mirror of https://github.com/open-mmlab/mmyolo.git
upload yolox_tiny and yolov5_n model
huanghaian
parent
01ab0619e3
commit
98712bb898
|
|
@ -177,7 +177,7 @@ def convert_model_info_to_pwc(model_infos):
|
|||
Metrics={'PQ': metric}))
|
||||
pwc_model_info['Results'] = results
|
||||
|
||||
link_string = 'https://download.openmmlab.com/mmdetection/v2.0/'
|
||||
link_string = 'https://download.openmmlab.com/mmyolo/v0/'
|
||||
link_string += '{}/{}'.format(model['config'].rstrip('.py'),
|
||||
osp.split(model['model_path'])[-1])
|
||||
pwc_model_info['Weights'] = link_string
|
||||
|
|
|
|||
|
|
@ -10,11 +10,12 @@ YOLOv5 is a family of object detection architectures and models pretrained on th
|
|||
|
||||
### COCO
|
||||
|
||||
| Backbone | size | SyncBN | AMP | Mem (GB) | box AP | Config | Download |
|
||||
| :------: | :--: | :----: | :-: | :------: | :----: | :-------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
||||
| YOLOv5-s | 640 | Yes | Yes | 2.7 | 37.7 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json) |
|
||||
| YOLOv5-m | 640 | Yes | Yes | 5.0 | 45.3 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944.log.json) |
|
||||
| YOLOv5-l | 640 | Yes | Yes | 8.1 | 48.8 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007-096ef0eb.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007.log.json) |
|
||||
| Backbone | size | SyncBN | AMP | Mem (GB) | box AP | Config | Download |
|
||||
| :------: | :--: | :----: | :-: | :------: | :----: | :-------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
||||
| YOLOv5-n | 640 | Yes | Yes | 1.5 | 28.0 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739-b804c1ad.pthh) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739.log.json) |
|
||||
| YOLOv5-s | 640 | Yes | Yes | 2.7 | 37.7 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json) |
|
||||
| YOLOv5-m | 640 | Yes | Yes | 5.0 | 45.3 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944.log.json) |
|
||||
| YOLOv5-l | 640 | Yes | Yes | 8.1 | 48.8 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007-096ef0eb.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007.log.json) |
|
||||
|
||||
**Note**:
|
||||
|
||||
|
|
|
|||
|
|
@ -17,6 +17,18 @@ Collections:
|
|||
Version: v0.1.0
|
||||
|
||||
Models:
|
||||
- Name: yolov5_n-v61_syncbn_fast_8xb16-300e_coco
|
||||
In Collection: YOLOv5
|
||||
Config: configs/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py
|
||||
Metadata:
|
||||
Training Memory (GB): 1.5
|
||||
Epochs: 300
|
||||
Results:
|
||||
- Task: Object Detection
|
||||
Dataset: COCO
|
||||
Metrics:
|
||||
box AP: 28.0
|
||||
Weights: https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739-b804c1ad.pth
|
||||
- Name: yolov5_s-v61_syncbn_fast_8xb16-300e_coco
|
||||
In Collection: YOLOv5
|
||||
Config: configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py
|
||||
|
|
|
|||
|
|
@ -14,15 +14,15 @@ In this report, we present some experienced improvements to YOLO series, forming
|
|||
|
||||
## Results and Models
|
||||
|
||||
| Backbone | size | Mem (GB) | box AP | Config | Download |
|
||||
| :------: | :--: | :------: | :----: | :------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
||||
| YOLOX-s | 640 | 5.6 | 40.8 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolox/yolox_s_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738-d7e60cb2.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738.log.json) |
|
||||
| Backbone | size | Mem (GB) | box AP | Config | Download |
|
||||
| :--------: | :--: | :------: | :----: | :---------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
||||
| YOLOX-tiny | 640 | 2.8 | 32.7 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolox/yolox_tiny_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908-0e40a6fc.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908.log.json) |
|
||||
| YOLOX-s | 640 | 5.6 | 40.8 | [config](https://github.com/open-mmlab/mmyolo/tree/master/configs/yolox/yolox_s_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738-d7e60cb2.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738.log.json) |
|
||||
|
||||
**Note**:
|
||||
|
||||
1. The test score threshold is 0.001.
|
||||
2. Due to the need for pre-training weights, we cannot reproduce the performance of the `yolox-nano` model. Please refer to https://github.com/Megvii-BaseDetection/YOLOX/issues/674 for more information.
|
||||
3. We also trained the model by the official release of YOLOX based on [Megvii-BaseDetection/YOLOX#735](https://github.com/Megvii-BaseDetection/YOLOX/issues/735) with commit ID [38c633](https://github.com/Megvii-BaseDetection/YOLOX/tree/38c633bf176462ee42b110c70e4ffe17b5753208). We found that the best AP of `YOLOX-tiny`, `YOLOX-s`, `YOLOX-l`, and `YOLOX-x` is 31.8, 40.3, 49.2, and 50.9, respectively. The performance is consistent with that of our re-implementation (see Table above) but still has a gap (0.3~0.8 AP) in comparison with the reported performance in their [README](https://github.com/Megvii-BaseDetection/YOLOX/blob/38c633bf176462ee42b110c70e4ffe17b5753208/README.md#benchmark).
|
||||
|
||||
## Citation
|
||||
|
||||
|
|
|
|||
|
|
@ -20,6 +20,18 @@ Collections:
|
|||
|
||||
|
||||
Models:
|
||||
- Name: yolox_tiny_8xb8-300e_coco
|
||||
In Collection: YOLOX
|
||||
Config: configs/yolox/yolox_tiny_8xb8-300e_coco.py
|
||||
Metadata:
|
||||
Training Memory (GB): 2.8
|
||||
Epochs: 300
|
||||
Results:
|
||||
- Task: Object Detection
|
||||
Dataset: COCO
|
||||
Metrics:
|
||||
box AP: 32.7
|
||||
Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908-0e40a6fc.pth
|
||||
- Name: yolox_s_8xb8-300e_coco
|
||||
In Collection: YOLOX
|
||||
Config: configs/yolox/yolox_s_8xb8-300e_coco.py
|
||||
|
|
|
|||
Loading…
Reference in New Issue