Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Jakel21/vehicle-ReID-baseline
vehicle re-identification baseline for veri and vehicleID dataset
https://github.com/Jakel21/vehicle-ReID-baseline
reid vehicle
Last synced: about 1 month ago
JSON representation
vehicle re-identification baseline for veri and vehicleID dataset
- Host: GitHub
- URL: https://github.com/Jakel21/vehicle-ReID-baseline
- Owner: Jakel21
- Created: 2019-03-11T02:48:34.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-08-12T03:13:33.000Z (almost 5 years ago)
- Last Synced: 2024-02-04T13:12:55.455Z (4 months ago)
- Topics: reid, vehicle
- Language: Python
- Homepage:
- Size: 70.3 KB
- Stars: 129
- Watchers: 5
- Forks: 45
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
Lists
- Awesome-person-re-identification - [Jakel21(pytorch) - reID-2020)] (d. Codes)
README
# vehicle-ReID-baseline
## Introduction
Vehicle ReID baseline is a pytorch-based baseline for training and evaluating deep vehicle re-identification models on reid benchmarks.
## Updates
2019.4.1 update some test results
2019.3.11 update the basic baseline code
## Installation
1. cd to your preferred directory and run ' git clone https://github.com/Jakel21/vehicle-ReID '.
2. Install dependencies by pip install -r requirements.txt (if necessary).
## Datasets
+ [veri776](https://github.com/VehicleReId/VeRidataset)
+ [vehicleID](https://pkuml.org/resources/pku-vehicleid.html)
The keys to use these datasets are enclosed in the parentheses. See vehiclereid/datasets/__init__.py for details.Both two datasets need to pull request to the supplier.
## Models
+ resnet50
## Losses
+ cross entropy loss
+ triplet loss
## Tutorial
### train
Input arguments for the training scripts are unified in [args.py](./args.py).
To train an image-reid model with cross entropy loss, you can do
```
python train-xent-tri.py \
-s veri \ #source dataset for training
-t veri \ # target dataset for test
--height 128 \ # image height
--width 256 \ # image width
--optim amsgrad \ # optimizer
--lr 0.0003 \ # learning rate
--max-epoch 60 \ # maximum epoch to run
--stepsize 20 40 \ # stepsize for learning rate decay
--train-batch-size 64 \
--test-batch-size 100 \
-a resnet50 \ # network architecture
--save-dir log/resnet50-veri \ # where to save the log and models
--gpu-devices 0 \ # gpu device index
```
### test
Use --evaluate to switch to the evaluation mode. In doing so, no model training is performed.
For example you can load pretrained model weights at path_to_model.pth.tar on veri dataset and do evaluation on VehicleID, you can do
```
python train_imgreid_xent.py \
-s veri \ # this does not matter any more
-t vehicleID \ # you can add more datasets here for the test list
--height 128 \
--width 256 \
--test-size 800 \
--test-batch-size 100 \
--evaluate \
-a resnet50 \
--load-weights path_to_model.pth.tar \
--save-dir log/eval-veri-to-vehicleID \
--gpu-devices 0 \
```
## Results
Some test results on veri776 and vehicleID:
### veri776
model:resnet50
loss: xent+htri
| mAP | rank-1 | rank-5 | rank-20 |
|:---:| :----: | :----: | :-----: |
|59.0|87.6|94.3|98.2|
### vehicleID
model:resnet50
loss: xent+htri
| testset size | mAP | rank-1 | rank-5 | rank-20 |
| :----------- |:---:| :----: | :----: | :-----: |
|800|76.4|69.1|85.8|94.5|
|1600|74.1|67.4|80.5|90.5|
|2400|71.4|65.2|78.3|89.2|