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Update readme according to issue #12

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shaoniangu 2019-03-26 17:02:11 +08:00
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@ -104,13 +104,13 @@ The designed architecture follows this guide [PyTorch-Project-Template](https://
5. Prepare pretrained model if you don't have
（1）resnet
（1）Resnet
```python
from torchvision import models
models.resnet50(pretrained=True)
```
（2）senet
（2）Senet
```python
import torch.utils.model_zoo as model_zoo
@ -118,10 +118,14 @@ The designed architecture follows this guide [PyTorch-Project-Template](https://
```
Then it will automatically download model in `~/.torch/models/`, you should set this path in `config/defaults.py` for all training or set in every single training config file in `configs/` or set in every single command.
（3）Load your self-trained model
If you want to continue your train process based on your self-trained model, you can change the configuration `PRETRAIN_CHOICE` from 'imagenet' to 'self' and set the `PRETRAIN_PATH` to your self-trained model. We offer `Experiment-pretrain_choice-all_tricks-tri_center-market.sh` as an example.
6. If you want to know the detailed configurations and their meaning, please refer to `config/defaults.py`. If you want to set your own parameters, you can follow our method: create a new yml file, then set your own parameters. Add `--config_file='configs/your yml file'` int the commands described below, then our code will merge your configuration. automatically.
## Train
You can run these commands in `.sh ` files for training different datasets of differernt loss. You can also directly run code `sh *.sh` to run our demo.
You can run these commands in `.sh ` files for training different datasets of differernt loss. You can also directly run code `sh *.sh` to run our demo after your custom modification.
1. Market1501, cross entropy loss + triplet loss
@ -137,23 +141,23 @@ python3 tools/train.py --config_file='configs/softmax_triplet_with_center.yml' M
```
## Test
You can test your model's performance directly by running these commands in `.sh ` files. You can also change the configuration to determine which feature of BNNeck is used and whether the feature is normalized (equivalent to use Cosine distance or Euclidean distance) for testing.
You can test your model's performance directly by running these commands in `.sh ` files after your custom modification. You can also change the configuration to determine which feature of BNNeck is used and whether the feature is normalized (equivalent to use Cosine distance or Euclidean distance) for testing.
Please replace the data path of the model.
Please replace the data path of the model and set the `PRETRAIN_CHOICE` as 'self' to avoid time consuming on loading ImageNet pretrained model.
1. Test with Euclidean distance using feature before BN without re-ranking,.
```bash
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('market1501')" TEST.NECK_FEAT "('before')" TEST.FEAT_NORM "('no')" TEST.WEIGHT "('your path to trained checkpoints')"
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('market1501')" TEST.NECK_FEAT "('before')" TEST.FEAT_NORM "('no')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('your path to trained checkpoints')"
```
2. Test with Cosine distance using feature after BN without re-ranking,.
```bash
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('market1501')" TEST.NECK_FEAT "('after')" TEST.FEAT_NORM "('yes')" TEST.WEIGHT "('your path to trained checkpoints')"
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('market1501')" TEST.NECK_FEAT "('after')" TEST.FEAT_NORM "('yes')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('your path to trained checkpoints')"
```
3. Test with Cosine distance using feature after BN with re-ranking
```bash
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('dukemtmc')" TEST.NECK_FEAT "('after')" TEST.FEAT_NORM "('yes')" TEST.RE_RANKING "('yes')" TEST.WEIGHT "('your path to trained checkpoints')"
python3 tools/test.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('dukemtmc')" TEST.NECK_FEAT "('after')" TEST.FEAT_NORM "('yes')" MODEL.PRETRAIN_CHOICE "('self')" TEST.RE_RANKING "('yes')" TEST.WEIGHT "('your path to trained checkpoints')"
```