文件列表:

LICENSE (1062, 2020-01-14)
assets (0, 2020-01-14)
assets\TensorBoard.png (60733, 2020-01-14)
assets\results.png (1326981, 2020-01-14)
benchmarks (0, 2020-01-14)
benchmarks\__init__.py (0, 2020-01-14)
benchmarks\run_SiamFC.py (2295, 2020-01-14)
configuration.py (4276, 2020-01-14)
datasets (0, 2020-01-14)
datasets\__init__.py (1, 2020-01-14)
datasets\dataloader.py (3902, 2020-01-14)
datasets\sampler.py (793, 2020-01-14)
datasets\transforms.py (2285, 2020-01-14)
datasets\vid.py (2407, 2020-01-14)
embeddings (0, 2020-01-14)
embeddings\__init__.py (0, 2020-01-14)
embeddings\convolutional_alexnet.py (5521, 2020-01-14)
experiments (0, 2020-01-14)
experiments\SiamFC-3s-color-pretrained.py (1281, 2020-01-14)
experiments\SiamFC-3s-color-scratch.py (985, 2020-01-14)
experiments\SiamFC-3s-gray-pretrained.py (1248, 2020-01-14)
experiments\SiamFC-3s-gray-scratch.py (1092, 2020-01-14)
inference (0, 2020-01-14)
inference\__init__.py (0, 2020-01-14)
inference\inference_wrapper.py (9975, 2020-01-14)
inference\tracker.py (8094, 2020-01-14)
metrics (0, 2020-01-14)
metrics\__init__.py (0, 2020-01-14)
metrics\track_metrics.py (5315, 2020-01-14)
scripts (0, 2020-01-14)
scripts\__init__.py (0, 2020-01-14)
scripts\build_VID2015_imdb.py (4641, 2020-01-14)
scripts\convert_pretrained_model.py (2878, 2020-01-14)
scripts\download_assets.py (1625, 2020-01-14)
scripts\preprocess_VID_data.py (4023, 2020-01-14)
scripts\run_tracking.py (2873, 2020-01-14)
scripts\show_tracking.py (3164, 2020-01-14)
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# SiamFC-TensorFlow A TensorFlow implementation of the SiamFC tracker ## Introduction This is a TensorFlow implementation of [Fully-Convolutional Siamese Networks for Object Tracking](https://arxiv.org/abs/1606.09549). You can find the original MatConvNet version [here](https://github.com/bertinetto/siamese-fc). The SiamFC authors have also released a TensorFlow port of the tracking part (using pretrained model only) in [here](https://github.com/torrvision/siamfc-tf). This TensorFlow implementation is designed with these goals: - [x] **Self-contained**. Data preparation, model training, tracking, visualization and logging all in one. - [x] **Correctness**. The tracking performance should be similar to the MatConvNet version. - [x] **Efficiency**. The training and inference procedures should be as efficient as possible. - [x] **Modularization**. The whole system should be modularized and easy to expand with new ideas. - [x] **Readability**. The code should be clear and consistent. ## Main Results In a computer with a `GeForce GTX 1080` GPU, the main results on OTB-100 are in the table below. Note that the performance of SiamFC-3s-color trained from scratch using our implementation is consistently better than SiamFC-3s-gray-scratch. Moreover, We observe that the tracking performance of saved models in different epochs varies considerably, therefore, you may want to evaluate a few more models instead of just picking the model in the last epoch. | | Reported (AUC) | Pretrained (AUC) | Train from scratch (AUC) | Training time | Tracking time| |-------------------|:-------------------:|:---------------------:|:-----:|:--------------:|:------------------:| |SiamFC-3s-color | -- | 0.578 | 0.584 | ~5h | ~120fps | |SiamFC-3s-gray | 0.582 | 0.587 | 0.573 | ~5h | ~120fps | Qualitative results:  ## Prerequisite The main requirements can be installed by: ```bash # (OPTIONAL) 0. It is highly recommended to create a virtualenv or conda environment # For example, # conda create -n tensorflow1.4 python=2.7 # source activate tensorflow1.4 # 1. Install TensorFlow 1.4.0 # Version 1.4.0 is required for training since we use tf.data API # You can use TensorFlow > 1.0 for tracking though. # Note the tracking performance slightly varies in different versions. # pip install tensorflow # For CPU pip install tensorflow-gpu # For GPU # 2. Install scipy for loading mat files pip install scipy # 3. Install sacred for experiments logging pip install sacred==0.7.5 # 4. Install matplotlib for visualizing tracking results pip install matplotlib # 5. Install opencv for preprocessing training examples pip install opencv-python # 6. Install pillow for some image-related operations pip install pillow # (OPTIONAL) 7. Install nvidia-ml-py for automatically selecting GPU pip install nvidia-ml-py ``` ## Tracking ```bash # 1. Clone this repository to your disk git clone https://github.com/bilylee/SiamFC-TensorFlow.git # 2. Change working directory cd SiamFC-TensorFlow # 3. Download pretrained models and one test sequence python scripts/download_assets.py # 4. Convert pretrained MatConvNet model into TensorFlow format. # Note we use SiamFC-3s-color-pretrained as one example. You # Can also use SiamFC-3s-gray-pretrained. python experiments/SiamFC-3s-color-pretrained.py # 5. Run tracking on the test sequence with the converted model python scripts/run_tracking.py # 6. Show tracking results # You can press Enter to toggle between play and pause, and drag the # scrolling bar in the figure. For more details, see utils/videofig.py python scripts/show_tracking.py ``` ## Training ```bash # 1. Download and unzip the ImageNet VID 2015 dataset (~86GB) # Now, we assume it is unzipped in /path/to/ILSVRC2015 DATASET=/path/to/ILSVRC2015 # 2. Clone this repository to your disk # (Skip this step if you have already done it in the Tracking section) git clone https://github.com/bilylee/SiamFC-TensorFlow.git # 3. Change working directory cd SiamFC-TensorFlow # 4. Create a soft link pointing to the ImageNet VID dataset mkdir -p data ln -s $DATASET data/ILSVRC2015 # 5. Prepare training data # If you have followed the data preparation procedures in # the MatConvNet implementation, simply create a soft link # pointing to the curated dataset: # ln -s $CURATED_DATASET data/ILSVRC2015-VID-Curation # Otherwise, create it from scratch by python scripts/preprocess_VID_data.py # 6. Split train/val dataset and store corresponding image paths python scripts/build_VID2015_imdb.py # 7. Start training # You can get quite good results after ~70k iterations. python experiments/SiamFC-3s-color-scratch.py # 8. (OPTIONAL) View the training progress in TensorBoard # Open a new terminal session and cd to SiamFC-TensorFlow, then tensorboard --logdir=Logs/SiamFC/track_model_checkpoints/SiamFC-3s-color-scratch ``` Example TensorBoard outputs are like:  ## Benchmark You can use the `run_SiamFC.py` in `benchmarks` directory to integrate with the OTB evaluation toolkit. The OTB toolkit has been ported to python. The original version is [here](https://github.com/jwlim/tracker_benchmark). However, you may want to use my [custom version](https://github.com/bilylee/tracker_benchmark) where several bugs are fixed. Assume that you have followed the steps in `Tracking` or `Training` section and now have a pretrained/trained-from-scratch model to evaluate. To integrate with the evaluation toolkit, ```bash # Let's follow this directory structure # Your-Workspace-Directory # |- SiamFC-TensorFlow # |- tracker_benchmark # |- ... # 0. Go to your workspace directory cd /path/to/your/workspace # 1. Download the OTB toolkit git clone https://github.com/bilylee/tracker_benchmark.git # 2. Modify line 22 and 25 in SiamFC-TensorFlow/benchmarks/run_SiamFC.py accordingly. # In Linux, you can simply run sed -i "s+/path/to/SiamFC-TensorFlow+`realpath SiamFC-TensorFlow`+g" SiamFC-TensorFlow/benchmarks/run_SiamFC.py # 3. Copy run_SiamFC.py to the evaluation toolkit cp SiamFC-TensorFlow/benchmarks/run_SiamFC.py tracker_benchmark/scripts/bscripts # 4. Add the tracker to the evaluation toolkit list echo "\nfrom run_SiamFC import *" >> tracker_benchmark/scripts/bscripts/__init__.py # 5. Create tracker directory in the evaluation toolkit mkdir tracker_benchmark/trackers/SiamFC # 6. Start evaluation (it will take some time to download test sequences). echo "tb100" | python tracker_benchmark/run_trackers.py -t SiamFC -s tb100 -e OPE # 7. Get the AUC score sed -i "s+tb50+tb100+g" tracker_benchmark/draw_graph.py python tracker_benchmark/draw_graph.py ``` ## License SiamFC-TensorFlow is released under the MIT License (refer to the LICENSE file for details).

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