docs: Unsloth-style KerasFormers model card for sam_vit_huge

README.md

@@ -1,21 +1,91 @@
 ---
 pipeline_tag: mask-generation
 license: apache-2.0
+base_model: facebook/sam-vit-huge
 library_name: kerasformers
 tags:
 - keras
 - kerasformers
 - sam
-- tf
-- jax
+- mask-generation
+- image-segmentation
+- arxiv:2304.02643
 - pytorch
+- jax
+- tf
 ---
 
-# sam_vit_huge (Keras 3)
+## ***See [our collection](https://huggingface.co/collections/kerasformers/sam-v1-v2-v3-6a6a8c261dabbc2996e1b4a2) for all versions of SAM.***
+
+# Run SAM 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-SAM-blue)](https://imvision12.github.io/KerasFormers/sam/) [![Collection](https://img.shields.io/badge/HF-SAM%20collection-yellow)](https://huggingface.co/collections/kerasformers/sam-v1-v2-v3-6a6a8c261dabbc2996e1b4a2)
+
+# kerasformers/sam_vit_huge
+
+Paper: [Segment Anything (arXiv:2304.02643)](https://arxiv.org/abs/2304.02643) · [HF Papers](https://huggingface.co/papers/2304.02643)
+
+SAM (Segment Anything Model) segments whatever you point at. It has no class vocabulary: you give it a prompt (a click or a box) and it returns a mask. A heavy ViT image encoder runs once per image; the prompt encoder and mask decoder are light enough for interactive use.
+
+For more details on the model, please go to Meta's original [model card](https://huggingface.co/facebook/sam-vit-huge).
+
+Pure-**Keras 3** conversion of [`facebook/sam-vit-huge`](https://huggingface.co/facebook/sam-vit-huge) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
 
-Pure-Keras 3 weights for [kerasformers](https://github.com/IMvision12/KerasFormers), mirrored from the source. License: `apache-2.0`.
+This is a **promptable segmentation** checkpoint (`SAMPromptableSegment`): point (and optional box) prompts, backbone ViT-H.
+
+## ✨ Quick start
 
 ```python
-from kerasformers.models.sam import SAMPromptableSegment
-model = SAMPromptableSegment.from_weights("sam_vit_huge")
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+import numpy as np
+from PIL import Image
+from kerasformers.models.sam import (
+    SAMPromptableSegment,
+    SAMImageProcessorWithPrompts,
+)
+
+model = SAMPromptableSegment.from_weights("kerasformers/sam_vit_huge")
+processor = SAMImageProcessorWithPrompts()
+
+image = Image.open("your_image.jpg").convert("RGB")
+inputs = processor(
+    image,
+    input_points=np.array([[[[450, 200]]]], dtype="float32"),
+    input_labels=np.array([[[1]]], dtype="int32"),
+)
+META = ("original_size", "reshaped_size")
+output = model({k: v for k, v in inputs.items() if k not in META})
+masks = processor.post_process_masks(
+    output["pred_masks"], original_size=inputs["original_size"]
+)
+print(output["iou_scores"].shape, masks.shape)
 ```
+
+Load any SAM / SAM2 / SAM3 variant the same way with `from_weights("kerasformers/<variant>")` (use `SAMPromptableSegment` for this repo):
+
+| Variant | Hub | Family |
+|---|---|---|
+| `sam_vit_base` | [`kerasformers/sam_vit_base`](https://huggingface.co/kerasformers/sam_vit_base) | SAM |
+| `sam_vit_large` | [`kerasformers/sam_vit_large`](https://huggingface.co/kerasformers/sam_vit_large) | SAM |
+| `sam_vit_huge` | [`kerasformers/sam_vit_huge`](https://huggingface.co/kerasformers/sam_vit_huge) | SAM |
+| `sam2_hiera_small` | [`kerasformers/sam2_hiera_small`](https://huggingface.co/kerasformers/sam2_hiera_small) | SAM2 |
+| `sam2_hiera_base_plus` | [`kerasformers/sam2_hiera_base_plus`](https://huggingface.co/kerasformers/sam2_hiera_base_plus) | SAM2 |
+| `sam2_hiera_large` | [`kerasformers/sam2_hiera_large`](https://huggingface.co/kerasformers/sam2_hiera_large) | SAM2 |
+| `sam3` | [`kerasformers/sam3`](https://huggingface.co/kerasformers/sam3) | SAM3 |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- SAM / SAM2: point coordinates are in original pixel space; box prompts need `enable_boxes=True` / `include_box_input=True` when building the graph.
+- SAM2 in this port is image-only (no video memory bank).
+- SAM3: prefer `SAM3InstanceSegment.predict(...)` for text prompts; upstream `facebook/sam3` is gated.
+- See [SAM docs](https://imvision12.github.io/KerasFormers/sam/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `SAMPromptableSegment.from_weights("hf:facebook/sam-vit-huge")`.
+
+## Special Thanks
+
+A huge thank you to the Meta Segment Anything authors for creating and releasing these models.
+
+License: Apache 2.0.