IMGOVO - NEURAL-NETWORK MODEL FILES (/models/) ============================================================================== Used by the tools Remove Background and Blur Background. Downloaded by the visitor's browser only when one of those tools is used and a quality level is chosen. They run inside the browser; photos are never uploaded. Page with all components: https://imgovo.com/open-source-licenses (list in one file: /open-source-licenses/notices.txt) FILES ----- isnet-general-int8.0.bin 16000000 bytes sha256 da51bceac325630cfe5e1c0910fe0cc883cc7653a55bb5ba57da69719aad5136 isnet-general-int8.1.bin 16000000 bytes sha256 7b38e51217372fb2e86565e97c07eaf7417767d6c2901e821d988d78a7aa465f isnet-general-int8.2.bin 14527293 bytes sha256 fbbcae3d6a1323bc84d95a21fd4cc05414fe146973ce290c58f2098ed48310df The three parts, joined in this order, are ONE ONNX model (46527293 bytes, sha256 928502b41348f24f89b2a0b7dd315c5caa5b24c36a0ca1d3a63d957249e3c7fa). Used for the quality levels "Balanced" and "Maximum". u2netp.onnx 4574861 bytes sha256 309c8469258dda742793dce0ebea8e6dd393174f89934733ecc8b14c76f4ddd8 Used for the quality level "Fast". Byte-identical to the file published by the rembg project. ORIGIN ------ Both files come from the release v0.0.0 of the rembg project (https://github.com/danielgatis/rembg, MIT licence): https://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-general-use.onnx sha256 60920e99c45464f2ba57bee2ad08c919a52bbf852739e96947fbb4358c0d964a (178648008 bytes) Model: IS-Net, "general use" - Xuebin Qin et al., "Highly Accurate Dichotomous Image Segmentation", ECCV 2022 Code repository: https://github.com/xuebinqin/DIS (code and evaluation metric: Apache License 2.0) https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnx sha256 309c8469258dda742793dce0ebea8e6dd393174f89934733ecc8b14c76f4ddd8 (4574861 bytes) Model: U2-Net-P - Xuebin Qin et al., "U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection", Pattern Recognition 2020 Code repository: https://github.com/xuebinqin/U-2-Net (Apache License 2.0) CHANGES MADE BY IMGOVO (notice under section 4(b) of the Apache License 2.0) --------------------------------------------------------------------------- u2netp.onnx: none. isnet-general-int8.*.bin: the file isnet-general-use.onnx was MODIFIED by Imgovo: 1. pruned to its main output (the 11 auxiliary side outputs and the layers only they need were removed); 2. the fixed target sizes of the decoder's Resize operators were replaced by sizes computed from the tensors they are concatenated with, so that the model accepts any input size that is a multiple of 32; 3. the convolution weights were stored as 8-bit integers with one scale per output channel and an explicit DequantizeLinear operator (weight-only quantisation: the network still computes in 32-bit floating point, the weights are expanded when the model is loaded; this reduces the file from 178 MB to 46.5 MB); 4. the result was cut into three parts (Cloudflare Pages accepts files of at most 25 MiB). The scripts that did this are in the Imgovo project: tools/models/dynpatch.py and tools/models/quant.py (commands in tools/models/README.md). Imgovo did not train or fine-tune any model. OPEN LICENSING QUESTION (not resolved; not legal advice) -------------------------------------------------------- The Apache License 2.0 text below is the licence of the authors' CODE. The authors do not state a licence for the released weights. The DIS repository says that its dataset DIS5K is available for non-commercial use in research or education and forbids commercial use of that dataset "even after copying, editing, processing or any operations" (DIS5K-Dataset-Terms-of-Use.pdf in the repository). Which data the "isnet-general-use" weights were trained on is not stated that we could find. The rembg README says that model weights carry their own licences, independent of rembg's MIT licence. Whether these weights may be offered on a website that could carry advertising has not been established; Imgovo has neither asked the authors nor obtained a legal opinion. The same applies, with the dataset DUTS-TR named in the U2-Net paper, to u2netp.onnx. ============================================================================== LICENCE TEXT 1 - Apache License 2.0 (identical in xuebinqin/DIS and xuebinqin/U-2-Net) ============================================================================== Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. 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See the License for the specific language governing permissions and limitations under the License. ============================================================================== LICENCE TEXT 2 - rembg (MIT), the project that publishes the ONNX files ============================================================================== MIT License Copyright (c) 2020 Daniel Gatis Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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