+Description +
+
+
+| + | name | +backbone | +Data | +box AP on COCO | +Checkpoint | +Config | +
|---|---|---|---|---|---|---|
| 1 | +GroundingDINO-T | +Swin-T | +O365,GoldG,Cap4M | +48.4 (zero-shot) / 57.2 (fine-tune) | +Github link | HF link | +link | +
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+Introduction
+-----------------
+The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have intricate structures. We propose HQ-SAM, equipping SAM with the ability to accurately segment any object, while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability. Our careful design reuses and preserves the pre-trained model weights of SAM, while only introducing minimal additional parameters and computation. We design a learnable High-Quality Output Token, which is injected into SAM's mask decoder and is responsible for predicting the high-quality mask. Instead of only applying it on mask-decoder features, we first fuse them with early and final ViT features for improved mask details. To train our introduced learnable parameters, we compose a dataset of 44K fine-grained masks from several sources. HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs. We show the efficacy of HQ-SAM in a suite of 9 diverse segmentation datasets across different downstream tasks, where 7 out of them are evaluated in a zero-shot transfer protocol.
+
+
+
+
+Quantitative comparison between SAM and HQ-SAM
+-----------------
+Note: For box-prompting-based evaluation, we feed SAM and our HQ-SAM with the same image/video bounding boxes and adopt the single mask output mode of SAM.
+
+### Various ViT backbones on COCO:
+
+Note: For the COCO dataset, we use a SOTA detector FocalNet-DINO trained on the COCO dataset as our box prompt generator.
+
+### YTVIS and HQ-YTVIS
+Note:Using ViT-L backbone. We adopt the SOTA detector Mask2Former trained on the YouTubeVIS 2019 dataset as our video boxes prompt generator while reusing its object association prediction.
+
+
+### DAVIS
+Note: Using ViT-L backbone. We adopt the SOTA model XMem as our video boxes prompt generator while reusing its object association prediction.
+
+
+ ### Interactive segmentation comparison using various points
+Note:Using ViT-L backbone. On the high-quality COIFT (zero-shot) and DIS val set.
+
+
+### **Installation**
+The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
+
+Clone the repository locally and install with
+
+```
+git clone https://github.com/SysCV/sam-hq.git
+cd sam-hq; pip install -e .
+```
+
+The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks.
+
+```
+pip install opencv-python pycocotools matplotlib onnxruntime onnx
+```
+
+### Example conda environment setup
+```bash
+conda create --name sam_hq python=3.8 -y
+conda activate sam_hq
+conda install pytorch==1.10.0 torchvision==0.11.0 cudatoolkit=11.1 -c pytorch -c nvidia
+pip install opencv-python pycocotools matplotlib onnxruntime onnx
+
+# under your working directory
+git clone https://github.com/SysCV/sam-hq.git
+cd sam-hq
+pip install -e .
+export PYTHONPATH=$(pwd)
+```
+
+### **Model Checkpoints**
+
+Three HQ-SAM model versions of the model are available with different backbone sizes. These models can be instantiated by running
+
+```
+from segment_anything import sam_model_registry
+sam = sam_model_registry["