Add Google's OG Model Efficientdet

README.md

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 ## 📖 Introduction
 
-ZeroModels is a collection of models with pretrained weights, built entirely with Keras 3. It supports a range of tasks, including classification, object detection (DETR, RT-DETR, RT-DETRv2, RF-DETR, D-FINE, OWL-ViT, OWLv2, Grounding DINO), segmentation (SAM, SAM2, SAM3, SegFormer, DeepLabV3, EoMT, MaskFormer, Mask2Former, OneFormer, MobileViT-DeepLabV3, RF-DETR), monocular depth estimation (Depth Anything V1, Depth Anything V2, TIPSv2-DPT), feature extraction (DINO, DINOv2, DINOv3), vision-language modeling (CLIP, SigLIP, SigLIP2, MetaCLIP 2, TIPSv2), speech recognition (Whisper, Speech2Text, Moonshine, Granite Speech 5), speech-aware language modeling (Granite Speech, Granite Speech Plus), text encoding and masked language modeling (BERT, ModernBERT, ELECTRA, RoBERTa, XLM-RoBERTa, DeBERTa, DeBERTa-v2, DeBERTa-v3), text generation with large language models (GPT, GPT-2, Qwen2, Qwen2-MoE, Qwen3, Qwen3-MoE, Qwen3-Next, Qwen3.5, GPT-OSS, Llama 2, Llama 3, Llama 4, Mistral, Mixtral, Gemma, Gemma 2, MiniMax-Text-01, MiniMax-M2, DeepSeek-V2, DeepSeek-V3, DeepSeek-V4, GLM-4, GLM-4-0414, GLM-4.5/GLM-4.6, GLM-5/GLM-5.1/GLM-5.2), text-to-text encoder-decoder modeling (T5), multimodal vision-language generation (Qwen2-VL, Qwen2.5-VL, Qwen3-VL, Qwen3-VL-MoE, Qwen3.5-MoE, InternVL3, Gemma 3, Gemma 3n, Gemma 4, Gemma 4 Unified, Mistral 3, DeepSeek-VL, Janus-Pro, MiniMax-M3-VL, GLM-4V, GLM-4.5V, Kimi K2.5, Kimi K2.6, Kimi K2.7-Code), vision-language grounding across object detection, OCR, pointing, and referring (LocateAnything), and more. It includes hybrid architectures like MaxViT alongside traditional CNNs and pure transformers. zeromodels includes custom layers and backbone support, providing flexibility and efficiency across various applications. For backbones, there are various weight variants like `in1k`, `in21k`, `fb_dist_in1k`, `ms_in22k`, `fb_in22k_ft_in1k`, `ns_jft_in1k`, `aa_in1k`, `cvnets_in1k`, `augreg_in21k_ft_in1k`, `augreg_in21k`, and many more.
+ZeroModels is a collection of models with pretrained weights, built entirely with Keras 3. It supports a range of tasks, including classification, object detection (DETR, RT-DETR, RT-DETRv2, RF-DETR, D-FINE, EfficientDet, OWL-ViT, OWLv2, Grounding DINO), segmentation (SAM, SAM2, SAM3, SegFormer, DeepLabV3, EoMT, MaskFormer, Mask2Former, OneFormer, MobileViT-DeepLabV3, RF-DETR), monocular depth estimation (Depth Anything V1, Depth Anything V2, TIPSv2-DPT), feature extraction (DINO, DINOv2, DINOv3), vision-language modeling (CLIP, SigLIP, SigLIP2, MetaCLIP 2, TIPSv2), speech recognition (Whisper, Speech2Text, Moonshine, Granite Speech 5), speech-aware language modeling (Granite Speech, Granite Speech Plus), text encoding and masked language modeling (BERT, ModernBERT, ELECTRA, RoBERTa, XLM-RoBERTa, DeBERTa, DeBERTa-v2, DeBERTa-v3), text generation with large language models (GPT, GPT-2, Qwen2, Qwen2-MoE, Qwen3, Qwen3-MoE, Qwen3-Next, Qwen3.5, GPT-OSS, Llama 2, Llama 3, Llama 4, Mistral, Mixtral, Gemma, Gemma 2, MiniMax-Text-01, MiniMax-M2, DeepSeek-V2, DeepSeek-V3, DeepSeek-V4, GLM-4, GLM-4-0414, GLM-4.5/GLM-4.6, GLM-5/GLM-5.1/GLM-5.2), text-to-text encoder-decoder modeling (T5), multimodal vision-language generation (Qwen2-VL, Qwen2.5-VL, Qwen3-VL, Qwen3-VL-MoE, Qwen3.5-MoE, InternVL3, Gemma 3, Gemma 3n, Gemma 4, Gemma 4 Unified, Mistral 3, DeepSeek-VL, Janus-Pro, MiniMax-M3-VL, GLM-4V, GLM-4.5V, Kimi K2.5, Kimi K2.6, Kimi K2.7-Code), vision-language grounding across object detection, OCR, pointing, and referring (LocateAnything), and more. It includes hybrid architectures like MaxViT alongside traditional CNNs and pure transformers. zeromodels includes custom layers and backbone support, providing flexibility and efficiency across various applications. For backbones, there are various weight variants like `in1k`, `in21k`, `fb_dist_in1k`, `ms_in22k`, `fb_in22k_ft_in1k`, `ns_jft_in1k`, `aa_in1k`, `cvnets_in1k`, `augreg_in21k_ft_in1k`, `augreg_in21k`, and many more.
 
 ## ⚡ Installation
 
@@ -139,6 +139,7 @@ The Markdown sources live in [`docs/`](docs/) if you would rather read them in t
     |---------------|-------------------|---------------------|
     | D-FINE | [D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) | `transformers` |
     | DETR | [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) | `transformers` |
+    | EfficientDet | [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/abs/1911.09070) | `automl` |
     | RT-DETR | [DETRs Beat YOLOs on Real-time Object Detection](https://arxiv.org/abs/2304.08069) | `transformers` |
     | RT-DETRv2 | [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformers](https://arxiv.org/abs/2407.17140) | `transformers` |
     | RF-DETR | [RF-DETR: Neural Architecture Search for Real-Time Detection Transformers](https://arxiv.org/abs/2511.09554) | `transformers` |