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LibreEfficientDetd1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5e6b5d5e4ae96771424ded21a33cca5e3666bd97db6c39ec3f072756ad1f8183
+size 27108633

NOTICE

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+LibreEfficientDet third-party attribution
+=========================================
+
+The EfficientDet D0-D4 architecture, TensorFlow-compatible EfficientNet
+backbones, BiFPN topology, prediction heads, anchor generation, checkpoint
+configuration, and evaluation transform are derived from:
+
+    Repository: https://github.com/rwightman/efficientdet-pytorch
+    Commit:     c6dff775a36cea0bf9b76c58e59f936411c5ce01
+    Release:    effdet 0.4.1
+    License:    Apache License 2.0
+    Copyright:  Copyright 2020 Ross Wightman
+
+The native EfficientNet block structure and timm-compatible parameter names
+also follow:
+
+    Repository: https://github.com/huggingface/pytorch-image-models
+    Version:    v1.0.28
+    Commit:     8ef73809f622e0031bd7f4940265734aef8b9978
+    License:    Apache License 2.0
+    Copyright:  Ross Wightman and the timm contributors
+
+The original EfficientDet TensorFlow implementation is from Google Research:
+
+    Repository: https://github.com/google/automl/tree/master/efficientdet
+    BiFPN source commit: 56815c9986ffd4b508fe1d68508e268d129715c1
+    Anchor source commit: 6f6694cec1a48cdb33d5d1551a2d5db8ad227798
+    License: Apache License 2.0
+    Copyright: Copyright 2020 Google Research
+
+Official D0-D4 COCO checkpoints are unmodified learned tensors from the
+``rwightman/efficientdet-pytorch`` v0.1 GitHub release. That Apache-2.0
+project supplies no checkpoint-specific license, so the redistribution basis
+is Apache-2.0 implied by the releasing project rather than a separate explicit
+weight grant. Converted files add LibreYOLO metadata only.
+
+No source from the LGPL-licensed
+``zylo117/Yet-Another-EfficientDet-Pytorch`` project was consulted or used.
+
+The Apache License 2.0 text is distributed at
+``licenses/Apache-2.0.txt``.

README.md

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+---
+license: apache-2.0
+library_name: libreyolo
+pipeline_tag: object-detection
+tags:
+  - object-detection
+  - efficientdet
+  - pytorch
+datasets:
+  - detection-datasets/coco
+---
+
+# LibreEfficientDetd1
+
+EfficientDet-D1 with an EfficientNet-B1 backbone, a weighted BiFPN, 6.63M
+parameters, and a fixed 640x640 input, repackaged for
+[LibreYOLO](https://github.com/LibreYOLO/libreyolo).
+
+```python
+from libreyolo import LibreYOLO
+
+model = LibreYOLO("LibreEfficientDetd1.pt")
+results = model.predict("image.jpg")
+```
+
+LibreYOLO ships this family for detection inference and validation. Training is
+not implemented. ONNX, TorchScript, OpenVINO, and TensorRT exports have
+trained-checkpoint prediction-parity coverage on the D0 family representative.
+
+## Source
+
+Converted from the official
+[`tf_efficientdet_d1_40-a30f94af.pth`](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d1_40-a30f94af.pth)
+release asset from
+[`rwightman/efficientdet-pytorch`](https://github.com/rwightman/efficientdet-pytorch)
+0.4.1 at commit `c6dff775a36cea0bf9b76c58e59f936411c5ce01`.
+Copyright 2020 Ross Wightman. Licensed under the Apache License 2.0.
+
+The native EfficientNet block implementation also follows
+[`huggingface/pytorch-image-models`](https://github.com/huggingface/pytorch-image-models)
+v1.0.28 at commit `8ef73809f622e0031bd7f4940265734aef8b9978`
+(Apache-2.0). The original EfficientDet design is from Google Research's
+Apache-2.0 [`google/automl`](https://github.com/google/automl/tree/master/efficientdet)
+implementation.
+
+Source checkpoint SHA-256:
+`a30f94afc3326a6ef7a61c1657baefe3ea7168139006fbf0fe41807260b885b8`.
+
+## Modifications
+
+Checkpoint metadata wrap only. Learned parameter names, dtypes, and tensor
+values are unchanged. See `weights/convert_efficientdet_weights.py` in the
+[LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo).
+
+Strict loading succeeds with no missing or unexpected keys. Against `effdet`
+0.4.1, identical fixed inputs produce bit-exact FP32 class and box feature
+tensors, anchors, and all 5,000 decoded candidate rows (`max_abs_diff == 0.0`).
+LibreYOLO maps the sparse 90-slot COCO head to a contiguous COCO-80 interface.
+
+## Benchmarks
+
+The pinned upstream model zoo reports **40.1 COCO val2017 box AP** for this
+checkpoint. Independent accuracy and speed results will appear at
+[visionanalysis.org/model/efficientdet-d1](https://www.visionanalysis.org/model/efficientdet-d1)
+after EfficientDet is added to the Vision Analysis model catalog.
+
+## Limitations
+
+- Detection uses the fixed 640x640 evaluation canvas and COCO-80 classes.
+- The focal-loss and anchor-matching training recipe is not included.
+- TensorRT uses 3,840 pre-NMS candidates because TensorRT 10.x limits `TopK`;
+  native, ONNX, TorchScript, and OpenVINO retain the upstream 5,000 candidates.
+
+## License
+
+Apache License 2.0. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE)
+files in this repository. The upstream release does not attach a separate
+checkpoint-specific license object; this mirror relies on Apache-2.0 as implied
+by the releasing project, not a publisher-confirmed asset-specific grant.