omlab
/

omdet-turbo-swin-tiny-hf

@@ -1,199 +1,124 @@
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 ---
+license: apache-2.0
+pipeline_tag: zero-shot-object-detection
 ---
+# OmDet model
+The OmDet model was proposed in [Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head](https://arxiv.org/abs/2403.06892) by Tiancheng Zhao, Peng Liu, Xuan He, Lu Zhang, Kyusong Lee.
+# Intended use cases
+This model is intended for zero-shot (also called open-vocabulary) object detection.
+# Usage
+## Single image inference
+Here's how to load the model and prepare the inputs to perform zero-shot object detection on a single image:
+```python
+import requests
+from PIL import Image
+from transformers import AutoProcessor, OmDetTurboForObjectDetection
+processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-tiny")
+model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-tiny")
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+classes = ["cat", "remote"]
+inputs = processor(image, text=classes, return_tensors="pt")
+outputs = model(**inputs)
+# convert outputs (bounding boxes and class logits)
+results = processor.post_process_grounded_object_detection(
+    outputs,
+    classes=classes,
+    target_sizes=[image.size[::-1]],
+    score_threshold=0.3,
+    nms_threshold=0.3,
+)[0]
+for score, class_name, box in zip(
+    results["scores"], results["classes"], results["boxes"]
+):
+    box = [round(i, 1) for i in box.tolist()]
+    print(
+        f"Detected {class_name} with confidence "
+        f"{round(score.item(), 2)} at location {box}"
+    )
+```
+## Batched images inference
+OmDet-Turbo can perform batched multi-image inference, with support for different text prompts and classes in the same batch:
+```python
+>>> import torch
+>>> import requests
+>>> from io import BytesIO
+>>> from PIL import Image
+>>> from transformers import AutoProcessor, OmDetTurboForObjectDetection
+>>> processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
+>>> model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
+>>> url1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
+>>> image1 = Image.open(BytesIO(requests.get(url1).content)).convert("RGB")
+>>> classes1 = ["cat", "remote"]
+>>> task1 = "Detect {}.".format(", ".join(classes1))
+>>> url2 = "http://images.cocodataset.org/train2017/000000257813.jpg"
+>>> image2 = Image.open(BytesIO(requests.get(url2).content)).convert("RGB")
+>>> classes2 = ["boat"]
+>>> task2 = "Detect everything that looks like a boat."
+>>> url3 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+>>> image3 = Image.open(BytesIO(requests.get(url3).content)).convert("RGB")
+>>> classes3 = ["statue", "trees"]
+>>> task3 = "Focus on the foreground, detect statue and trees."
+>>> inputs = processor(
+...     images=[image1, image2, image3],
+...     text=[classes1, classes2, classes3],
+...     task=[task1, task2, task3],
+...     return_tensors="pt",
+... )
+>>> with torch.no_grad():
+...     outputs = model(**inputs)
+>>> # convert outputs (bounding boxes and class logits)
+>>> results = processor.post_process_grounded_object_detection(
+...     outputs,
+...     classes=[classes1, classes2, classes3],
+...     target_sizes=[image1.size[::-1], image2.size[::-1], image3.size[::-1]],
+...     score_threshold=0.2,
+...     nms_threshold=0.3,
+... )
+>>> for i, result in enumerate(results):
+...     for score, class_name, box in zip(
+...         result["scores"], result["classes"], result["boxes"]
+...     ):
+...         box = [round(i, 1) for i in box.tolist()]
+...         print(
+...             f"Detected {class_name} with confidence "
+...             f"{round(score.item(), 2)} at location {box} in image {i}"
+...         )
+Detected remote with confidence 0.77 at location [39.9, 70.4, 176.7, 118.0] in image 0
+Detected cat with confidence 0.72 at location [11.6, 54.2, 314.8, 474.0] in image 0
+Detected remote with confidence 0.56 at location [333.4, 75.8, 370.7, 187.0] in image 0
+Detected cat with confidence 0.55 at location [345.2, 24.0, 639.8, 371.7] in image 0
+Detected boat with confidence 0.32 at location [146.9, 219.8, 209.6, 250.7] in image 1
+Detected boat with confidence 0.3 at location [319.1, 223.2, 403.2, 238.4] in image 1
+Detected boat with confidence 0.27 at location [37.7, 220.3, 84.0, 235.9] in image 1
+Detected boat with confidence 0.22 at location [407.9, 207.0, 441.7, 220.2] in image 1
+Detected statue with confidence 0.73 at location [544.7, 210.2, 651.9, 502.8] in image 2
+Detected trees with confidence 0.25 at location [3.9, 584.3, 391.4, 785.6] in image 2
+Detected trees with confidence 0.25 at location [1.4, 621.2, 118.2, 787.8] in image 2
+Detected statue with confidence 0.2 at location [428.1, 205.5, 767.3, 759.5] in image 2
+```