Original Paper
: Learning Spatiotemporal Features with 3D Convolutional NetworksReferenced repo
: pytorch-video-recognitionWhat did I do?
: Train C3D from scratch with UCF101 (the paper trained C3D with Sports-1M)
This paper proposed a simple, yet effective approach for spatio-temporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset (Sports-1M).
- 3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets.
- Best setting : A homogeneous architecture with 3x3x3 convolution kernels in all layers.
- Using C3D features with a linear classifier can outperform or approach sota on different video analysis benchmarks.
- Dataset : UCF101
- Train : UCF101 train split 1 (trainlist01.txt)
- Valiation : UCF101 test split 1 (testlist01.txt)
python main.py --learning_rate 0.005 --scheduler step --step_size 20
Epoch | train_acc | train_loss | val_acc | val_loss |
---|---|---|---|---|
60 | 98.469 | 0.045 | 42.162 | 4.651 |
Collecting environment information...
PyTorch version: 1.6.0+cu101
Is debug build: False
CUDA used to build PyTorch: 10.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.10.2
Libc version: glibc-2.17
Python version: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-4.15.0-39-generic-x86_64-with-debian-buster-sid
Is CUDA available: True
CUDA runtime version: 10.1.243
GPU models and configuration:
GPU 0: Tesla V100-SXM2-32GB
GPU 1: Tesla V100-SXM2-32GB
GPU 2: Tesla V100-SXM2-32GB
GPU 3: Tesla V100-SXM2-32GB
GPU 4: Tesla V100-SXM2-32GB
GPU 5: Tesla V100-SXM2-32GB
GPU 6: Tesla V100-SXM2-32GB
GPU 7: Tesla V100-SXM2-32GB
Nvidia driver version: 410.104
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Versions of relevant libraries:
[pip3] numpy==1.18.2
[pip3] pytorchvideo==0.1.5
[pip3] torch==1.6.0+cu101
[pip3] torchinfo==1.6.3
[pip3] torchsummary==1.5.1
[pip3] torchsummaryX==1.3.0
[pip3] torchvision==0.7.0+cu101
[conda] numpy 1.18.2 pypi_0 pypi
[conda] torch 1.6.0+cu101 pypi_0 pypi
[conda] torchvision 0.7.0+cu101 pypi_0 pypi