Transformers documentation

DETR

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DETR

Overview

The DETR model was proposed in End-to-End Object Detection with Transformers by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use things like region proposals, non-maximum suppression procedure and anchor generation. Moreover, DETR can also be naturally extended to perform panoptic segmentation, by simply adding a mask head on top of the decoder outputs.

The abstract from the paper is the following:

We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines.

This model was contributed by nielsr. The original code can be found here.

The quickest way to get started with DETR is by checking the example notebooks (which showcase both inference and fine-tuning on custom data).

Here’s a TLDR explaining how DetrForObjectDetection works:

First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use ResNet-50/ResNet-101). Let’s assume we also add a batch dimension. This means that the input to the backbone is a tensor of shape (batch_size, 3, height, width), assuming the image has 3 color channels (RGB). The CNN backbone outputs a new lower-resolution feature map, typically of shape (batch_size, 2048, height/32, width/32). This is then projected to match the hidden dimension of the Transformer of DETR, which is 256 by default, using a nn.Conv2D layer. So now, we have a tensor of shape (batch_size, 256, height/32, width/32). Next, the feature map is flattened and transposed to obtain a tensor of shape (batch_size, seq_len, d_model) = (batch_size, width/32*height/32, 256). So a difference with NLP models is that the sequence length is actually longer than usual, but with a smaller d_model (which in NLP is typically 768 or higher).

Next, this is sent through the encoder, outputting encoder_hidden_states of the same shape (you can consider these as image features). Next, so-called object queries are sent through the decoder. This is a tensor of shape (batch_size, num_queries, d_model), with num_queries typically set to 100 and initialized with zeros. These input embeddings are learnt positional encodings that the authors refer to as object queries, and similarly to the encoder, they are added to the input of each attention layer. Each object query will look for a particular object in the image. The decoder updates these embeddings through multiple self-attention and encoder-decoder attention layers to output decoder_hidden_states of the same shape: (batch_size, num_queries, d_model). Next, two heads are added on top for object detection: a linear layer for classifying each object query into one of the objects or “no object”, and a MLP to predict bounding boxes for each query.

The model is trained using a bipartite matching loss: so what we actually do is compare 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 find an optimal one-to-one mapping of each of the N queries to 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.

DETR can be naturally extended to perform panoptic segmentation (which unifies semantic segmentation and instance segmentation). DetrForSegmentation adds a segmentation mask head on top of DetrForObjectDetection. The mask head can be trained either jointly, or in a two steps process, where one first trains a DetrForObjectDetection model to detect bounding boxes around both “things” (instances) and “stuff” (background things like trees, roads, sky), then freeze all the weights and train only the mask head for 25 epochs. Experimentally, these two approaches give similar results. Note that predicting boxes is required for the training to be possible, since the Hungarian matching is computed using distances between boxes.

Tips:

DETR uses so-called object queries to detect objects in an image. The number of queries determines the maximum number of objects that can be detected in a single image, and is set to 100 by default (see parameter num_queries of DetrConfig). Note that it’s good to have some slack (in COCO, the authors used 100, while the maximum number of objects in a COCO image is ~70).
The decoder of DETR updates the query embeddings in parallel. This is different from language models like GPT-2, which use autoregressive decoding instead of parallel. Hence, no causal attention mask is used.
DETR adds position embeddings to the hidden states at each self-attention and cross-attention layer before projecting to queries and keys. For the position embeddings of the image, one can choose between fixed sinusoidal or learned absolute position embeddings. By default, the parameter position_embedding_type of DetrConfig is set to "sine".
During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help the model output the correct number of objects of each class. If you set the parameter auxiliary_loss of DetrConfig to True, then prediction feedforward neural networks and Hungarian losses are added after each decoder layer (with the FFNs sharing parameters).
If you want to train the model in a distributed environment across multiple nodes, then one should update the num_boxes variable in the DetrLoss class of modeling_detr.py. When training on multiple nodes, this should be set to the average number of target boxes across all nodes, as can be seen in the original implementation here.
DetrForObjectDetection and DetrForSegmentation can be initialized with any convolutional backbone available in the timm library. Initializing with a MobileNet backbone for example can be done by setting the backbone attribute of DetrConfig to "tf_mobilenetv3_small_075", and then initializing the model with that config.
DETR resizes the input images such that the shortest side is at least a certain amount of pixels while the longest is at most 1333 pixels. At training time, scale augmentation is used such that the shortest side is randomly set to at least 480 and at most 800 pixels. At inference time, the shortest side is set to 800. One can use DetrFeatureExtractor to prepare images (and optional annotations in COCO format) for the model. Due to this resizing, images in a batch can have different sizes. DETR solves this by padding images up to the largest size in a batch, and by creating a pixel mask that indicates which pixels are real/which are padding. Alternatively, one can also define a custom collate_fn in order to batch images together, using pad_and_create_pixel_mask().
The size of the images will determine the amount of memory being used, and will thus determine the batch_size. It is advised to use a batch size of 2 per GPU. See this Github thread for more info.

There are three ways to instantiate a DETR model (depending on what you prefer):

Option 1: Instantiate DETR with pre-trained weights for entire model

>>> from transformers import DetrForObjectDetection

>>> model = DetrForObjectDetection.from_pretrained("facebook/resnet-50")

Option 2: Instantiate DETR with randomly initialized weights for Transformer, but pre-trained weights for backbone

>>> from transformers import DetrConfig, DetrForObjectDetection

>>> config = DetrConfig()
>>> model = DetrForObjectDetection(config)

Option 3: Instantiate DETR with randomly initialized weights for backbone + Transformer

>>> config = DetrConfig(use_pretrained_backbone=False)
>>> model = DetrForObjectDetection(config)

As a summary, consider the following table:

Task	Object detection	Instance segmentation	Panoptic segmentation
Description	Predicting bounding boxes and class labels around objects in an image	Predicting masks around objects (i.e. instances) in an image	Predicting masks around both objects (i.e. instances) as well as “stuff” (i.e. background things like trees and roads) in an image
Model	DetrForObjectDetection	DetrForSegmentation	DetrForSegmentation
Example dataset	COCO detection	COCO detection, COCO panoptic	COCO panoptic
Format of annotations to provide to DetrFeatureExtractor	{‘image_id’: `int`, ‘annotations’: `List[Dict]`} each Dict being a COCO object annotation	{‘image_id’: `int`, ‘annotations’: `List[Dict]`} (in case of COCO detection) or {‘file_name’: `str`, ‘image_id’: `int`, ‘segments_info’: `List[Dict]`} (in case of COCO panoptic)	{‘file_name’: `str`, ‘image_id’: `int`, ‘segments_info’: `List[Dict]`} and masks_path (path to directory containing PNG files of the masks)
Postprocessing (i.e. converting the output of the model to COCO API)	`post_process()`	`post_process_segmentation()`	`post_process_segmentation()`, `post_process_panoptic()`
evaluators	`CocoEvaluator` with `iou_types="bbox"`	`CocoEvaluator` with `iou_types="bbox"` or `"segm"`	`CocoEvaluator` with `iou_tupes="bbox"` or `"segm"`, `PanopticEvaluator`

In short, one should prepare the data either in COCO detection or COCO panoptic format, then use DetrFeatureExtractor to create pixel_values, pixel_mask and optional labels, which can then be used to train (or fine-tune) a model. For evaluation, one should first convert the outputs of the model using one of the postprocessing methods of DetrFeatureExtractor. These can be be provided to either CocoEvaluator or PanopticEvaluator, which allow you to calculate metrics like mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the original repository. See the example notebooks for more info regarding evaluation.

Transformers

DETR

Overview

DETR specific outputs

class transformers.models.detr.modeling_detr.DetrModelOutput

class transformers.models.detr.modeling_detr.DetrObjectDetectionOutput

class transformers.models.detr.modeling_detr.DetrSegmentationOutput

DetrConfig

class transformers.DetrConfig

DetrFeatureExtractor

class transformers.DetrFeatureExtractor

__call__

pad_and_create_pixel_mask

post_process_object_detection

post_process_semantic_segmentation

post_process_instance_segmentation

post_process_panoptic_segmentation

DetrModel

class transformers.DetrModel

forward

DetrForObjectDetection

class transformers.DetrForObjectDetection

forward

DetrForSegmentation

class transformers.DetrForSegmentation

forward

call