Fast R-CNN

ConvNet Two-Stage Detector

class lucid.models.FastRCNN(config: FastRCNNConfig)

FastRCNN implements the Fast Region-based Convolutional Neural Network architecture for object detection, building upon the R-CNN approach by introducing a more efficient detection pipeline. It replaces per-region feature extraction with RoI pooling and integrates classification and bounding box regression into a single forward pass. Model structure is defined through FastRCNNConfig.

        %%{init: {"flowchart":{"curve":"monotoneX","nodeSpacing":50,"rankSpacing":50}} }%%
flowchart LR
  linkStyle default stroke-width:2.0px
  subgraph sg_m0["<span style='font-size:20px;font-weight:700'>FastRCNN</span>"]
  style sg_m0 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
    subgraph sg_m1["backbone"]
    style sg_m1 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
      subgraph sg_m2["Sequential"]
        direction TB;
      style sg_m2 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
        m3["Conv2d<br/><span style='font-size:11px;color:#c53030;font-weight:400'>(1,3,64,64) → (1,32,32,32)</span>"];
        m4["BatchNorm2d"];
        m5["ReLU"];
        m6["Conv2d"];
        m7["BatchNorm2d"];
        m8["ReLU"];
        m9["Conv2d<br/><span style='font-size:11px;color:#c53030;font-weight:400'>(1,32,32,32) → (1,64,32,32)</span>"];
      end
      m10["MaxPool2d<br/><span style='font-size:11px;color:#b7791f;font-weight:400'>(1,64,32,32) → (1,64,16,16)</span>"];
      subgraph sg_m11["Sequential"]
        direction TB;
      style sg_m11 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
        subgraph sg_m12["_ResNeStBottleneck"]
          direction TB;
        style sg_m12 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
          m13["ConvBNReLU2d"];
          m14["_SplitAttention"];
          m15["Conv2d<br/><span style='font-size:11px;color:#c53030;font-weight:400'>(1,64,16,16) → (1,256,16,16)</span>"];
          m16["BatchNorm2d"];
          m17["ReLU"];
          m18["Sequential<br/><span style='font-size:11px;font-weight:400'>(1,64,16,16) → (1,256,16,16)</span>"];
        end
      end
      subgraph sg_m19["Sequential x 2"]
        direction TB;
      style sg_m19 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
        m19_in(["Input"]);
        m19_out(["Output"]);
  style m19_in fill:#e2e8f0,stroke:#64748b,stroke-width:1px;
  style m19_out fill:#e2e8f0,stroke:#64748b,stroke-width:1px;
        subgraph sg_m20["_ResNeStBottleneck"]
          direction TB;
        style sg_m20 fill:#000000,fill-opacity:0.05,stroke:#000000,stroke-opacity:0.75,stroke-width:1px
          m21["ConvBNReLU2d<br/><span style='font-size:11px;font-weight:400'>(1,256,16,16) → (1,128,16,16)</span>"];
          m22["_SplitAttention"];
          m23["AvgPool2d<br/><span style='font-size:11px;color:#b7791f;font-weight:400'>(1,128,16,16) → (1,128,8,8)</span>"];
          m24["Conv2d<br/><span style='font-size:11px;color:#c53030;font-weight:400'>(1,128,8,8) → (1,512,8,8)</span>"];
          m25["BatchNorm2d"];
          m26["ReLU"];
          m27["Sequential<br/><span style='font-size:11px;font-weight:400'>(1,256,16,16) → (1,512,8,8)</span>"];
        end
      end
    end
    m28["ROIAlign<br/><span style='font-size:11px;font-weight:400'>(1,1024,4,4)x3 → (1,1024,7,7)</span>"];
    m29["SelectiveSearch<br/><span style='font-size:11px;font-weight:400'>(3,64,64) → (1,4)</span>"];
    m30(["Linear x 2<br/><span style='font-size:11px;color:#2b6cb0;font-weight:400'>(1,50176) → (1,4096)</span>"]);
    m31(["Dropout x 2"]);
    m32(["Linear x 2<br/><span style='font-size:11px;color:#2b6cb0;font-weight:400'>(1,4096) → (1,100)</span>"]);
  end
  input["Input<br/><span style='font-size:11px;color:#a67c00;font-weight:400'>(1,3,64,64)</span>"];
  output["Output<br/><span style='font-size:11px;color:#a67c00;font-weight:400'>(1,100)x2</span>"];
  style input fill:#fff3cd,stroke:#a67c00,stroke-width:1px;
  style output fill:#fff3cd,stroke:#a67c00,stroke-width:1px;
  style m3 fill:#ffe8e8,stroke:#c53030,stroke-width:1px;
  style m4 fill:#e6fffa,stroke:#2c7a7b,stroke-width:1px;
  style m5 fill:#faf5ff,stroke:#6b46c1,stroke-width:1px;
  style m6 fill:#ffe8e8,stroke:#c53030,stroke-width:1px;
  style m7 fill:#e6fffa,stroke:#2c7a7b,stroke-width:1px;
  style m8 fill:#faf5ff,stroke:#6b46c1,stroke-width:1px;
  style m9 fill:#ffe8e8,stroke:#c53030,stroke-width:1px;
  style m10 fill:#fefcbf,stroke:#b7791f,stroke-width:1px;
  style m15 fill:#ffe8e8,stroke:#c53030,stroke-width:1px;
  style m16 fill:#e6fffa,stroke:#2c7a7b,stroke-width:1px;
  style m17 fill:#faf5ff,stroke:#6b46c1,stroke-width:1px;
  style m23 fill:#fefcbf,stroke:#b7791f,stroke-width:1px;
  style m24 fill:#ffe8e8,stroke:#c53030,stroke-width:1px;
  style m25 fill:#e6fffa,stroke:#2c7a7b,stroke-width:1px;
  style m26 fill:#faf5ff,stroke:#6b46c1,stroke-width:1px;
  style m30 fill:#ebf8ff,stroke:#2b6cb0,stroke-width:1px;
  style m31 fill:#edf2f7,stroke:#4a5568,stroke-width:1px;
  style m32 fill:#ebf8ff,stroke:#2b6cb0,stroke-width:1px;
  input --> m29;
  m10 --> m13;
  m13 --> m14;
  m14 --> m15;
  m15 --> m16;
  m16 --> m18;
  m17 -.-> m21;
  m18 --> m17;
  m19_in -.-> m21;
  m19_out --> m28;
  m21 --> m22;
  m22 --> m23;
  m23 --> m24;
  m24 --> m25;
  m25 --> m27;
  m26 --> m19_in;
  m27 --> m19_out;
  m27 --> m26;
  m28 -.-> m30;
  m29 --> m3;
  m3 --> m4;
  m30 --> m31;
  m31 -.-> m30;
  m31 --> m32;
  m32 --> output;
  m4 --> m5;
  m5 --> m6;
  m6 --> m7;
  m7 --> m8;
  m8 --> m9;
  m9 --> m10;
    

Class Signature

class FastRCNN(nn.Module):
    def __init__(self, config: FastRCNNConfig) -> None

Parameters

• config (FastRCNNConfig): Configuration object describing the backbone, feature-map channel width, RoIAlign output size, fully connected head width, bounding-box regression normalization constants, dropout, and proposal generator.

Architecture

Fast R-CNN improves over the original R-CNN by computing the CNN feature map once per image and classifying object proposals directly on this shared map:

1. Full-Image Feature Map:

   • The input image is passed through the backbone to extract a dense feature map.

2. Region of Interest (RoI) Pooling:

   • Region proposals are projected onto the feature map and cropped to a fixed size using RoI pooling (size defined by pool_size).

3. Two-Stream Head:

   • Each pooled region is passed through a set of fully connected layers.

   • One stream performs classification over num_classes.

   • The other stream regresses bounding box adjustments per class.

4. Bounding Box Normalization:

   • Regression outputs are scaled using bbox_reg_means and bbox_reg_stds.

Examples

>>> import lucid
>>> import lucid.models as models
>>> import lucid.nn as nn
>>> backbone = nn.Sequential(
...     nn.Conv2d(3, 64, kernel_size=3, padding=1),
...     nn.ReLU(),
... )
>>> config = models.FastRCNNConfig(
...     backbone=backbone,
...     feat_channels=64,
...     num_classes=4,
... )
>>> model = models.FastRCNN(config)
>>> images = lucid.random.randn(1, 3, 256, 256)
>>> detections = model.predict(images)
>>> first = detections[0]
>>> print(first["boxes"].shape, first["scores"].shape, first["labels"].shape)