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* cleanup * updates * more refactoring * make style * update inits * support other inputs in base * update based on review Co-authored-by: Nicolas Patry <patry.nicolas@gmail.com> * Update tests/pipelines/test_pipelines_automatic_mask_generation.py Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com> * update * fixup * TODO x and y to refactor, _h _w refactored here * update docstring * more nits * style on these * more doc fix * rename variables * update * updates * style * update * fix `_mask_to_rle_pytorch` * styling * fix ask to rle, wrong outputs * add device arg * update * more updates, fix tets * udpate * update docstrings * styling * fixup * add notebook on the docs * update orginal sizes * fix docstring * updat condition on point_per-batch * updates tests * fix CI test * extend is required, append does not work! * fixup * fix CI tests * whit pixels left * address doc comments * fix doc * slow pipeline tests * update auto init * add revision * make fixup * update p!ipoeline tag when calling tests * alphabeitcal order in inits * fix copies * last style nits * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * reformat docstring * more reformat * address most of the comments * Update src/transformers/pipelines/mask_generation.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * final refactor * Update src/transformers/models/sam/image_processing_sam.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * fixup and fix slow tests * revert --------- Co-authored-by: Nicolas Patry <patry.nicolas@gmail.com> Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com> Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
100 lines
4.3 KiB
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100 lines
4.3 KiB
Plaintext
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# SAM
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## Overview
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SAM (Segment Anything Model) was proposed in [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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The model can be used to predict segmentation masks of any object of interest given an input image.
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The abstract from the paper is the following:
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*We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at \href{https://segment-anything.com}{https://segment-anything.com} to foster research into foundation models for computer vision.*
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Tips:
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- The model predicts binary masks that states the presence or not of the object of interest given an image.
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- The model predicts much better results if input 2D points and/or input bounding boxes are provided
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- You can prompt multiple points for the same image, and predict a single mask.
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- Fine-tuning the model is not supported yet
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- According to the paper, textual input should be also supported. However, at this time of writing this seems to be not supported according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844).
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This model was contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).
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The original code can be found [here](https://github.com/facebookresearch/segment-anything).
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Below is an example on how to run mask generation given an image and a 2D point:
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```python
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import torch
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from PIL import Image
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import requests
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from transformers import SamModel, SamProcessor
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
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processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
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img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
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input_points = [[[450, 600]]] # 2D location of a window in the image
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inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
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outputs = model(**inputs)
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masks = processor.image_processor.post_process_masks(
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outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
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)
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scores = outputs.iou_scores
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```
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Resources:
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- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/segment_anything.ipynb) for using the model
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- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/automatic_mask_generation.ipynb) for using automatic mask generation pipeline.
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## SamConfig
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[[autodoc]] SamConfig
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## SamVisionConfig
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[[autodoc]] SamVisionConfig
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## SamMaskDecoderConfig
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[[autodoc]] SamMaskDecoderConfig
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## SamPromptEncoderConfig
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[[autodoc]] SamPromptEncoderConfig
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## SamProcessor
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[[autodoc]] SamProcessor
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## SamImageProcessor
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[[autodoc]] SamImageProcessor
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## SamModel
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[[autodoc]] SamModel
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- forward
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