Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+inference: false
+---
+
+# SegGPT model
+
+The SegGPT model was proposed in [SegGPT: Segmenting Everything In Context](https://arxiv.org/abs/2304.03284) by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang.
+
+## Model description
+
+SegGPT employs a decoder-only (GPT-like) Transformer that can generate a segmentation mask given an input image, a prompt image and its corresponding prompt mask.
+The model achieves remarkable one-shot results with 56.1 mIoU on COCO-20 and 85.6 mIoU on FSS-1000.
+
+## Intended uses & limitations
+
+You can use the raw model for one-shot image segmentation.
+
+### How to use
+
+Here's how to use the model for one-shot semantic segmentation:
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import SegGptImageProcessor, SegGptForImageSegmentation
+
+model_id = "EduardoPacheco/seggpt-vit-large"
+image_processor = SegGptImageProcessor.from_pretrained(checkpoint)
+model = SegGptForImageSegmentation.from_pretrained(checkpoint)
+
+dataset_id = "EduardoPacheco/FoodSeg103"
+ds = load_dataset(dataset_id, split="train")
+# Number of labels in FoodSeg103 (not including background)
+num_labels = 103
+
+image_input = ds[4]["image"]
+ground_truth = ds[4]["label"]
+image_prompt = ds[29]["image"]
+mask_prompt = ds[29]["label"]
+
+inputs = image_processor(
+    images=image_input, 
+    prompt_images=image_prompt,
+    prompt_masks=mask_prompt, 
+    num_labels=num_labels,
+    return_tensors="pt"
+)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+target_sizes = [image_input.size[::-1]]
+mask = image_processor.post_process_semantic_segmentation(outputs, target_sizes, num_labels=num_labels)[0]
+```
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{wang2023seggpt,
+      title={SegGPT: Segmenting Everything In Context}, 
+      author={Xinlong Wang and Xiaosong Zhang and Yue Cao and Wen Wang and Chunhua Shen and Tiejun Huang},
+      year={2023},
+      eprint={2304.03284},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```