refactor: strided conv encoder with 80x60 input resolution

- CNNEncoder: stride=2 convs replace Conv2d+MaxPool2d pattern
- 3 layers (32,64,128) instead of 4 (32,64,128,256), GRU input 192
- DecodeSample resizes grayscale frames to 80x60 via INTER_AREA
- Model params: 227K (was 1.5M), input 80x60 (was 320x240)

Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>

AGENTS.md

@@ -1,56 +1,49 @@
 # UZH-FPV Velocity Prediction
 
-从 DAVIS 事件相机灰度图像序列中预测**机体速度**（body-frame forward/lateral velocity）。
+从 DAVIS 事件相机灰度图像序列预测机体速度（body-frame forward/lateral velocity）。
 
 ## 项目结构
 
 ```
 uzh_fpv/
-├── AGENTS.md                          # ← 本文件
-├── requirements.txt                   # Python 依赖
-├── DATASET_FORMAT.md                  # 数据集格式详细说明
-├── rosbag2wds.py                      # ROS bag → WebDataset shard 转换脚本
-├── batch_convert.sh                   # 批量转换脚本
-├── dataset/                           # 数据集（.gitignore 忽略）
+├── AGENTS.md
+├── requirements.txt
+├── DATASET_FORMAT.md
+├── rosbag2wds.py                      # ROS bag → WebDataset shard
+├── batch_convert.sh
+├── dataset/                           # 数据集（.gitignore）
 │   └── <scene_name>/
-│       ├── shard_0000.tar             # WebDataset shard（图像+GT）
-│       ├── imu_sequence.npz           # 完整 IMU 序列
-│       └── metadata.json              # 元信息
+│       ├── shard_0000.tar
+│       ├── imu_sequence.npz
+│       └── metadata.json
 ├── src/
-│   ├── event_utils.py                 # EventProcessor: 帧间亮度变化 → 模拟事件帧
+│   ├── event_utils.py                 # 帧间亮度变化 → 模拟事件帧
 │   └── velocity_prediction/           # 主项目代码
-│       ├── __init__.py                # 模块说明
-│       ├── config.py                  # 路径、模型架构、训练超参数
-│       ├── utils.py                   # 四元数运算（torch + numpy 封装）
-│       ├── transforms.py              # 数据预处理管线
-│       ├── dataset.py                 # WebDataset 加载 + 序列采样
-│       ├── model.py                   # CNN + PoseMLP + GRU + Head
-│       ├── train.py                   # 训练循环
-│       └── evaluate.py                # 评估 + 绘图
+│       ├── config.py
+│       ├── utils.py
+│       ├── transforms.py
+│       ├── dataset.py
+│       ├── model.py
+│       ├── train.py
+│       └── evaluate.py
 ├── visualize/
-│   ├── __init__.py
-│   └── visualize_dataset.py           # 数据集可视化：叠加位姿信息并生成视频
-├── benchmark/
-│   ├── __init__.py
-│   ├── config.py                      # 评估配置
-│   ├── evaluate.py                    # 完整评估管线
-│   └── benchmark.py                   # 统一评估入口
-├── checkpoints/                       # 模型权重（.gitignore 忽略）
-├── logs/                              # TensorBoard 日志（.gitignore 忽略）
-└── videos/                            # 可视化输出视频
+│   └── visualize_dataset.py
+├── checkpoints/                       # 模型权重（.gitignore）
+├── logs/                              # TensorBoard 日志（.gitignore）
+└── videos/                            # 可视化输出（.gitignore）
 ```
 
 ## 运行环境
 
 ```bash
-uv run python -m <module>    # 使用 uv 虚拟环境运行
+uv run python -m <module>
 ```
 
-依赖见 `requirements.txt`，核心依赖：PyTorch、WebDataset、OpenCV、NumPy、Matplotlib。
+依赖：PyTorch, WebDataset, OpenCV, NumPy, Matplotlib。
 
 ## 数据集
 
-UZH-FPV 数据集，由 DAVIS 事件相机采集。每个场景目录包含：
+UZH-FPV 数据集，DAVIS 事件相机采集。每个场景目录：
 
 | 文件 | 格式 | 内容 |
 |------|------|------|
@@ -58,7 +51,7 @@ UZH-FPV 数据集，由 DAVIS 事件相机采集。每个场景目录包含：
 | `imu_sequence.npz` | NPZ | 完整 IMU 序列（加速度+角速度） |
 | `metadata.json` | JSON | 场景元信息 |
 
-shard 中每个样本的字段：
+shard 样本字段：
 
 | Key | 类型 | 说明 |
 |-----|------|------|
@@ -67,8 +60,6 @@ shard 中每个样本的字段：
 | `pose` | float32[7] | `[x, y, z, qx, qy, qz, qw]` 世界→机体四元数 |
 | `vel` | float32[6] | `[vx, vy, vz, wx, wy, wz]` 世界线速度 + 角速度 |
 
-坐标系：z 轴与重力对齐（水平坐标系）。
-
 ### 场景列表
 
 | 场景 | 帧数 | 类型 |
@@ -78,62 +69,11 @@ shard 中每个样本的字段：
 | outdoor_forward_1/3/5 | 907~13299 | 室外前飞 |
 | outdoor_45_1 | 799 | 室外 45° 飞行 |
 
-## 模型
-
-### 架构
-
-```
-Event frame (1, 240, 320) ──► CNN (4 Conv+Pool+GAP, 256-d)
-                                                                  │
-Body up (3,) ──► PoseMLP (3→32→64, 64-d) ────────────────────────┤
-                                                                  │
-                                                          concat (320-d)  ← per-frame
-                                                                  │
-                                                          GRU (hidden=128)
-                                                                  │
-                                                          Head MLP (128→64→2)
-                                                                  │
-                                                          [v_right, v_forward]
-```
-
-**注意**：当前 CNN 编码器被禁用（输出全零），模型仅依赖 `PoseMLP + GRU + Head`。
-
-### 输入
-
-- `events`: `(B, S, 1, H, W)` — 模拟事件帧，值域 `{-1, 0, +1}`
-- `tilt`: `(B, S, 3)` — body up 向量（世界 up 旋转到机体坐标系），仅含 pitch/roll，不含 yaw，单位向量
-
-### 输出
-
-- `v_body`: `(B, 2)` — 机体坐标系 `[v_right, v_forward]` 速度 (m/s)
-
-### 数据预处理管线
-
-```
-shard_*.tar → DecodeSample → SimulateEvents → ComputeTilt → ComputeBodyVelocity → NormalizeVelocity
-```
-
-1. **DecodeSample**: JPEG → 灰度图 uint8 (H,W)；bytes → float32 数组
-2. **SimulateEvents**: 帧间亮度变化 → 二值事件帧 `{-1, 0, +1}`
-3. **ComputeTilt**: 四元数 (world→odom) → 应用 R_odom_to_body → 旋转 world-up [0,0,1] → body up 向量 (3,)
-4. **ComputeBodyVelocity**: 世界速度 → 应用 R_odom_to_body → yaw 补偿（仅去除偏航，保留 tilt）→ 水平面 `[v_right, v_forward]`
-5. **NormalizeVelocity**: 归一化
-
-### 训练配置
-
-- seq_len=8, batch_size=32, epochs=100
-- lr=1e-3, AdamW, StepLR (step=30, gamma=0.5)
-- Loss: MSELoss
-- 训练/验证/测试场景见 `config.py`
-
 ## 关键命令
 
 ```bash
-# 训练
-uv run python -m src.velocity_prediction.train --device cuda:0
-
-# 评估
-uv run python -m src.velocity_prediction.evaluate --checkpoint checkpoints/best.pt
+# 训练（GPU 优先 cuda:7）
+uv run python -m src.velocity_prediction.train --device cuda:7
 
 # 数据集可视化（单场景）
 uv run python -m visualize.visualize_dataset --scene indoor_forward_3 --output videos/scene.mp4
@@ -143,49 +83,4 @@ uv run python -m visualize.visualize_dataset --all --output videos/
 
 # 数据集可视化（实时显示）
 uv run python -m visualize.visualize_dataset --scene indoor_forward_3 --show
-
-# Benchmark 评估
-uv run python -m benchmark.benchmark --checkpoint checkpoints/best.pt
 ```
-
-## 可视化说明
-
-`visualize/visualize_dataset.py` 在每帧图像上叠加：
-
-- 帧号、时间戳、世界坐标位置
-- 欧拉角 `[roll, pitch, yaw]`（从 body 四元数计算）
-- Body up 向量 `[x, y, z]`
-- 机体速度 `v_body [forward, lateral]`
-- 世界速度 `v_world [vx, vy, vz]`
-- 机体坐标系三轴箭头（左下角）
-- 机体速度方向箭头（图像中心）
-
-## 关键约定
-
-- 四元数格式：`[x, y, z, w]`（不是 `[w, x, y, z]`）
-- GT 四元数表示 **world→odom**（不是 world→body），通过静态 R_odom_to_body 校正
-- Body 坐标系（ROS 右手系）：`body_x=右, body_y=前, body_z=上`
-- R_odom_to_body = R_y(45°) @ R_x(90°)：先绕 odom_x 转 +90°，再绕 odom_y 转 +45°
-- 速度归一化统计量：待重新计算
-- 模型预测 `[v_right, v_forward]`（右向和前向速度）
-- 所有代码在项目根目录下以 `uv run python -m <module>` 运行
-- GPU 优先使用 `cuda:7`，训练时添加 `--device cuda:7`
-
-## 已知问题
-
-### 1. 滑窗跨 shard 边界
-
-`dataset.py` 中滑窗实现基于 WebDataset 串联后的连续流，不感知 shard 边界。当样本恰好处于 shard 末尾时，序列会跨越到下一个 shard 的起始帧。
-
-- 影响：每个 shard 边界处约有 `seq_len` 个序列包含跨 shard 样本（占总数 <1%）
-- 修复思路：在 `_sliding_window_fn` 中注入 shard 边界标记，遇到边界时清空缓冲区
-- 严重程度：低。若 shard 内帧数远大于 seq_len，可忽略
-
-### 2. `SimulateEvents` 跨 shard 状态残留
-
-`EventProcessor` 内部维护 `_prev_frame` 用于帧差计算。跨 shard 时，新 shard 的第一帧会与上一个 shard 最后一帧计算差，产生错误的事件帧。
-
-- 影响：每个 shard 的第 1 帧事件帧错误，涉及该帧的所有滑窗序列均受影响
-- 每 shard 错误帧数：1 帧（加上滑窗放大，约 `seq_len` 个序列各包含此帧）
-- 修复：在 shard 边界处调用 `EventProcessor.reset()`。需在 `_build_pipeline` 中插入边界信号或改用按 shard 独立处理的方案
-- 严重程度：低。每 shard 仅 1 帧，训练数据量大时可忽略

src/velocity_prediction/config.py

@@ -39,10 +39,10 @@ VAL_SCENES = [
     # "indoor_forward_3", "indoor_forward_9", "indoor_forward_10",  # Easy
 ]
 TEST_SCENES = [
-    "indoor_forward_9",
+    # "indoor_forward_9",
     # "indoor_forward_9","indoor_forward_3",
     # "indoor_forward_7",      # Hard 室内
-    # "outdoor_forward_1",     # Easy 室外
+    "outdoor_forward_1",     # Easy 室外
     # "outdoor_forward_5"      # Hard 室外
     # "indoor_forward_3", "indoor_forward_9", "indoor_forward_10",  # Easy
 ]
@@ -53,9 +53,9 @@ TEST_SCENES = [
 @dataclass
 class CNNConfig:
     in_channels: int = 1
-    channels: tuple = (32, 64, 128, 256)       # per-layer output channels
+    channels: tuple = (32, 64, 128)            # per-layer output channels
     kernel_size: int = 3
-    pool_size: int = 2
+    stride: int = 2                            # strided conv replaces conv+pool
     use_bn: bool = True
 
 
@@ -68,7 +68,7 @@ class PoseMLPConfig:
 
 @dataclass
 class GRUConfig:
-    input_size: int = 320       # CNN(256) + PoseMLP(64)
+    input_size: int = 192       # CNN(128) + PoseMLP(64)
     hidden_size: int = 128
     num_layers: int = 1
     dropout: float = 0.0
@@ -104,7 +104,7 @@ class TrainConfig:
     seed: int = 42
 
     # Sliding window: stride=1 → full overlap, stride=seq_len → non-overlapping
-    sliding_window_stride: int = 1
+    sliding_window_stride: int = 64
 
     # Event simulation
     event_threshold: float = 0.1

src/velocity_prediction/evaluate.py

@@ -166,7 +166,11 @@ def main():
     ckpt = torch.load(args.checkpoint, map_location="cpu")
     model.load_state_dict(ckpt["model_state_dict"])
     model.to(device)
+
+    # Restore event threshold from checkpoint, fall back to config default
+    event_threshold = ckpt.get("event_threshold", train_cfg.event_threshold)
     print(f"Loaded checkpoint from {args.checkpoint} (epoch={ckpt.get('epoch', '?')})")
+    print(f"Event threshold: {event_threshold} (checkpoint={ckpt.get('event_threshold', 'not saved')}, config={train_cfg.event_threshold})")
 
     # Evaluate each scene independently → NaN gaps prevent plot mixing
     from src.velocity_prediction.config import TEST_SCENES
@@ -180,7 +184,7 @@ def main():
             stride=1,
             batch_size=1,
             num_workers=0,               # strict temporal order
-            event_threshold=train_cfg.event_threshold,
+            event_threshold=event_threshold,
             event_use_log=train_cfg.event_use_log,
         )
         results = evaluate_stateful(model, loader, device)

src/velocity_prediction/model.py

@@ -14,7 +14,10 @@ from src.velocity_prediction.config import model_cfg
 
 class CNNEncoder(nn.Module):
     """
-    4-layer ConvNet with BatchNorm, ReLU, MaxPool, ending with Global Avg Pool.
+    Strided-convolution encoder with BatchNorm and LeakyReLU.
+
+    Each layer uses stride=2 to downsample, replacing the traditional
+    conv+pool pattern.  Ends with Global Avg Pool.
 
     Input:  (B, S, 1, H, W)  — processed per-frame (flattened to (B*S, 1, H, W))
     Output: (B, S, C_out)     — per-frame feature vectors
@@ -24,15 +27,15 @@ class CNNEncoder(nn.Module):
         super().__init__()
         channels = cfg.channels
         in_ch = cfg.in_channels
+        stride = cfg.stride
 
         layers = []
         for out_ch in channels:
             layers.extend([
-                nn.Conv2d(in_ch, out_ch, kernel_size=cfg.kernel_size, padding=cfg.kernel_size // 2),
+                nn.Conv2d(in_ch, out_ch, kernel_size=cfg.kernel_size,
+                          stride=stride, padding=cfg.kernel_size // 2),
                 nn.BatchNorm2d(out_ch) if cfg.use_bn else nn.Identity(),
-                nn.Identity(),
                 nn.LeakyReLU(inplace=True),
-                nn.MaxPool2d(cfg.pool_size),
             ])
             in_ch = out_ch
 
@@ -102,7 +105,7 @@ class VelocityPredictionModel(nn.Module):
         self.cnn = CNNEncoder(cnn_cfg)
         self.pose_mlp = PoseMLP(pose_cfg)
 
-        fused_dim = self.cnn.out_dim + self.pose_mlp.out_dim  # 256 + 64 = 320
+        fused_dim = self.cnn.out_dim + self.pose_mlp.out_dim  # 128 + 64 = 192
 
         self.gru = nn.GRU(
             input_size=fused_dim,
@@ -139,14 +142,13 @@ class VelocityPredictionModel(nn.Module):
         B, S = events.shape[:2]
 
         # Per-frame encoding
-        cnn_feat = self.cnn(events)           # (B, S, 256)
+        cnn_feat = self.cnn(events)           # (B, S, 128)
         # B, S = events.shape[:2]
-        # cnn_feat = events.new_zeros(B, S, self.cnn.out_dim) # 全零替代
 
         pose_feat = self.pose_mlp(tilt)       # (B, S, 64)
 
         # Fuse per frame
-        fused = torch.cat([cnn_feat, pose_feat], dim=-1)  # (B, S, 320)
+        fused = torch.cat([cnn_feat, pose_feat], dim=-1)  # (B, S, 192)
 
         # GRU temporal modelling — accepts external hidden state for TBPTT
         gru_out, h_new = self.gru(fused, h)    # (B, S, 128), (num_layers, B, 128)
@@ -174,9 +176,9 @@ class VelocityPredictionModel(nn.Module):
             v_body: (B, 2)  body-frame [v_right, v_forward]
             h_new:  (num_layers, B, hidden_size)
         """
-        cnn_feat = self.cnn(events)          # (B, 1, 256)
+        cnn_feat = self.cnn(events)          # (B, 1, 128)
         pose_feat = self.pose_mlp(tilt)      # (B, 1, 64)
-        fused = torch.cat([cnn_feat, pose_feat], dim=-1)  # (B, 1, 320)
+        fused = torch.cat([cnn_feat, pose_feat], dim=-1)  # (B, 1, 192)
         _, h_new = self.gru(fused, h)        # (num_layers, B, 128)
         last_hidden = h_new[-1]              # (B, 128)
         v_body = self.head(last_hidden)      # (B, 2)
@@ -195,10 +197,11 @@ if __name__ == "__main__":
     print(f"Total trainable parameters: {total:,} ({total/1e6:.3f} M)")
 
     # Forward pass test
-    B, S, H, W = 4, 8, 240, 320
+    B, S, H, W = 4, 8, 60, 80
     events = torch.randn(B, S, 1, H, W)
     tilt = torch.randn(B, S, 3)
     out, h = model(events, tilt)
     print(f"Input events: {events.shape}")
     print(f"Input tilt:   {tilt.shape}")
     print(f"Output:       {out.shape}  (should be [4, 8, 2])")
+    print(f"CNN out_dim:  {model.cnn.out_dim}")

src/velocity_prediction/train.py

@@ -8,6 +8,7 @@ Usage:
 
 import argparse
 import os
+import random
 import time
 import numpy as np
 from pathlib import Path
@@ -39,9 +40,9 @@ def train_one_epoch(
     log_interval: int = 50,
     global_step: int = 0,
     use_amp: bool = True,
-    h_state: torch.Tensor = None,
-) -> tuple[float, int, torch.Tensor]:
-    """Train for one epoch. Returns (avg_loss, updated_global_step, h_state_final)."""
+) -> tuple[float, int]:
+    """Train for one epoch (stateless — each batch starts from zero hidden state).
+    Returns (avg_loss, updated_global_step)."""
     model.train()
     total_loss = 0.0
     num_batches = 0
@@ -52,15 +53,11 @@ def train_one_epoch(
         tilt = batch["tilt"].to(device)              # (B, S, 3)
         target = batch["v_body_target"].to(device)   # (B, S, 2)
 
-        # Drop carried state if batch size changed (partial last batch / new scene)
-        if h_state is not None and h_state.shape[1] != events.shape[0]:
-            h_state = None
-
-        # Per-step supervision over the whole sequence — TBPTT hidden state
+        # Stateless: each batch starts from zero hidden state
         with torch.amp.autocast(device.type, enabled=use_amp):
-            pred_seq, h_new = model(events, tilt, h_state)   # (B, S, 2), (L, B, H)
-            loss_per_step = criterion(pred_seq, target)      # (B, S, 2)
-            loss_per_step = loss_per_step.mean(-1)           # (B, S)
+            pred_seq, _ = model(events, tilt, None)     # (B, S, 2), h=None
+            loss_per_step = criterion(pred_seq, target)  # (B, S, 2)
+            loss_per_step = loss_per_step.mean(-1)       # (B, S)
             loss = loss_per_step.mean()
 
         optimizer.zero_grad()
@@ -68,9 +65,6 @@ def train_one_epoch(
         scaler.step(optimizer)
         scaler.update()
 
-        # Detach hidden state for next batch — gradient only back to current seq_len
-        h_state = h_new.detach()
-
         total_loss += loss.item()
         num_batches += 1
         global_step += 1
@@ -87,7 +81,7 @@ def train_one_epoch(
 
     avg_loss = total_loss / max(num_batches, 1)
     print(f"  Epoch {epoch} | Avg Loss: {avg_loss:.6f}")
-    return avg_loss, global_step, h_state
+    return avg_loss, global_step
 
 
 @torch.no_grad()
@@ -150,15 +144,12 @@ def main():
     print(f"Model parameters: {total_params:,} ({total_params/1e6:.3f} M)")
     print(f"AMP: {'enabled' if use_amp else 'disabled'}")
 
-    # Data loaders — TBPTT training requires strict temporal order
-    train_loader = create_tbptt_loader(
-        seq_len=train_cfg.seq_len,
-        batch_size=train_cfg.batch_size,
-        event_threshold=event_threshold,
-        event_use_log=train_cfg.event_use_log,
-    )
+    # ── Randomised sequence lengths for curriculum ──────────────
+    SEQ_LEN_CHOICES = [64, 96, 128, 160, 192]
+
+    # Validation uses a fixed seq_len for consistent comparison
     val_loader = create_val_loader(
-        seq_len=train_cfg.seq_len,
+        seq_len=128,
         stride=train_cfg.sliding_window_stride,
         batch_size=train_cfg.batch_size,
         num_workers=train_cfg.num_workers,
@@ -200,6 +191,10 @@ def main():
         global_step = ckpt.get("global_step", 0)
         best_val_loss = ckpt.get("best_val_loss", float("inf"))
         run_id = ckpt.get("run_id", None)
+        # Restore event threshold from checkpoint if present
+        if "event_threshold" in ckpt:
+            event_threshold = ckpt["event_threshold"]
+            print(f"Event threshold restored from checkpoint: {event_threshold}")
 
         print(f"Resumed from checkpoint: {ckpt_path}")
         print(f"  Resumed epoch={ckpt.get('epoch', '?')}, global_step={global_step}, "
@@ -216,20 +211,28 @@ def main():
     ckpt_dir.mkdir(parents=True, exist_ok=True)
     writer = SummaryWriter(log_dir=str(log_dir))
 
-    print(f"  seq_len={train_cfg.seq_len}, batch_size={train_cfg.batch_size}")
+    print(f"  seq_len=random({min(SEQ_LEN_CHOICES)}~{max(SEQ_LEN_CHOICES)}), "
+          f"batch_size={train_cfg.batch_size}")
     print(f"  lr={train_cfg.lr}, weight_decay={train_cfg.weight_decay}")
     print(f"  log_dir={log_dir}, checkpoint_dir={ckpt_dir}\n")
 
     for epoch in range(start_epoch, train_cfg.epochs + 1):
         epoch_start = time.time()
 
-        # Reset GRU hidden state at epoch start — each epoch begins with h=0
-        train_loss, global_step, _ = train_one_epoch(
+        # Random seq_len per epoch — GRU learns to handle varying temporal horizons
+        seq_len = random.choice(SEQ_LEN_CHOICES)
+        train_loader = create_tbptt_loader(
+            seq_len=seq_len,
+            batch_size=train_cfg.batch_size,
+            event_threshold=event_threshold,
+            event_use_log=train_cfg.event_use_log,
+        )
+
+        train_loss, global_step = train_one_epoch(
             model, train_loader, optimizer, criterion, scaler, device, epoch, writer,
             log_interval=train_cfg.log_interval,
             global_step=global_step,
             use_amp=use_amp,
-            h_state=None,
         )
         val_loss = validate(model, val_loader, criterion, device, use_amp=use_amp)
         scheduler.step()
@@ -238,6 +241,7 @@ def main():
         current_lr = scheduler.get_last_lr()[0]
 
         print(f"Epoch {epoch:3d}/{train_cfg.epochs} | "
+              f"seq_len={seq_len:3d} | "
               f"Train Loss: {train_loss:.6f} | Val Loss: {val_loss:.6f} | "
               f"LR: {current_lr:.2e} | Time: {epoch_time:.1f}s")
 
@@ -253,6 +257,7 @@ def main():
                 "model_state_dict": model.state_dict(),
                 "optimizer_state_dict": optimizer.state_dict(),
                 "scheduler_state_dict": scheduler.state_dict(),
+                "event_threshold": event_threshold,
                 "global_step": global_step,
                 "best_val_loss": best_val_loss,
                 "run_id": run_id,
@@ -269,6 +274,7 @@ def main():
                 "model_state_dict": model.state_dict(),
                 "optimizer_state_dict": optimizer.state_dict(),
                 "scheduler_state_dict": scheduler.state_dict(),
+                "event_threshold": event_threshold,
                 "global_step": global_step,
                 "best_val_loss": best_val_loss,
                 "run_id": run_id,

src/velocity_prediction/transforms.py

@@ -20,11 +20,16 @@ from src.velocity_prediction.config import VELOCITY_MEAN, VELOCITY_STD
 
 
 class DecodeSample:
-    """Decode raw bytes from WebDataset tar entry into numpy arrays."""
+    """Decode raw bytes from WebDataset tar entry into numpy arrays, resize to 80×60."""
+
+    def __init__(self, height: int = 60, width: int = 80):
+        self.height = height
+        self.width = width
 
     def __call__(self, sample: dict) -> dict:
-        # Image: JPEG bytes → grayscale uint8 (H, W)
+        # Image: JPEG bytes → grayscale uint8 (H, W) → resize
         img = cv2.imdecode(np.frombuffer(sample["jpg"], np.uint8), cv2.IMREAD_GRAYSCALE)
+        img = cv2.resize(img, (self.width, self.height), interpolation=cv2.INTER_AREA)
 
         # Timestamp
         ts = np.frombuffer(sample["ts"], dtype=np.float64).item()