@@ -152,7 +152,7 @@ The following is the relationship between COCO mAP and FPS on Tesla V100 of repr
PaddleDetection is released under the [Apache 2.0 license](LICENSE).
## Updates
v0.3.0 was released at `05/2020`, add anchor-free, EfficientDet, YOLOv4, etc. Launched mobile and server-side practical and efficient multiple models, refactored predictive deployment functions, and improved ease of use, fix many known bugs, etc.
v0.3.0 was released at `05/2020`, add anchor-free, EfficientDet, YOLOv4, etc. Launched mobile and server-side practical and efficient multiple models. For example, the YOLOv3-MobileNetv3 mobile side model is accelerated 3.5 times, the server side has optimized the two-stage model, and the speed and accuracy have high cost performance. We also refactored predictive deployment functions, and improved ease of use, fix many known bugs, etc.
Please refer to [版本更新文档](docs/CHANGELOG.md) for details.
| -c | ALL | Select config file | None | **The description of configure can refer to [CONFIG.md](../advanced_tutorials/CONFIG.md)** |
| -c | ALL | Select config file | None | **The description of configure can refer to [CONFIG.md](../advanced_tutorials/config_doc/CONFIG.md)** |
| -o | ALL | Set parameters in configure file | None | `-o` has higher priority to file configured by `-c`. Such as `-o use_gpu=False max_iter=10000` |
| -r/--resume_checkpoint | train | Checkpoint path for resuming training | None | `-r output/faster_rcnn_r50_1x/10000` |
| --eval | train | Whether to perform evaluation in training | False | |
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@@ -100,7 +100,7 @@ list below can be viewed by `--help`
##### NOTES
-`CUDA_VISIBLE_DEVICES` can specify different gpu numbers. Such as: `export CUDA_VISIBLE_DEVICES=0,1,2,3`. GPU calculation rules can refer [FAQ](./FAQ.md)
-`CUDA_VISIBLE_DEVICES` can specify different gpu numbers. Such as: `export CUDA_VISIBLE_DEVICES=0,1,2,3`. GPU calculation rules can refer [FAQ](../FAQ.md)
- Dataset will be downloaded automatically and cached in `~/.cache/paddle/dataset` if not be found locally.
- Pretrained model is downloaded automatically and cached in `~/.cache/paddle/weights`.
- Checkpoints are saved in `output` by default, and can be revised from save_dir in configure files.
