Add FSD50K Part 1 training documentation

model_checkpoints/Primitive_Extractor/FSD50k/training_script_part1.md

@@ -0,0 +1,85 @@
+# FSD50K Primitive Extractor Training - Part 1
+
+## Overview
+This document describes the training of a **Primitive Extractor** on the FSD50K dataset (Part 1 only), using transfer learning from a pretrained Clotho checkpoint.
+
+## Architecture
+The model is a **36.7M parameter** Primitive Extractor with three main components:
+
+### 1. Extractor (Encoder) - From Clotho
+- **Conv Stack**: 3 × (Conv1d → Transpose → LayerNorm → Transpose → GELU)
+  - Conv1d(768 → 1728, k=7)
+  - Conv1d(1728 → 1728, k=5)
+  - Conv1d(1728 → 1728, k=3)
+- **Projection**: Linear(1728 → 512) + LayerNorm
+- **Self-Attention**: MultiheadAttention(512, 8 heads) + residual + LayerNorm
+- **Output**: 512-dimensional primitive vectors (seq_len, 512)
+
+### 2. Decoder - From Clotho
+- Linear(512 → 1024) + GELU
+- Linear(1024 → 768)
+- **Purpose**: Reconstruct the original 768-dim BEATs features for the reconstruction_loss
+
+### 3. Classifier Head - NEW for FSD50K
+- Linear(512 → 512) + GELU + Dropout(0.2)
+- Linear(512 → 200)
+- **Purpose**: Predict the 200 FSD50K sound classes
+
+## Training Configuration
+- **Dataset**: FSD50K Part 1 (7,343 matched samples)
+  - Train: 6,608 samples
+  - Validation: 735 samples
+- **Features**: 768-dim BEATs features (variable length per clip)
+- **Pretrained weights**: `primitive_extractor_clotho_recon_best.pt` (26 parameter tensors transferred)
+- **Epochs**: 8
+- **Optimizer**: AdamW (lr=2e-4, weight_decay=1e-4)
+- **Scheduler**: Cosine decay with 10% warmup (5,286 steps)
+- **Loss function**:
+  - Classification: Weighted BCE (pos_weight handles class imbalance)
+  - reconstruction_loss: MSE (between input BEATs and decoder output)
+  - Total: `weighted_bce_loss + 0.1 * reconstruction_loss`
+- **Checkpoint metric**: Validation mAP (macro) — saved only when improved
+
+## Loss Strategy
+- **Weighted BCE** (optimization loss): Handles FSD50K's multi-label nature and class imbalance. Rare classes get higher penalty.
+- **reconstruction_loss** (regularization): Forces the 512-dim bottleneck to preserve acoustic details, not just collapse into a classifier.
+- **mAP** (checkpoint metric): Non-differentiable ranking metric that measures real classification quality.
+
+## Results
+
+| Epoch | weighted_bce_loss | reconstruction_loss | total_loss | val_mAP |
+|-------|-------------------|---------------------|------------|---------|
+| 1     | 0.4938            | 0.1174              | 0.5055     | 24.24%  |
+| 2     | 0.3081            | 0.1301              | 0.3211     | 30.68%  |
+| 3     | 0.2400            | 0.1259              | 0.2526     | 37.76%  |
+| 4     | 0.1911            | 0.1227              | 0.2034     | 41.97%  |
+| 5     | 0.1486            | 0.1197              | 0.1605     | 47.50%  |
+| 6     | 0.1161            | 0.1173              | 0.1278     | 50.70%  |
+| 7     | 0.0964            | 0.1154              | 0.1079     | 52.41%  |
+| 8     | 0.0860            | 0.1145              | 0.0974     | **52.72%** |
+
+### Best Result
+- **Validation mAP**: **52.72%** (achieved at epoch 8)
+- **Checkpoint**: `best_fsd50k_primitive.pt`
+
+## Notes
+- The sklearn warnings about "No positive class found in y_true" are expected. With only 735 validation samples across 200 classes, many rare classes have zero examples in the validation split. This does not affect the macro mAP calculation.
+- reconstruction_loss stayed stable (~0.11-0.13) throughout training, confirming the bottleneck preserves acoustic information while learning to classify.
+
+## Limitations
+- Only trained on **Part 1** (7,343 samples). Full FSD50K has **36,796 training samples** across parts 1-5.
+- The 52.72% mAP is a strong baseline but expected to improve significantly with the full dataset.
+
+## Next Steps
+1. Extract BEATs features for parts 2-5
+2. Retrain on the full 36,796 samples
+3. Evaluate on the separate FSD50K eval set (10,234 files)
+4. Consider longer training (15-20 epochs) with full data
+
+## Files
+- `best_fsd50k_primitive.pt` — Best checkpoint (52.72% mAP)
+- `training_result_part1.json` — Machine-readable results
+- `training_script_part1.md` — This file
+
+---
+*Trained on October 06, 2026*