Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+pipeline_tag: zero-shot-object-detection
+license: apache-2.0
+base_model: IDEA-Research/grounding-dino-base
+library_name: kerasformers
+tags:
+- keras
+- kerasformers
+- grounding-dino
+- zero-shot-object-detection
+- arxiv:2303.05499
+- pytorch
+- jax
+- tf
+---
+
+# Run Grounding DINO 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-Grounding%20DINO-blue)](https://imvision12.github.io/KerasFormers/grounding_dino/)
+
+# kerasformers/grounding_dino_base
+
+Paper: [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection (arXiv:2303.05499)](https://arxiv.org/abs/2303.05499) · [HF Papers](https://huggingface.co/papers/2303.05499)
+
+Grounding DINO performs **open-set, text-grounded** object detection: it finds the objects a free-form text prompt names, not a fixed label set. A Swin image backbone and a BERT text encoder feed a deformable cross-modality encoder that fuses vision and language, a contrastive query-selection stage picks object proposals, and a decoder with iterative box refinement emits one box per query scored against the prompt tokens. No anchors, no NMS, and categories that were never in a detection training set (here "Swin-Base" backbone).
+
+For more details on the model, please go to IDEA-Research's original [model card](https://huggingface.co/IDEA-Research/grounding-dino-base).
+
+Pure-**Keras 3** conversion of [`IDEA-Research/grounding-dino-base`](https://huggingface.co/IDEA-Research/grounding-dino-base) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is an **open-set object detection** checkpoint (`GroundingDinoForObjectDetection`, Swin-Base backbone): each query predicts a box and a score over the prompt tokens.
+
+## ✨ Quick start
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+import torch
+from PIL import Image
+from kerasformers.models.grounding_dino import (
+    GroundingDinoForObjectDetection,
+    GroundingDinoProcessor,
+)
+
+model = GroundingDinoForObjectDetection.from_weights("kerasformers/grounding_dino_base")
+processor = GroundingDinoProcessor.from_weights("kerasformers/grounding_dino_base")
+
+image = Image.open("your_image.jpg").convert("RGB")
+# Prompts are free text; pass a list of candidates (or one "a. b. c." string). Skip
+# articles: in "a paddle" the "a" can outscore the noun.
+inputs = processor(images=image, text=["person", "paddle", "board"])
+
+with torch.no_grad():  # torch backend: avoids a large autograd graph (can OOM otherwise)
+    output = model(inputs)
+# output["logits"]:     (1, 900, 256)
+# output["pred_boxes"]: (1, 900, 4)
+
+results = processor.post_process_object_detection(
+    output,
+    threshold=0.3,
+    target_sizes=[(image.height, image.width)],
+    input_ids=inputs["input_ids"],
+)[0]
+for score, name, box in sorted(
+    zip(results["scores"], results["text_labels"], results["boxes"]),
+    key=lambda d: -float(d[0]),
+):
+    print(f"{name}: {float(score):.3f} {[round(float(v)) for v in box]}")
+```
+
+Load either Grounding DINO variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | Backbone |
+|---|---|---|
+| `grounding_dino_tiny` | [`kerasformers/grounding_dino_tiny`](https://huggingface.co/kerasformers/grounding_dino_tiny) | Swin-Tiny |
+| `grounding_dino_base` | [`kerasformers/grounding_dino_base`](https://huggingface.co/kerasformers/grounding_dino_base) | Swin-Base |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- On the **torch** backend, wrap inference in `with torch.no_grad():` — the forward keeps a large autograd graph otherwise and can OOM. The JAX / TensorFlow backends need no such wrap.
+- Write prompts as lower-case phrases separated as a list or by `.`; **drop articles** ("a", "the") so the noun scores highest. `post_process_object_detection` needs `input_ids=` to map scores back to prompt words (`text_labels`).
+- `threshold=0.3` is a reasonable start; raise it for cleaner scenes.
+- See [Grounding DINO docs](https://imvision12.github.io/KerasFormers/grounding_dino/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `GroundingDinoForObjectDetection.from_weights("hf:IDEA-Research/grounding-dino-base")`.
+
+## Special Thanks
+
+A huge thank you to the IDEA-Research authors for creating and releasing Grounding DINO.
+
+License: Apache 2.0.

kf_config.json

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+{
+  "library_name": "kerasformers",
+  "kerasformers_version": "1.2.1",
+  "model_module": "kerasformers.models.grounding_dino",
+  "model_class": "GroundingDinoDetect",
+  "variant": "grounding_dino_base",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "grounding-dino",
+  "vision_config": {
+    "d_model": 256,
+    "encoder_layers": 6,
+    "encoder_ffn_dim": 2048,
+    "encoder_attention_heads": 8,
+    "decoder_layers": 6,
+    "decoder_ffn_dim": 2048,
+    "decoder_attention_heads": 8,
+    "num_queries": 900,
+    "num_feature_levels": 4,
+    "encoder_n_points": 4,
+    "decoder_n_points": 4,
+    "max_text_len": 256,
+    "query_dim": 4,
+    "two_stage": true,
+    "positional_embedding_temperature": 20.0,
+    "layer_norm_eps": 1e-05,
+    "activation_function": "relu",
+    "backbone_embed_dim": 128,
+    "backbone_depths": [
+      2,
+      2,
+      18,
+      2
+    ],
+    "backbone_num_heads": [
+      4,
+      8,
+      16,
+      32
+    ],
+    "backbone_window_size": 12,
+    "backbone_out_indices": [
+      2,
+      3,
+      4
+    ],
+    "text_vocab_size": 30522,
+    "text_hidden_size": 768,
+    "text_num_layers": 12,
+    "text_num_heads": 12,
+    "text_intermediate_size": 3072,
+    "text_max_position_embeddings": 512,
+    "text_layer_norm_eps": 1e-12
+  }
+}

kf_preprocessor.json

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+{
+  "library_name": "kerasformers",
+  "kerasformers_version": "1.1.3",
+  "preprocessor_module": "kerasformers.models.grounding_dino",
+  "preprocessor_class": "GroundingDinoImageProcessor",
+  "variant": "grounding_dino_base",
+  "shortest_edge": 800,
+  "longest_edge": 1333,
+  "image_mean": [
+    0.48500001430511475,
+    0.4560000002384186,
+    0.4059999883174896
+  ],
+  "image_std": [
+    0.2290000021457672,
+    0.2240000069141388,
+    0.22499999403953552
+  ],
+  "data_format": null
+}

model.weights.h5

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