diff --git a/README.md b/README.md index bb54a2e..ccfbc0f 100644 --- a/README.md +++ b/README.md @@ -26,6 +26,8 @@ The DETR model is an encoder-decoder transformer with a convolutional backbone. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. +![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/detr_architecture.png) + ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models. @@ -36,21 +38,39 @@ Here is how to use this model: ```python from transformers import DetrFeatureExtractor, DetrForObjectDetection +import torch from PIL import Image import requests -url = 'http://images.cocodataset.org/val2017/000000039769.jpg' +url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) -feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50') -model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50') +feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50") +model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) -# model predicts bounding boxes and corresponding COCO classes -logits = outputs.logits -bboxes = outputs.pred_boxes +# convert outputs (bounding boxes and class logits) to COCO API +target_sizes = torch.tensor([image.size[::-1]]) +results = feature_extractor.post_process(outputs, target_sizes=target_sizes)[0] + +for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): + box = [round(i, 2) for i in box.tolist()] + # let's only keep detections with score > 0.9 + if score > 0.9: + print( + f"Detected {model.config.id2label[label.item()]} with confidence " + f"{round(score.item(), 3)} at location {box}." + ) +``` +This should output: +``` +Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98] +Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66] +Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76] +Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93] +Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72] ``` Currently, both the feature extractor and model support PyTorch.