Mask R-CNN is a convolution-based neural network for the task of object instance segmentation. The paper describing the model can be found here. NVIDIA's Mask R-CNN is an optimized version of Google's TPU implementation, leveraging mixed precision arithmetic using Tensor Cores while maintaining target accuracy.

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 2.2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

This repository also contains scripts to interactively launch training, benchmarking and inference routines in a Docker container.

The major differences between the official implementation of the paper and our version of Mask R-CNN are as follows:

Mixed precision support with TensorFlow AMP
Gradient accumulation to simulate larger batches
Custom fused CUDA kernels for faster computations

There are other publicly NVIDIA available implementations of Mask R-CNN:

Model architecture

Mask R-CNN builds on top of Faster R-CNN adding a mask head for the task of image segmentation.

The architecture consists of the following:

ResNet-50 backbone with Feature Pyramid Network (FPN)
Region proposal network (RPN) head
RoI Align
Bounding and classification box head
Mask head

$\"Architecture\"$

Figure 1. Diagram of Mask R-CNN framework from original paper

Default configuration

The Mask R-CNN configuration and the hyper-parameters for training and testing purposes are in separate files.

The default configuration of this model can be found at mrcnn_tf2/config.py.

The default configuration is as follows:

Feature extractor:
Images resized with aspect ratio maintained and smaller side length between [832,1344]
Ground Truth mask size 112
Backbone network weights are frozen after second epoch
RPN:
Anchor stride set to 16
Anchor sizes set to (32, 64, 128, 256, 512)
Foreground IOU Threshold set to 0.7, Background IOU Threshold set to 0.3
RPN target fraction of positive proposals set to 0.5
Train Pre-NMS Top proposals set to 2000 per FPN layer
Train Post-NMS Top proposals set to 1000
Test Pre-NMS Top proposals set to 1000 per FPN layer
Test Post-NMS Top proposals set to 1000
RPN NMS Threshold set to 0.7
RoI heads:
Foreground threshold set to 0.5
Batch size per image set to 512
A positive fraction of batch set to 0.25

The default hyper-parameters can be found at mrcnn_tf2/arguments.py.\nThese hyperparameters can be overridden through the command-line options, in the launch scripts.

Feature support matrix

The following features are supported by this model:

\n\n\n\n\n\n\n\n

Feature	Mask R-CNN

| Automatic mixed precision (AMP) | Yes |\n| Accelerated Linear Algebra (XLA)| Yes |

Features

Automatic Mixed Precision (AMP)\nThis implementation of Mask-RCNN uses AMP to implement mixed precision training. It allows us to use FP16 training with FP32 master weights by modifying just a few lines of code.

XLA support (experimental)\nXLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage: most internal benchmarks run ~1.1-1.5x faster after XLA is enabled.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training previously required two steps:

Porting the model to use the FP16 data type where appropriate.
Adding loss scaling to preserve small gradient values.

This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full mixed precision methodology in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.

In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.

For information about:

How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.
APEX tools for mixed precision training, see the NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch.

Enabling mixed precision

Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.

To enable mixed precision, you can simply add the values to the environmental variables inside your training script:

Enable TF-AMP graph rewrite:

os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n

Enable Automated Mixed Precision:

os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n

TF32

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.

For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.

TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.

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Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- TensorFlow 21.02 NGC container\n- Supported GPUs:\n- [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n- [NVIDIA Turing architecture](https://www.nvidia.com/en-us/design-visualization/technologies/turing-architecture/)\n- [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:\n- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry)\n- Running [framework name - link to topic]\n \nFor those unable to use the TensorFlow 2 NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the Mask R-CNN model on the COCO 2017 dataset. For the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n\n ```bash\n git clone https://github.com/NVIDIA/DeepLearningExamples.git\n cd DeepLearningExamples/TensorFlow/Segmentation/MaskRCNN\n ```\n\n2. Build the Mask R-CNN TensorFlow NGC container.\n\n ```bash\n nvidia-docker build -t nvidia_mrcnn_tf2 .\n ```\n\n3. Start an interactive session in the NGC container to run training/inference.\n\n Run the following command to launch the Docker container.\n \n ```bash\n docker run --gpus all -it --rm --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 nvidia_mrcnn_tf2\n ```\n \n If you want to reuse the dataset and pretrained ResNet-50 weights between runs, **(recommended)**, use `-v [data directory]:/data -v [weights directory]:/weights` to mount your directories inside the container:\n \n ```bash\n docker run --gpus all -it --rm --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 -v [data directory]:/data -v [weights directory]:/weights nvidia_mrcnn_tf2\n ```\n \n The contents of `/data` and `/weights` will be downloaded in the following steps.\n\n4. Download and preprocess the dataset.\n\n This repository provides scripts to download and extract the [COCO 2017 dataset](http://cocodataset.org/#download). \n If you already have the data, then you do not need to run the following script; instead proceed to the next step. \n Data will be downloaded to the `[data directory]` directory provided in step 3.\n \n ```bash\n cd dataset\n bash download_and_preprocess_coco.sh /data\n ```\n\n5. Download the pre-trained ResNet-50 weights.\n\n This repository also provides scripts to download the pre-trained weights of ResNet-50 backbone. \n The following script will download the pre-trained weights to `/weights`.\n\n ```bash\n python scripts/download_weights.py --save_dir=/weights\n ```\n\n6. Start training.\n \n To run training with a default configuration (on 1/8 GPUs, AMP/FP32), run a `scripts/train.py` script:\n \n ```bash\n python scripts/train.py --gpus {1,8} [--amp] \n ```\n\n The above script trains a model and evaluates the COCO 2017 dataset using the content in the `/data` and `/weights` directories. Refer to the [Advanced](#advanced) section or run `python scripts/train.py --help` for more details.","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Scripts and sample code\n\nDescriptions of the key scripts and folders are provided below.\n\n- `mrcnn_tf2` - Contains source code of this model.\n- `main.py` - This is the entry point that provides advanced configuration of training and evaluation processes.\n- `scripts/` - A folder with utility scripts that simplifies running of this model.\n - `train.py` - Runs training followed by evaluation.\n - `evaluate.py` - Runs evaluation.\n - `inference.py` - Runs inference.\n - `benchmark_training.py` - Script for running train performance benchmarks.\n - `benchmark_inference.py` - Script for running inference performance benchmarks.\n - `download_weights.sh` - Can be used to download pre-trained weights for backbone models.\n- `dataset/` - A folder that contains shell scripts and Python files to download the dataset.\n\n### Parameters\n\nBelow you will find a description of the most important parameters accepted by scripts. See [Command-line options](#command-line-options) for list of all available options.\n\n#### Utility script parameters\n\nAll the scripts in the `scripts/` directory accept the following parameters:\n\n- `--batch_size N`- Size of the training or evaluation batch size (depends on the script).\n- `--amp` - When provided, enables automatic mixed precision.\n- `--no_xla` - When provided, disables XLA (accelerated linear algebra).\n- `--data_dir [path]` - Path to the directory that contains TFRecords of COCO 2017. Defaults to `/data`.\n- `--model_dir [path]` - Output directory for information related to the model that includes model checkpoints. Defaults to `/tmp/model`.\n- `--weights_dir [path]` - Directory containing pre-trained ResNet50 weights. Defaults to `/weights`.\n\nAdditionally, training scripts also accept some specific parameters:\n\n- `train.py`\n - `--gpus N` - Number of GPUs to use during training.\n - `--no_eval` - When provided, disables evaluation after training.\n- `benchmark_training.py`\n - `--gpus N` - Number of GPUs to use during training.\n\nNote: Any additional flags not specified above will be passed to `python main.py`. Refer to `python main.py --help` for a full list of available fags.\n\n#### Main script parameters\n\nFor most use cases, the scripts in `scripts/` should be sufficient, but if you need more control over the model, you can also directly execute `main.py`. \n\nTo get the list of all parameters accepted by `main.py`, run `python main.py --help`.\n\n### Command-line options\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:\n\n```\npython main.py --help\n```\n\nThe following example output is printed when running the model:\n\n```\nusage: main.py MODE [arguments...]\n\nNVIDIA implementation of MastRCNN for TensorFlow 2.x\n\nRuntime:\n MODE One of supported execution modes:\n train - run in training mode\n eval - run evaluation on eval data split\n infer - run inference on eval data split\n --data_dir DIR Input directory containing the dataset (default: /data)\n --model_dir DIR Output directory for information related to the model (default: /results)\n --backbone_checkpoint FILE Pretrained checkpoint for resnet (default: /weights/rn50_tf_amp_ckpt_v20.06.0/nvidia_rn50_tf_amp)\n --eval_file FILE Path to the validation json file (default: /data/annotations/instances_val2017.json)\n --epochs EPOCHS Number of training epochs (default: 12)\n --steps_per_epoch STEPS_PER_EPOCH Number of steps (batches) per epoch. Defaults to dataset size divided by batch size. (default: None)\n --eval_samples N Number of evaluation samples (default: None)\n\nHyperparameters:\n --train_batch_size N Batch size (per GPU) used during training (default: 4)\n --eval_batch_size N Batch size used during evaluation (default: 8)\n --seed SEED Set a constant seed for reproducibility (default: None)\n --l2_weight_decay L2D Weight of l2 regularization (default: 0.0001)\n --init_learning_rate LR Initial learning rate (default: 0.0)\n --learning_rate_values [D [D ...]] Learning rate decay levels that are then scaled by global batch size (default: [0.01, 0.001, 0.0001])\n --learning_rate_boundaries [N [N ...]] Steps (in epochs) at which learning rate changes (default: [0.3, 8.0, 10.0])\n --momentum MOMENTUM Optimizer momentum (default: 0.9)\n --finetune_bn Is batchnorm finetuned training mode (default: False)\n --use_synthetic_data Use synthetic input data, meant for testing only (default: False)\n --xla Enable XLA JIT Compiler (default: False)\n --amp Enable automatic mixed precision (default: False)\n\nLogging:\n --log_file FILE Output file for DLLogger logs (default: mrcnn-dlll.json)\n --log_every N Log performance every N steps (default: 100)\n --log_warmup_steps N Number of steps that will be ignored when collecting perf stats (default: 100)\n --log_graph Print details about TF graph (default: False)\n --log_tensorboard PATH When provided saves tensorboard logs to given dir (default: None)\n\nUtility:\n -h, --help Show this help message and exit\n -v, --verbose Displays debugging logs (default: False)\n --eagerly Runs model in eager mode. Use for debugging only as it reduces performance. (default: False)\n```\n\n### Getting the data\n\nThe Mask R-CNN model was trained on the COCO 2017 dataset. This dataset comes with a training and validation set.\n\nThis repository contains the `./dataset/download_and_preprocess_coco.sh` script which automatically downloads and preprocesses the training and validation sets, saving them to `tfrecord` files.\n\n#### Dataset guidelines\n\nThe `tfrecord` files are fed to the model through `tf.data.TFRecordDataset()` to achieve high performance.\n\nFirst, the images are normalized using predefined, channel-wise values (offset `0.485, 0.456, 0.406`, scale `0.229, 0.224, 0.225`). Then, they are augmented (random vertical flip) and resized/padded. The resizing maintains the original aspect ratio while setting the smaller side length to be between `832` and `1344`.\n\nThe bounding boxes and masks are processed accordingly so that they match the processed images.\n\n#### Multi-dataset\n\nThis implementation is tuned for the COCO 2017 dataset. Using other datasets is possible, but may require changes to the code (data loader) and tuning some hyperparameters (for example, learning rate, number of iterations).\n\nIn the current implementation, the data loader works with TFRecord files. If you would like to change the format of the input data, you should substitute the `Dataset` class which you can find in `mrcnn_tf2/dataset/dataset.py`.\n\n### Training process\n\nTraining is performed using the `scripts/train.py` script which runs `main.py` with the appropriate flags.\n\nThe results are displayed in the console and are saved in `./mrcnn-dll.json` (can be overridden by `--log_file`) in a form of [DLLogger](https://github.com/NVIDIA/dllogger) logs in which you can find:\n\n- Full configuration used during training\n- Losses, learning rate and performance metrics for steps\n- Final losses\n- Average performance metrics\n\nAdditionally, checkpoints will be saved to `/tmp/model` (can be overridden by `--model_dir`).\n\n### Inference process\n\nInference is performed using the `scripts/evaluate.py` script which runs `main.py` with the appropriate flags.\n\nThe results are displayed in the console and are saved in `./mrcnn-dll.json` (can be overridden by `--log_file`) in a form of [DLLogger](https://github.com/NVIDIA/dllogger) logs in which you can find:\n\n- Full configuration used during the evaluation\n- Evaluation metrics\n- Average performance metrics","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA's latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nTo run training benchmarking on a selected number of GPUs with either AMP or TF32/FP32 precision, run the following script:\n\n```bash\npython scripts/benchmark_training.py --gpus {1,8} --batch_size {2,4} [--amp]\n```\n\n#### Inference performance benchmark\n\nTo run inference benchmarking on a single GPU with either AMP or TF32/FP32 precision, run the following script:\n\n```bash\npython scripts/benchmark_inference.py --batch_size {2,4,8} [--amp]\n```\n\n### Results\n\nThe following sections provide details on how we achieved our performance and accuracy in training and inference.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)\n \nOur results were obtained by running the `python scripts/train.py --gpus 8 --batch_size 4 [--amp]` training script in the TensorFlow 2.x 21.02-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs.\n\n| GPUs | Batch size / GPU | Precision | Final AP BBox | Final AP Segm | Time to train [h] | Time to train speedup |\n|:----:|:----------------:|:---------:|:-------------:|:-------------:|:-----------------:|:---------------------:|\n| 8 | 2 | TF32 | 0.3796 | 0.3444 | 4.81 | - |\n| 8 | 2 | AMP | 0.3795 | 0.3443 | 3.77 | 1.27 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `python scripts/train.py --gpus 8 --batch_size 2 [--amp]` training script in the TensorFlow 2.x 21.02-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.\n\n| GPUs | Batch size / GPU | Precision | Final AP BBox | Final AP Segm | Time to train [h] | Time to train speedup |\n|:----:|:----------------:|:---------:|:-------------:|:-------------:|:-----------------:|:---------------------:|\n| 8 | 2 | FP32 | 0.3793 | 0.3442 | 11.37 | - |\n| 8 | 2 | AMP | 0.3792 | 0.3444 | 9.01 | 1.26 |\n\n**Learning curves**\n\nThe following image shows the training loss as a function of iteration for training using DGX A100 (TF32 and TF-AMP) and DGX-1 V100 (FP32 and TF-AMP).\n\n![LearningCurves](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow2/Segmentation/MaskRCNN/documentation/learning_curves.png)\n\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the `python scripts/benchmark_training.py --gpus {1,8} --batch_size {4,8,16} [--amp]` training script in the TensorFlow 2.x 21.02-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers (in images per second) were averaged over 200 steps omitting the first 100 warm-up steps.\n\n| GPUs | Batch size / GPU | Throughput - TF32 [img/s] | Throughput - mixed precision [img/s] | Throughput speedup (TF32 - mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision |\n|:----:|:----------------:|:-------------------------:|:------------------------------------:|:-------------------------------------------:|:-------------------:|:------------------------------:|\n| 1 | 2 | 13.44 | 18.26 | 1.35 | - | - |\n| 1 | 4 | 18.41 | 28.58 | 1.55 | - | - |\n| 8 | 2 | 84.29 | 87.31 | 1.03 | 6.27 | 4.78 |\n| 8 | 4 | 103.80 | 114.45 | 1.10 | 5.63 | 4.04 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `python scripts/benchmark_training.py --gpus {1,8} --batch_size {2,4} [--amp]` training script in the TensorFlow 2.x 21.02-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs. Performance numbers (in images per second) were averaged over 200 steps omitting the first 100 warm-up steps.\n\n| GPUs | Batch size / GPU | Throughput - FP32 [img/s] | Throughput - mixed precision [img/s] | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision |\n|:----:|:----------------:|:-------------------------:|:------------------------------------:|:-------------------------------------------:|:-------------------:|:------------------------------:|\n| 1 | 2 | 7.57 | 14.47 | 1.91 | - | - |\n| 1 | 4 | 8.51 | 19.35 | 2.27 | - | - |\n| 8 | 2 | 44.55 | 53.40 | 1.37 | 5.26 | 3.69 |\n| 8 | 4 | 50.56 | 58.33 | 1.15 | 6.67 | 4.03 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)\n\nOur results were obtained by running the `python scripts/benchmark_inference.py --batch_size {8,16,24} [--amp]` benchmarking script in the TensorFlow 2.x 21.02-py3 NGC container on NVIDIA DGX A100 (1x A100 80GB) GPU.\n\nTF32\n\n| Batch size | Throughput Avg [img/s] | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|:----------:|:----------------------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| 6 | 39.23 | 0.1530\t | 0.1540\t | 0.1542\t | 0.1546 |\n| 12 | 42.55 | 0.2654\t | 0.2840\t | 0.2875\t | 0.2945 |\n| 24 | 47.92 | 0.5007\t | 0.5248\t | 0.5294\t | 0.5384 |\n\nFP16\n\n| Batch size | Throughput Avg [img/s] | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|:----------:|:----------------------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| 6 | 60.79 | 0.0987\t | 0.0988\t | 0.1000\t | 0.1005 |\n| 12 | 76.23 | 0.1574\t | 0.1614\t | 0.1621\t | 0.1636 |\n| 24 | 80.67 | 0.2975\t | 0.3025\t | 0.3035\t | 0.3054 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nOur results were obtained by running the `python scripts/benchmark_inference.py --batch_size {6,12,24} [--amp]` benchmarking script in the TensorFlow 2.x 21.02-py3 NGC container on NVIDIA DGX-1 with (1x V100 16GB) GPU.\n\nFP32\n\n| Batch size | Throughput Avg [img/s] | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|:----------:|:----------------------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| 6 | 18.56 | 0.3234\t | 0.3263\t | 0.3269\t | 0.3280 |\n| 12 | 20.50 | 0.5854 \t| 0.5920\t | 0.5933\t | 0.5958 |\n| 24 | OOM | -\t | -\t | -\t | - |\n\nFP16\n\n| Batch size | Throughput Avg [img/s] | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|:----------:|:----------------------:|:-----------:|:-----------:|:-----------:|:-----------:|\n| 6 | 35.46 | 0.1692\t | 0.1705\t | 0.1707\t | 0.1712 |\n| 12 | 41.44 | 0.2896\t | 0.2937\t | 0.2945\t | 0.2960 |\n| 24 | 42.53 | 0.5643 | 0.5718 | 0.5733 | 0.5761 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).","hasPerformance","policies",[],{"_82":579,"_84":580,"_521":-5,"_528":31,"_531":31,"_547":578,"_534":438},{"_82":577,"_84":577,"_521":-5,"_528":31,"_531":31,"_547":578,"_534":438},{"_82":575,"_84":575,"_521":-5,"_528":31,"_531":31,"_547":576,"_534":438},{"_82":573,"_84":573,"_521":-5,"_528":31,"_531":31,"_547":574,"_534":438},{"_82":571,"_84":571,"_521":-5,"_528":31,"_531":31,"_547":572,"_534":438},{"_82":569,"_84":569,"_521":-5,"_528":31,"_531":31,"_547":570,"_534":438},{"_82":567,"_84":567,"_521":-5,"_528":31,"_531":31,"_547":568,"_534":438},{"_82":565,"_84":565,"_521":-5,"_528":31,"_531":31,"_547":566,"_534":438},{"_82":563,"_84":563,"_521":-5,"_528":31,"_531":31,"_547":564,"_534":438},{"_82":561,"_84":561,"_521":-5,"_528":31,"_531":31,"_547":562,"_534":438},{"_82":22,"_84":22,"_521":-5,"_528":31,"_531":31,"_547":560,"_534":438},[],"20.06.0",[],"20.06.1",[],"20.06.2",[],"20.06.3",[],"21.02.0",[],"21.02.1",[],"21.02.2",[],"21.02.3",[],"21.02.4",[],"-","Latest (21.02.4)","nvidia/mask_r_cnn_for_tensorflow2","key","nvidia/resources/mask_r_cnn_for_tensorflow2","resourceType","resources","resourceId","mask_r_cnn_for_tensorflow2","shortDescription","Mask R-CNN is a convolution based network for object instance segmentation.","Mask R-CNN is a convolution-based neural network for the task of object instance segmentation. The paper describing the model can be found [here](https://arxiv.org/abs/1703.06870). NVIDIA's Mask R-CNN is an optimized version of [Google's TPU implementation](https://github.com/tensorflow/tpu/tree/master/models/official/mask_rcnn), leveraging mixed precision arithmetic using Tensor Cores while maintaining target accuracy. \n\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 2.2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\nThis repository also contains scripts to interactively launch training, benchmarking and inference routines in a Docker container.\n\nThe major differences between the official implementation of the paper and our version of Mask R-CNN are as follows:\n\n- Mixed precision support with [TensorFlow AMP](https://docs.nvidia.com/deeplearning/frameworks/tensorflow-user-guide/index.html#tfamp)\n- Gradient accumulation to simulate larger batches\n- Custom fused CUDA kernels for faster computations\n\nThere are other publicly NVIDIA available implementations of Mask R-CNN:\n\n- [NVIDIA PyTorch implementation](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/MaskRCNN)\n- [Matterport](https://github.com/matterport/Mask_RCNN)\n- [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN)\n\n### Model architecture\n\nMask R-CNN builds on top of Faster R-CNN adding a mask head for the task of image segmentation.\n\nThe architecture consists of the following:\n\n- ResNet-50 backbone with Feature Pyramid Network (FPN)\n- Region proposal network (RPN) head\n- RoI Align\n- Bounding and classification box head\n- Mask head\n\n![Architecture](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow2/Segmentation/MaskRCNN/documentation/architecture.png)\n\n\nFigure 1. Diagram of Mask R-CNN framework from [original paper](https://arxiv.org/abs/1703.06870)\n\n### Default configuration\n\nThe Mask R-CNN configuration and the hyper-parameters for training and testing purposes are in separate files.\n\nThe default configuration of this model can be found at `mrcnn_tf2/config.py`. \n\nThe default configuration is as follows:\n\n - Feature extractor:\n - Images resized with aspect ratio maintained and smaller side length between [832,1344]\n - Ground Truth mask size 112\n - Backbone network weights are frozen after second epoch\n - RPN:\n - Anchor stride set to 16\n - Anchor sizes set to (32, 64, 128, 256, 512)\n - Foreground IOU Threshold set to 0.7, Background IOU Threshold set to 0.3\n - RPN target fraction of positive proposals set to 0.5\n - Train Pre-NMS Top proposals set to 2000 per FPN layer\n - Train Post-NMS Top proposals set to 1000\n - Test Pre-NMS Top proposals set to 1000 per FPN layer\n - Test Post-NMS Top proposals set to 1000\n - RPN NMS Threshold set to 0.7\n - RoI heads:\n - Foreground threshold set to 0.5\n - Batch size per image set to 512\n - A positive fraction of batch set to 0.25\n\nThe default hyper-parameters can be found at `mrcnn_tf2/arguments.py`. \nThese hyperparameters can be overridden through the command-line options, in the launch scripts.\n\n### Feature support matrix\n\nThe following features are supported by this model:\n\n| **Feature** | **Mask R-CNN** |\n-------------|---------------------|\n\n| Automatic mixed precision (AMP) | Yes |\n| Accelerated Linear Algebra (XLA)| Yes | \n\n#### Features\n\n**Automatic Mixed Precision (AMP)** \nThis implementation of Mask-RCNN uses AMP to implement mixed precision training. It allows us to use FP16 training with FP32 master weights by modifying just a few lines of code.\n\n**XLA support (experimental)** \nXLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage: most internal benchmarks run ~1.1-1.5x faster after XLA is enabled.\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).\n\n#### Enabling mixed precision\n\nMixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.\n\nTo enable mixed precision, you can simply add the values to the environmental variables inside your training script:\n- Enable TF-AMP graph rewrite:\n ```\n os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n ```\n \n- Enable Automated Mixed Precision:\n ```\n os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n ```\n\n#### TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. \n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.","displayName","Mask R-CNN for TensorFlow2",["D",1680632693201],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/mask_r_cnn_for_tensorflow2","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",588967,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[625,626],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_591":636,"_628":637,"_630":638,"_632":633},{"_591":627,"_628":629,"_630":631,"_632":633},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_634":635,"_628":629},"category","label","field","platform","Computer Vision","soln_computer_vision",{"_634":639,"_628":637},"technology",{"_459":1773,"_461":1774},"ngcApps",[1726],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1707":1708,"_1709":1710,"_1711":1712,"_1713":1714,"_1715":651,"_491":677,"_1716":1717,"_1718":56,"_1719":438,"_1720":438,"_1721":1722,"_1723":438,"_1724":1725},"amplitude",{"_1704":1705,"_1313":-7,"_1706":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1698":1699,"_1700":1701,"_1702":1703},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1791207888503,"1.19.0","defaultSearchSuggestions",{"_1583":758,"_1584":1585,"_1586":1587,"_1588":1589},"announcements",[],"consentState",{"_714":1573,"_1574":31,"_1575":31,"_1576":438,"_1577":1578},"consentConfig",{"_696":697,"_698":699,"_700":701,"_702":703,"_704":705,"_706":707,"_708":709,"_710":711,"_712":713,"_714":715},"showContactAdmin",[],[],[690,691,692,693,694,695],{"_45":59,"_47":70,"_49":454,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":454,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":454,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":454,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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Name	Size	Updated	Actions


.dockerignore	389 B	March 24, 2020 UTC	curl wget Download
Dockerfile	2.04 KB	March 24, 2020 UTC	curl wget Download
download_and_process_pretrained_weights.sh	2.87 KB	March 24, 2020 UTC	curl wget Download
LICENSE	11.08 KB	March 24, 2020 UTC	curl wget Download
README.md	27.96 KB	March 24, 2020 UTC	curl wget Download