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# Updates !!! # Updates !!!
** (2022-Aug.-17)** The optimized model for general use of our IS-Net is now released: ```isnet-general-use.pth``` (for general use) from [(Google Drive)](https://drive.google.com/file/d/1nV57qKuy--d5u1yvkng9aXW1KS4sOpOi/view?usp=sharing) or [(Baidu Pan 提取码:6jh2)](https://pan.baidu.com/s/111MqmwnUc8Z4Wsq2Pc4bhQ?pwd=6jh2).
![u2net-isnet-cmp](figures/u2net-isnet-cmp.png)
** (2022-Jul.-30)** Thank [**AK391**](https://github.com/AK391) for the implementaiton of a Web Demo: Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/doevent/dis-background-removal). <br> ** (2022-Jul.-30)** Thank [**AK391**](https://github.com/AK391) for the implementaiton of a Web Demo: Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/doevent/dis-background-removal). <br>
Notes for official DIS group: Currently, the released DIS deep model is the academic version that was trained with DIS V1.0, which includes very few animal, human, cars, etc. So it may not work well on these targets. We will release another version for general use and test. In addition, our DIS V2.0 will cover more categories with extremely well-annotated samples. Please stay tuned. <br> Notes for official DIS group: Currently, the released DIS deep model is the academic version that was trained with DIS V1.0, which includes very few animal, human, cars, etc. So it may not work well on these targets. We will release another version for general use and test. In addition, our DIS V2.0 will cover more categories with extremely well-annotated samples. Please stay tuned. <br>
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### (5) Inference ### (5) Inference
Download the pre-trained weights (for fair academic comparisons) ```isnet.pth``` from [(Google Drive)](https://drive.google.com/file/d/1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn/view?usp=sharing) or [(Baidu Pan 提取码：xbfk)](https://pan.baidu.com/s/1-X2WutiBkWPt-oakuvZ10w?pwd=xbfk) or the optimized model weights ```isnet-general-use.pth``` (for general use) from [(Google Drive)](https://drive.google.com/file/d/1nV57qKuy--d5u1yvkng9aXW1KS4sOpOi/view?usp=sharing) or [(Baidu Pan 提取码:6jh2)](https://pan.baidu.com/s/111MqmwnUc8Z4Wsq2Pc4bhQ?pwd=6jh2), and store them in ```saved_models/IS-Net``` <br> Download the pre-trained weights (for fair academic comparisons) ```isnet.pth``` from [(Google Drive)](https://drive.google.com/file/d/1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn/view?usp=sharing) or [(Baidu Pan 提取码：xbfk)](https://pan.baidu.com/s/1-X2WutiBkWPt-oakuvZ10w?pwd=xbfk) OR the optimized model weights ```isnet-general-use.pth``` (for general use) from [(Google Drive)](https://drive.google.com/file/d/1nV57qKuy--d5u1yvkng9aXW1KS4sOpOi/view?usp=sharing) or [(Baidu Pan 提取码:6jh2)](https://pan.baidu.com/s/111MqmwnUc8Z4Wsq2Pc4bhQ?pwd=6jh2), and store them in ```saved_models/IS-Net``` <br>
## I. Simple inference code for your own dataset without ground truth: ## I. Simple inference code for your own dataset without ground truth:
(a) Open ```\ISNet\inference.py``` and configure your input and output directories (a) Open ```\ISNet\inference.py``` and configure your input and output directories