# Hybrid Velocity Estimator trained with non augmented data (5202 samples) and 80 Epochs. L1 loss was modified into a weighted loss. Modified FC

The main purpose of this experiment is training the first version of Hybrid Estimator using ImageVelocityEstimator for image2embedding transformation and TabularVelocityEstimator aproach in a new regressor. 

- L1 loss was modified into a weighted loss in order to lower MAE in higher speed bins
- Fully connected regressor was modifed into a more powerfull architecture

**Experiment Architecture**:

```python
VelocityEstimatorModel(
  (model): VelocityNetwork(
    (image_encoder): VelocityNetwork(
      (backbone): Sequential(
        (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        (4): Sequential(
          (0): Bottleneck(
            (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
            (downsample): Sequential(
              (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            )
          )
          (1): Bottleneck(
            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
          (2): Bottleneck(
            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
        )
        (5): Sequential(
          (0): Bottleneck(
            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
            (downsample): Sequential(
              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            )
          )
          (1): Bottleneck(
            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
          (2): Bottleneck(
            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
          (3): Bottleneck(
            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
        )
        (6): Sequential(
          (0): Bottleneck(
            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
            (downsample): Sequential(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            )
          )
          (1): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
          (2): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
          (3): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
          (4): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
          (5): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
        )
        (7): Sequential(
          (0): Bottleneck(
            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
            (downsample): Sequential(
              (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
              (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            )
          )
          (1): Bottleneck(
            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
          (2): Bottleneck(
            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
          )
        )
        (8): AdaptiveAvgPool2d(output_size=(1, 1))
      )
      (embedding_head): Sequential(
        (0): Linear(in_features=2048, out_features=512, bias=True)
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
        (2): GELU(approximate='none')
        (3): Dropout(p=0.3, inplace=False)
      )
      (regression_head): Sequential(
        (0): Linear(in_features=512, out_features=128, bias=True)
        (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
        (2): GELU(approximate='none')
        (3): Dropout(p=0.2, inplace=False)
        (4): Linear(in_features=128, out_features=1, bias=True)
      )
    )
    (regressor): Sequential(
      (0): Linear(in_features=521, out_features=512, bias=True)
      (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
      (2): GELU(approximate='none')
      (3): Dropout(p=0.2, inplace=False)
      (4): Linear(in_features=512, out_features=256, bias=True)
      (5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
      (6): GELU(approximate='none')
      (7): Dropout(p=0.2, inplace=False)
      (8): Linear(in_features=256, out_features=128, bias=True)
      (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, bias=True, track_running_stats=True)
      (10): GELU(approximate='none')
      (11): Linear(in_features=128, out_features=64, bias=True)
      (12): GELU(approximate='none')
      (13): Linear(in_features=64, out_features=1, bias=True)
    )
  )
  (train_mae): MeanAbsoluteError()
  (val_mae): MeanAbsoluteError()
  (test_mae): MeanAbsoluteError()
)


```