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README.md

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+---
+pipeline_tag: object-detection
+license: apache-2.0
+library_name: zeromodels
+tags:
+- keras
+- zeromodels
+- efficientdet
+- object-detection
+- arxiv:1911.09070
+- pytorch
+- jax
+- tf
+---
+
+## ***See [our collection](https://hf.co/collections/zeromodels/efficientdet) for all versions of EfficientDet.***
+
+# Run EfficientDet 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-EfficientDet-blue)](https://imvision12.github.io/ZeroModels/efficientdet/) [![Collection](https://img.shields.io/badge/HF-EfficientDet%20collection-yellow)](https://hf.co/collections/zeromodels/efficientdet)
+
+# zeromodels/efficientdet_d5
+
+Paper: [EfficientDet: Scalable and Efficient Object Detection (arXiv:1911.09070)](https://arxiv.org/abs/1911.09070) · [HF Papers](https://huggingface.co/papers/1911.09070)
+
+EfficientDet is a family of single-shot, anchor-based detectors built for a clean accuracy/compute trade-off. An EfficientNet-B5 backbone feeds a weighted bi-directional feature pyramid (BiFPN) that fuses multi-scale features with learnable per-input weights, and one shared class head and box head run over every pyramid level. This checkpoint runs at 1280x1280 over the 90 COCO categories.
+
+For more details on the model, see Google's original [AutoML EfficientDet repository](https://github.com/google/automl/tree/master/efficientdet).
+
+Pure-**Keras 3** conversion of Google AutoML's [EfficientDet](https://github.com/google/automl/tree/master/efficientdet) (`efficientdet-d5`) for [zeromodels](https://github.com/IMvision12/ZeroModels). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is an **object detection** checkpoint (`EfficientDetDetect`): the backbone, BiFPN and shared heads emit per-anchor boxes that are decoded against anchors and NMS-filtered into detections.
+
+## ✨ Quick start
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from PIL import Image
+from zeromodels.models.efficientdet import EfficientDetDetect, EfficientDetImageProcessor
+
+model = EfficientDetDetect.from_weights("zeromodels/efficientdet_d5")
+processor = EfficientDetImageProcessor.from_weights("zeromodels/efficientdet_d5")
+
+image = Image.open("your_image.jpg").convert("RGB")
+inputs = processor(image)
+output = model(inputs["pixel_values"], training=False)
+results = processor.post_process_object_detection(
+    output, threshold=0.3, target_sizes=inputs["original_sizes"]
+)[0]
+for score, name, box in zip(
+    results["scores"], results["label_names"], results["boxes"]
+):
+    print(f"{name}: {float(score):.3f} {[round(float(v)) for v in box]}")
+```
+
+Load any EfficientDet variant the same way with `from_weights("zeromodels/<variant>")` (use `EfficientDetDetect` for detection, `EfficientDetModel` for the raw head outputs):
+
+| Variant | Hub | Backbone | Input |
+|---|---|---|---|
+| `efficientdet_d0` | [`zeromodels/efficientdet_d0`](https://huggingface.co/zeromodels/efficientdet_d0) | EfficientNet-B0 | 512 |
+| `efficientdet_d1` | [`zeromodels/efficientdet_d1`](https://huggingface.co/zeromodels/efficientdet_d1) | EfficientNet-B1 | 640 |
+| `efficientdet_d2` | [`zeromodels/efficientdet_d2`](https://huggingface.co/zeromodels/efficientdet_d2) | EfficientNet-B2 | 768 |
+| `efficientdet_d3` | [`zeromodels/efficientdet_d3`](https://huggingface.co/zeromodels/efficientdet_d3) | EfficientNet-B3 | 896 |
+| `efficientdet_d4` | [`zeromodels/efficientdet_d4`](https://huggingface.co/zeromodels/efficientdet_d4) | EfficientNet-B4 | 1024 |
+| `efficientdet_d5` | [`zeromodels/efficientdet_d5`](https://huggingface.co/zeromodels/efficientdet_d5) | EfficientNet-B5 | 1280 |
+| `efficientdet_d6` | [`zeromodels/efficientdet_d6`](https://huggingface.co/zeromodels/efficientdet_d6) | EfficientNet-B6 | 1280 |
+| `efficientdet_d7` | [`zeromodels/efficientdet_d7`](https://huggingface.co/zeromodels/efficientdet_d7) | EfficientNet-B6 | 1536 |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / zeromodels.
+- Detection: `EfficientDetDetect` + `post_process_object_detection` (try `threshold=0.3`-`0.4`).
+- NMS is class-agnostic by default (one box per object); pass `class_agnostic=False` for per-class NMS.
+- `EfficientDetModel.from_weights(...)` loads the same weights without the decode head, returning raw per-level `class_outputs` / `box_outputs`.
+- Larger variants take a bigger input (D0 512 up to D7 1536); each side must be divisible by 128.
+- Community / fine-tuned repos hosted in the zeromodels format load with `from_weights("<org>/<repo>")`.
+- Weights are resolution-independent: pass `image_size=N` (a multiple of 128) to `from_weights` to run at a custom size.
+- See [EfficientDet docs](https://imvision12.github.io/ZeroModels/efficientdet/) and [Loading Weights](https://imvision12.github.io/ZeroModels/loading_weights/).
+
+## Special Thanks
+
+A huge thank you to the Google Brain / AutoML authors (Mingxing Tan, Ruoming Pang, Quoc V. Le) for creating and releasing EfficientDet.
+
+License: Apache 2.0.

model.weights.h5

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+size 138153712

zm_config.json

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+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.2.6",
+  "model_module": "zeromodels.models.efficientdet",
+  "model_class": "EfficientDetModel",
+  "variant": "efficientdet_d5",
+  "weights": "model.weights.h5",
+  "schema_version": 2,
+  "weight_dtype": "float32",
+  "model_type": "efficientdet",
+  "vision_config": {
+    "backbone_name": "efficientnet_b5",
+    "image_size": 1280,
+    "num_classes": 90,
+    "min_level": 3,
+    "max_level": 7,
+    "num_scales": 3,
+    "aspect_ratios": [
+      1.0,
+      2.0,
+      0.5
+    ],
+    "anchor_scale": 4.0,
+    "fpn_num_filters": 288,
+    "fpn_cell_repeats": 7,
+    "box_class_repeats": 4,
+    "act_type": "swish",
+    "separable_conv": true,
+    "apply_bn_for_resampling": true,
+    "conv_after_downsample": false,
+    "conv_bn_act_pattern": false,
+    "fpn_weight_method": "fastattn",
+    "survival_prob": null
+  }
+}

zm_preprocessor.json

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+{
+  "library_name": "zeromodels",
+  "zeromodels_version": "1.2.6",
+  "preprocessor_module": "zeromodels.models.efficientdet",
+  "preprocessor_class": "EfficientDetImageProcessor",
+  "variant": "efficientdet_d5",
+  "image_size": 1280,
+  "resample": "bilinear",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "rescale_factor": 0.00392156862745098,
+  "return_tensor": true,
+  "data_format": null
+}