133,
-    "image_size": 384
-  }
 }

+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.2.1",
+  "model_module": "zeromodels.models.maskformer",
+  "model_class": "MaskFormerUniversalSegment",
+  "variant": "maskformer-swin-tiny-coco",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "maskformer",
+  "vision_config": {
+    "backbone_embed_dim": 96,
+    "backbone_depths": [
+      2,
+      2,
+      6,
+      2
+    ],
+    "backbone_num_heads": [
+      3,
+      6,
+      12,
+      24
+    ],
+    "backbone_window_size": 7,
+    "fpn_feature_size": 256,
+    "mask_feature_size": 256,
+    "decoder_d_model": 256,
+    "decoder_num_layers": 6,
+    "decoder_heads": 8,
+    "decoder_ffn_dim": 2048,
+    "num_queries": 100,
+    "num_classes": 133,
+    "image_size": 384
+  }
 }

kf_preprocessor.json → zm_preprocessor.json RENAMED Viewed

@@ -1,19 +1,19 @@
-{
-  "library_name": "kerasformers",
-  "kerasformers_version": "1.1.3",
-  "preprocessor_module": "kerasformers.models.maskformer",
-  "preprocessor_class": "MaskFormerImageProcessor",
-  "variant": null,
-  "target_size": 384,
-  "image_mean": [
-    0.485,
-    0.456,
-    0.406
-  ],
-  "image_std": [
-    0.229,
-    0.224,
-    0.225
-  ],
-  "data_format": null
 }

+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.1.3",
+  "preprocessor_module": "zeromodels.models.maskformer",
+  "preprocessor_class": "MaskFormerImageProcessor",
+  "variant": null,
+  "target_size": 384,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "data_format": null
 }

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

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