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README.md

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+---
+tags:
+  - robotics
+  - anima
+  - monad
+  - segmentation
+  - tracking
+  - video-object-segmentation
+  - sam
+  - xmem
+  - robot-flow-labs
+library_name: pytorch
+pipeline_tag: image-segmentation
+license: apache-2.0
+datasets:
+  - n/a
+language:
+  - en
+---
+
+# MONAD - Interactive Segmentation & Tracking for Robotics
+
+Part of the [ANIMA Perception Suite](https://github.com/RobotFlow-Labs) by **Robot Flow Labs** / AIFLOW LABS LIMITED.
+
+## Overview
+
+MONAD is an interactive video object segmentation and tracking module designed for robotics applications. It combines **SAM (Segment Anything Model)** for initial mask generation with **XMem** for long-term video object segmentation, providing a unified pipeline for real-time object tracking from a single point or box prompt.
+
+## Paper
+
+**IST-ROS: A flexible object segmentation and tracking framework for robotics applications**
+Published in *SoftwareX*, 2025.
+
+The paper presents a ROS-integrated framework for interactive segmentation and tracking, enabling robots to segment and track arbitrary objects in real-time from minimal user input.
+
+## Architecture
+
+MONAD implements a two-stage inference pipeline:
+
+1. **SAM (Segment Anything Model)** - Generates initial segmentation mask from point/box prompt
+   - `vit_h` variant (2.4 GB, higher quality) or `vit_b` variant (358 MB, faster)
+   - Produces binary mask from a single click or bounding box
+
+2. **XMem (Video Object Segmentation)** - Propagates mask across video frames
+   - Long-term memory with sensory, working, and long-term stores
+   - Handles occlusion, appearance changes, and fast motion
+   - Configurable memory management for edge deployment
+
+## Exported Formats
+
+| Format | File | Size | Use Case |
+|--------|------|------|----------|
+| SafeTensors | `pytorch/sam_vit_h_v1.safetensors` | 2564 MB | SAM vit_h - safe, fast loading |
+| SafeTensors | `pytorch/sam_vit_b_v1.safetensors` | 375 MB | SAM vit_b - edge deployment |
+| SafeTensors | `pytorch/xmem_v1.safetensors` | 249 MB | XMem tracker - safe format |
+| TorchScript | `pytorch/sam_vit_h_encoder.pt` | 2431 MB | SAM vit_h encoder - JIT compiled |
+| TorchScript | `pytorch/sam_vit_b_encoder.pt` | 342 MB | SAM vit_b encoder - JIT compiled |
+| Frozen Bundle | `pytorch/xmem_frozen.pt` | 238 MB | XMem - frozen inference bundle |
+
+## GPU Benchmark Results (NVIDIA L4, 22GB)
+
+### Segmentation (Single Frame)
+
+| SAM Model | Resolution | Mean Latency | Throughput | Peak VRAM |
+|-----------|-----------|-------------|------------|-----------|
+| vit_h | 640x480 | 1125.6 ms | 0.9 fps | 5977 MB |
+| vit_b | 640x480 | 324.1 ms | 3.1 fps | 3008 MB |
+| vit_b | 1280x720 | 353.8 ms | 2.8 fps | 3007 MB |
+
+### Tracking (24 Frames, SAM + XMem)
+
+| SAM Model | Resolution | Mean Latency | Throughput | Peak VRAM |
+|-----------|-----------|-------------|------------|-----------|
+| vit_h | 640x480 | 87.2 ms/f | 1.9 fps | 18.4 GB |
+| vit_b | 640x480 | 89.4 ms/f | 4.8 fps | 16.3 GB |
+| vit_b | 320x240 | 67.6 ms/f | 5.3 fps | 4.5 GB |
+
+> vit_b at 640x480 is the recommended configuration for NVIDIA L4 GPUs.
+> 1280x720 causes OOM on 22GB GPUs. Use 24GB+ for HD tracking.
+
+## Quick Start
+
+```python
+import torch
+from safetensors.torch import load_file
+
+# Load SAM vit_b weights (recommended for edge)
+sam_state = load_file("pytorch/sam_vit_b_v1.safetensors")
+
+# Load XMem weights
+xmem_state = load_file("pytorch/xmem_v1.safetensors")
+```
+
+### Full Pipeline Usage
+
+```bash
+# Install MONAD
+git clone https://github.com/RobotFlow-Labs/project_monad
+cd project_monad
+uv sync --extra gpu
+
+# Download required base weights separately:
+# - SAM vit_h: https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
+# - SAM vit_b: https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
+# - XMem: https://github.com/hkchengrex/XMem/releases
+
+# Run inference
+CUDA_VISIBLE_DEVICES=0 uv run python scripts/run_baseline_inference.py \
+  --config configs/gpu_server.toml \
+  --input-path your_video.mp4 \
+  --output-dir output/ \
+  --max-frames 24 --point 320 240
+
+# Start API server
+uv run python -m monad.server
+```
+
+## Hardware Requirements
+
+| Deployment | Min VRAM | Recommended | Max Frames |
+|-----------|----------|-------------|------------|
+| Edge (vit_b) | 6 GB | 8 GB | ~15 frames |
+| Standard (vit_b) | 16 GB | 24 GB | ~30 frames |
+| Full (vit_h) | 18 GB | 24 GB | ~30 frames |
+| Extended | 48 GB+ | 80 GB | 100+ frames |
+
+CPU fallback is functional but ~5x slower than GPU inference.
+
+## Repository Contents
+
+```
+pytorch/                          - Model weights (SafeTensors + TorchScript)
+configs/                          - Training & inference configurations
+  paper_mvp.toml                  - Paper-faithful MVP config
+  gpu_server.toml                 - GPU server optimized config
+logs/                             - Validation results & benchmarks
+  gpu_validation_results.json     - Full GPU benchmark data
+inference_runs/                   - Sample inference outputs
+  crossing_objects/               - Demo masks + overlay video
+frozen_models_manifest.json       - Export manifest with checksums
+```
+
+## Dual Backend Support
+
+MONAD supports both CUDA (GPU server / Jetson) and MLX (Apple Silicon) backends:
+- **CUDA**: bf16 mixed precision, TorchScript exports
+- **MLX**: fp32, native Apple GPU acceleration
+- **CPU**: Fallback for any platform
+
+Backend is auto-detected at runtime: CUDA > MLX > CPU.
+
+## Validation
+
+- **71/74 tests pass** on CUDA (3 failures are macOS-only MLX tests)
+- **82% code coverage**
+- **67 tests pass** on macOS with MLX backend
+- Inference validated on NVIDIA L4 (22GB) with 3 demo videos
+
+## Citation
+
+```bibtex
+@article{monad2025,
+  title={IST-ROS: A flexible object segmentation and tracking framework for robotics applications},
+  journal={SoftwareX},
+  year={2025},
+  publisher={Elsevier}
+}
+```
+
+## License
+
+Apache 2.0 - Robot Flow Labs / AIFLOW LABS LIMITED

configs/gpu_server.toml

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+# MONAD GPU Server Configuration
+# 8x NVIDIA L4 (23GB each) — inference validation
+
+[model]
+backend = "sam_xmem"
+device = "cuda"
+cache_dir = "/mnt/forge-data/.hf_cache"
+
+[segmentation]
+mask_confidence_threshold = 0.90
+binary_threshold = 0.50
+quality_threshold = 0.85
+stability_threshold = 0.80
+default_point_radius = 24
+return_single_mask = true
+
+[sam]
+model_type = "vit_h"
+checkpoint = "/mnt/forge-data/models/sam_vit_h_4b8939.pth"
+
+[xmem]
+checkpoint = "/mnt/forge-data/models/XMem.pth"
+buffer_size = 50
+num_objects = 1
+max_mid_term_frames = 10
+min_mid_term_frames = 5
+max_long_term_elements = 100000
+num_prototypes = 128
+top_k = 30
+mem_every = 10
+deep_update_every = -1
+enable_long_term = true
+enable_long_term_count_usage = true
+size = -1
+key_dim = 64
+value_dim = 512
+hidden_dim = 64
+single_object = false
+
+[mlx]
+enabled = false
+mode = "segment_anything"
+examples_dir = ".cache/monad/mlx-examples"
+sam_model = "vit_b"
+checkpoint = ""
+max_image_side = 1024
+prefer_unified_memory = false
+
+[tracking]
+tracker_type = "single_object"
+max_tracked_objects = 8
+tracking_memory_frames = 30
+confidence_decay = 0.98
+
+[inference]
+batch_size = 1
+max_batch_size = 4
+timeout_segmentation = 30.0
+timeout_tracking = 60.0
+
+[logging]
+level = "INFO"
+format = "json"
+structured = true
+
+[metrics]
+enable_metrics = true
+metrics_port = 9090
+track_inference_time = true
+track_memory_usage = true
+
+[api]
+host = "0.0.0.0"
+port = 8000
+workers = 1
+reload = false
+cors_origins = ["http://localhost:8000"]
+cors_allow_credentials = false
+cors_allow_methods = ["GET", "POST"]
+cors_allow_headers = ["Content-Type"]
+
+[grpc]
+enabled = false
+port = 50051
+
+[storage]
+models_cache_dir = "/mnt/forge-data/.hf_cache"
+temp_dir = "/tmp/monad"
+artifacts_dir = "/mnt/artifacts-datai/models/project_monad"
+max_cache_size_gb = 50
+run_retention_max_runs = 25
+run_retention_max_age_days = 30
+
+[features]
+enable_rest_api = true
+enable_grpc = false
+enable_websocket = false
+enable_batch_processing = true
+enable_stream_processing = false

configs/paper_mvp.toml

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+[model]
+backend = "sam_xmem"
+device = "auto"
+cache_dir = ".cache/monad"
+
+[segmentation]
+mask_confidence_threshold = 0.90
+binary_threshold = 0.50
+quality_threshold = 0.85
+stability_threshold = 0.80
+default_point_radius = 24
+return_single_mask = true
+
+[sam]
+model_type = "vit_h"
+checkpoint = "data/weights/sam_vit_h_4b8939.pth"
+
+[xmem]
+checkpoint = "data/weights/XMem.pth"
+buffer_size = 50
+num_objects = 1
+max_mid_term_frames = 10
+min_mid_term_frames = 5
+max_long_term_elements = 100000
+num_prototypes = 128
+top_k = 30
+mem_every = 10
+deep_update_every = -1
+enable_long_term = true
+enable_long_term_count_usage = true
+size = -1
+key_dim = 64
+value_dim = 512
+hidden_dim = 64
+single_object = false
+
+[mlx]
+enabled = false
+mode = "segment_anything"
+examples_dir = ".cache/monad/mlx-examples"
+sam_model = "vit_b"
+checkpoint = "data/weights/sam_vit_b_01ec64.pth"
+max_image_side = 1024
+prefer_unified_memory = true
+
+[tracking]
+tracker_type = "single_object"
+max_tracked_objects = 8
+tracking_memory_frames = 30
+confidence_decay = 0.98
+
+[inference]
+batch_size = 1
+max_batch_size = 4
+timeout_segmentation = 30.0
+timeout_tracking = 60.0
+
+[logging]
+level = "INFO"
+format = "json"
+structured = true
+
+[metrics]
+enable_metrics = true
+metrics_port = 9090
+track_inference_time = true
+track_memory_usage = true
+
+[api]
+host = "0.0.0.0"
+port = 8000
+workers = 1
+reload = false
+cors_origins = ["http://localhost:3000", "http://localhost:8000"]
+cors_allow_credentials = false
+cors_allow_methods = ["GET", "POST", "DELETE", "OPTIONS"]
+cors_allow_headers = ["Content-Type", "Authorization"]
+
+[grpc]
+enabled = false
+port = 50051
+
+[storage]
+models_cache_dir = ".cache/monad/models"
+temp_dir = "/tmp/monad"
+artifacts_dir = "artifacts"
+max_cache_size_gb = 50
+run_retention_max_runs = 25
+run_retention_max_age_days = 30
+
+[features]
+enable_rest_api = true
+enable_grpc = false
+enable_websocket = false
+enable_batch_processing = true
+enable_stream_processing = false

frozen_models_manifest.json

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+{
+  "timestamp": "2026-03-21T09:07:38",
+  "gpu": "NVIDIA L4",
+  "torch_version": "2.6.0+cu124",
+  "cuda_version": "12.4",
+  "exports": {
+    "sam_vit_h_encoder": {
+      "path": "/mnt/artifacts-datai/exports/project_monad/sam_vit_h_encoder.pt",
+      "size_mb": 2430.8,
+      "format": "torchscript",
+      "status": "ok"
+    },
+    "sam_vit_h_state_dict": {
+      "path": "/mnt/artifacts-datai/exports/project_monad/sam_vit_h_state_dict.pt",
+      "size_mb": 2445.8,
+      "format": "state_dict",
+      "status": "ok"
+    },
+    "sam_vit_b_encoder": {
+      "path": "/mnt/artifacts-datai/exports/project_monad/sam_vit_b_encoder.pt",
+      "size_mb": 342.3,
+      "format": "torchscript",
+      "status": "ok"
+    },
+    "sam_vit_b_state_dict": {
+      "path": "/mnt/artifacts-datai/exports/project_monad/sam_vit_b_state_dict.pt",
+      "size_mb": 357.7,
+      "format": "state_dict",
+      "status": "ok"
+    },
+    "xmem_state_dict": {
+      "path": "/mnt/artifacts-datai/exports/project_monad/xmem_state_dict.pt",
+      "size_mb": 237.5,
+      "format": "state_dict",
+      "status": "ok"
+    },
+    "xmem_frozen": {
+      "path": "/mnt/artifacts-datai/exports/project_monad/xmem_frozen.pt",
+      "size_mb": 237.5,
+      "format": "frozen_bundle",
+      "status": "ok"
+    }
+  }
+}

inference_runs/crossing_objects/metrics.json

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+{
+  "backend": "sam_xmem",
+  "requested_device": "cuda",
+  "resolved_device": "cuda",
+  "backend_ready": true,
+  "backend_notes": [
+    "Research backend available"
+  ],
+  "frames_processed": 24,
+  "latency_ms": {
+    "mean": 84.62345065214797,
+    "max": 125.68026799999643
+  },
+  "tracker_id": "trk_b03b9f3248",
+  "object_id": "obj_ac9a2a710a",
+  "frame_limit": 24
+}

logs/gpu_validation_results.json

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+{
+  "timestamp": "2026-03-21T08:44:14",
+  "cuda_devices": 2,
+  "gpu_names": [
+    "NVIDIA L4",
+    "NVIDIA L4"
+  ],
+  "model_loading": {
+    "vram_before": {
+      "gpu_0": {
+        "allocated_mb": 0.0,
+        "reserved_mb": 0.0,
+        "total_mb": 22563.1,
+        "utilization_pct": 0.0
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+      "gpu_1": {
+        "allocated_mb": 0.0,
+        "reserved_mb": 0.0,
+        "total_mb": 22563.1,
+        "utilization_pct": 0.0
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+    "vram_after": {
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+        "allocated_mb": 2695.1,
+        "reserved_mb": 2732.0,
+        "total_mb": 22563.1,
+        "utilization_pct": 11.9
+      },
+      "gpu_1": {
+        "allocated_mb": 0.0,
+        "reserved_mb": 0.0,
+        "total_mb": 22563.1,
+        "utilization_pct": 0.0
+      }
+    },
+    "load_time_s": 12.648,
+    "total_init_time_s": 12.755,
+    "backend": "sam_xmem",
+    "device_requested": "cuda",
+    "device_resolved": "cuda",
+    "sam_model_type": "vit_h",
+    "sam_checkpoint": "/mnt/forge-data/models/sam_vit_h_4b8939.pth",
+    "xmem_checkpoint": "/mnt/forge-data/models/XMem.pth"
+  },
+  "inference_benchmarks": [
+    {
+      "video": "crossing_objects.mp4",
+      "resolution": "640x480",
+      "video_fps": 24.0,
+      "total_video_frames": 36,
+      "frames_processed": 24,
+      "max_frames_limit": 24,
+      "latency_mean_ms": 84.62,
+      "latency_max_ms": 125.68,
+      "total_time_s": 3.479,
+      "peak_vram_mb": 18250.4,
+      "throughput_fps": 6.9,
+      "backend": "sam_xmem",
+      "device": "cuda",
+      "tracker_id": "trk_b03b9f3248",
+      "object_id": "obj_ac9a2a710a"
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+    {
+      "video": "moving_circle.mp4",
+      "resolution": "640x480",
+      "video_fps": 24.0,
+      "total_video_frames": 48,
+      "frames_processed": 24,
+      "max_frames_limit": 24,
+      "latency_mean_ms": 82.75,
+      "latency_max_ms": 103.4,
+      "total_time_s": 3.31,
+      "peak_vram_mb": 18250.4,
+      "throughput_fps": 7.25,
+      "backend": "sam_xmem",
+      "device": "cuda",
+      "tracker_id": "trk_6c1a8b0c67",
+      "object_id": "obj_1bb04c1f69"
+    },
+    {
+      "video": "static_scene.mp4",
+      "resolution": "640x480",
+      "video_fps": 24.0,
+      "total_video_frames": 12,
+      "frames_processed": 12,
+      "max_frames_limit": 24,
+      "latency_mean_ms": 73.22,
+      "latency_max_ms": 94.14,
+      "total_time_s": 1.994,
+      "peak_vram_mb": 10417.9,
+      "throughput_fps": 6.02,
+      "backend": "sam_xmem",
+      "device": "cuda",
+      "tracker_id": "trk_fb592fd895",
+      "object_id": "obj_527b82ec72"
+    },
+    {
+      "video": "synthetic_stress_test.mp4",
+      "error": "OOM at 48 frames: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 22.03 GiB of which 5.",
+      "note": "SAM vit_h + XMem exceeds 22GB L4 at this frame count"
+    }
+  ],
+  "final_vram": {
+    "gpu_0": {
+      "allocated_mb": 22248.9,
+      "reserved_mb": 22318.0,
+      "total_mb": 22563.1,
+      "utilization_pct": 98.6
+    },
+    "gpu_1": {
+      "allocated_mb": 0.0,
+      "reserved_mb": 0.0,
+      "total_mb": 22563.1,
+      "utilization_pct": 0.0
+    }
+  }
+}

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