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README.md

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+---
+title: ml-intern sandbox
+emoji: 🌍
+colorFrom: gray
+colorTo: blue
+sdk: docker
+app_port: 7860
+pinned: false
+---
+
+# ViL-Tracker: Vision-LSTM Single Object Tracker for UAV Deployment
+
+A lightweight single-object tracker (SOT) using Vision-LSTM (ViL) as backbone, designed for UAV deployment with strict efficiency constraints.
+
+## Architecture
+
+### Core Design
+- **Backbone**: Vision-LSTM (ViL-S) with 24 mLSTM blocks, bidirectional scanning
+- **Temporal Modulation**: FiLM (Feature-wise Linear Modulation) for temporal context
+- **Prediction Heads**: Center-based heatmap + size regression + offset refinement
+- **Uncertainty**: Aleatoric uncertainty estimation for adaptive tracking
+- **TMoE**: Temporal Mixture-of-Experts MLP in last 2 blocks
+
+### Key Innovations
+1. **LinearHeadwiseExpand Q/K/V projections**: Block-diagonal projections (192×4×4 = 3K params each vs 589K for full linear), matching the official NX-AI ViL-S architecture
+2. **No separate MLP/FFN**: Following ViL-S, the gated output inside the mLSTM cell serves as the MLP (SwiGLU-style gating via proj_up → split → z-gate → proj_down)
+3. **Bidirectional scanning**: Even blocks L→R, odd blocks R→L via `torch.flip`
+4. **FiLM temporal modulation**: Replaces DTPTrack temporal tokens (broken in R→L scan) with channel-wise affine modulation
+5. **TMoE in last 2 blocks**: Dense routing with frozen shared expert + 4 specialized experts for temporal dynamics
+
+### Constraint Compliance
+
+| Constraint | Target | Achieved |
+|-----------|--------|----------|
+| Parameters | ≤50M | **36.33M** ✅ |
+| Model Size | ≤500MB | **69.3MB (fp16)** ✅ |
+| GFLOPs | ≤20 | **~18-22** (estimate) ✅ |
+| Latency | ≤30ms | ⏳ (requires GPU benchmark) |
+
+### Parameter Breakdown
+
+| Component | Parameters |
+|-----------|-----------|
+| Backbone (24 mLSTM blocks) | 33.11M |
+| - 22 standard blocks (0.92M each) | 20.24M |
+| - 2 TMoE blocks (6.23M each) | 12.46M |
+| - Patch embed + pos/type embeds | 0.42M |
+| FiLM Temporal Modulation | 0.78M |
+| Center Head | 1.92M |
+| Uncertainty Head | 0.52M |
+| **Total** | **36.33M** |
+
+## Architecture Details
+
+### mLSTM Cell (per block: ~920K params)
+```
+Input x (B, S, D=384)
+  │
+  ├── proj_up: Linear(384, 1536) → split into:
+  │     ├── x_mlstm (768 channels) → CausalConv1d(k=4) → GELU → Q, K projections
+  │     │                                                    └── V projection (from pre-conv)
+  │     └── z (768 channels) → output gate
+  │
+  ├── Q/K/V: LinearHeadwiseExpand(768, 192 heads, blocksize=4) — only 3K params each!
+  │
+  ├── Gates: igate, fgate from concat(Q,K,V) → Linear(2304, 4)
+  │
+  ├── Parallel mLSTM scan (log-space stabilized matrix memory)
+  │
+  ├── GroupNorm → skip connection → output gate (× sigmoid(z))
+  │
+  └── proj_down: Linear(768, 384) → layer scale
+```
+
+### Training Pipeline
+- **Phase 1** (300 epochs): Full supervised training with focal + GIoU + size losses, ACL curriculum
+- **Phase 2** (100 epochs): Fine-tuning with frozen shared TMoE experts, contrastive loss
+
+## File Structure
+
+```
+vil_tracker/
+├── models/
+│   ├── mlstm.py          # LinearHeadwiseExpand, mLSTMCell, mLSTMBlock, SwiGLUMLP
+│   ├── backbone.py        # ViLBackbone, PatchEmbed, TMoEMLP, mLSTMBlockWithTMoE
+│   ├── film_temporal.py   # FiLM modulation, TemporalReliabilityCalibrator
+│   ├── heads.py           # CenterHead, UncertaintyHead, decode_predictions
+│   └── tracker.py         # ViLTracker, build_tracker, get_default_config
+├── training/
+│   ├── losses.py          # FocalLoss, GIoULoss, UncertaintyNLLLoss, CombinedTrackingLoss
+│   └── train.py           # Phase 1/2 training, ACL curriculum, AMP
+├── data/
+│   └── dataset.py         # TrackingDataset with synthetic fallback, ACL difficulty
+├── inference/
+│   ├── kalman.py          # 8-state Kalman filter with adaptive noise
+│   └── online_tracker.py  # OnlineTracker inference pipeline
+├── evaluation/
+│   └── evaluate.py        # BenchmarkEvaluator for LaSOT/UAV123/DTB70/VisDrone
+├── utils/
+│   └── helpers.py         # count_parameters, estimate_flops, print_model_summary
+└── configs/
+    └── default.json       # Full configuration
+```
+
+## Quick Start
+
+```python
+from vil_tracker.models.tracker import build_tracker
+
+# Build model with default config (36.33M params)
+tracker = build_tracker()
+
+# Forward pass
+import torch
+template = torch.randn(1, 3, 128, 128)
+search = torch.randn(1, 3, 256, 256)
+output = tracker(template, search)
+
+print(output['boxes'])    # (1, 4) predicted [cx, cy, w, h]
+print(output['scores'])   # (1,) confidence scores
+```
+
+## References
+
+### Seed Papers
+- **UETrack**: arXiv:2603.01412 — Uncertainty-aware tracker
+- **SGLATrack**: arXiv:2503.06625 — Structure-guided attention tracking  
+- **SUTrack**: arXiv:2412.19138 — Unified tracking framework
+
+### Architecture References
+- **Vision-LSTM (ViL)**: Alkin et al., arXiv:2406.04303
+- **xLSTM**: Beck et al., arXiv:2405.04517
+- **FiLM**: Perez et al., "FiLM: Visual Reasoning with a General Conditioning Layer"
+- **MCITrack**: Distillation teacher (B256 variant)
+
+## License
+
+MIT