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Glenn Jocher 2019-07-20 17:31:21 +02:00
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README.md
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@ -43,7 +43,7 @@ Python 3.7 or later with the following `pip3 install -U -r requirements.txt` pac
# Jupyter Notebook # Jupyter Notebook
A jupyter notebook with training, detection and testing examples is available at: A jupyter notebook with training, inference and testing examples is available at:
https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw
# Training # Training
@ -73,7 +73,7 @@ Reflection | 50% probability (horizontal-only)
H**S**V Saturation | +/- 50% H**S**V Saturation | +/- 50%
HS**V** Intensity | +/- 50% HS**V** Intensity | +/- 50%
<img src="https://user-images.githubusercontent.com/26833433/50525037-6cbcbc00-0ad9-11e9-8c38-9fd51af530e0.jpg"> <img src="https://user-images.githubusercontent.com/26833433/61579359-507b7d80-ab04-11e9-8a2a-bd6f59bbdfb4.jpg">
## Speed ## Speed
@ -100,7 +100,7 @@ GPUs | `batch_size` | batch time | epoch time | epoch cost
# Inference # Inference
Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder: `detect.py` runs inference on all images **and videos** in the `data/samples` folder:
**YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights` **YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.weights`
<img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="600"> <img src="https://user-images.githubusercontent.com/26833433/50524393-b0adc200-0ad5-11e9-9335-4774a1e52374.jpg" width="600">
@ -113,7 +113,7 @@ Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from
## Webcam ## Webcam
Run `detect.py` with `webcam=True` to show a live webcam feed. `detect.py` with `webcam=True` shows a live webcam feed.
# Pretrained Weights # Pretrained Weights
@ -136,9 +136,9 @@ Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'
# mAP # mAP
- `test.py --weights weights/yolov3.weights` to test official YOLOv3 weights. - `test.py --weights weights/yolov3.weights` tests official YOLOv3 weights.
- `test.py --weights weights/last.pt` to test most recent checkpoint. - `test.py --weights weights/last.pt` tests most recent checkpoint.
- `test.py --weights weights/best.pt` to test best checkpoint. - `test.py --weights weights/best.pt` tests best checkpoint.
- Compare to darknet published results https://arxiv.org/abs/1804.02767. - Compare to darknet published results https://arxiv.org/abs/1804.02767.
<!--- <!---
@ -170,48 +170,45 @@ YOLOv3-608 | 57.9 (58.2) | 57.9
`YOLOv3-spp 608` | 60.7 | 60.6 `YOLOv3-spp 608` | 60.7 | 60.6
``` bash ``` bash
git clone https://github.com/ultralytics/yolov3 # install pycocotools
# bash yolov3/data/get_coco_dataset.sh
git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3 git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
cd yolov3 cd yolov3
python3 test.py --save-json --img-size 416 python3 test.py --save-json --img-size 608
Namespace(batch_size=32, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB) Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP F1 Class Images Targets P R mAP F1: 100% 313/313 [07:40<00:00, 2.34s/it]
Calculating mAP: 100%|█████████████████████████████████████████| 157/157 [05:59<00:00, 1.71s/it] all 5e+03 3.58e+04 0.117 0.788 0.595 0.199
all 5e+03 3.58e+04 0.109 0.773 0.57 0.186 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.335 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 <--
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.387
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.349 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.392
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.360 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.493 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.297
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.465
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.432 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.495
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.332
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620
python3 test.py --save-json --img-size 608 --batch-size 16 python3 test.py --save-json --img-size 416
Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights')
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB) Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)
Class Images Targets P R mAP F1 Class Images Targets P R mAP F1: 100% 313/313 [07:01<00:00, 1.41s/it]
Computing mAP: 100%|█████████████████████████████████████████| 313/313 [06:11<00:00, 1.01it/s] all 5e+03 3.58e+04 0.105 0.746 0.554 0.18
all 5e+03 3.58e+04 0.12 0.81 0.611 0.203 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.336
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 <--
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.350
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.386 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.361
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.464 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.494 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
``` ```
# Citation # Citation

4
test.py
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@ -21,6 +21,7 @@ def test(cfg,
# Initialize/load model and set device # Initialize/load model and set device
if model is None: if model is None:
device = torch_utils.select_device() device = torch_utils.select_device()
verbose = False
# Initialize model # Initialize model
model = Darknet(cfg, img_size).to(device) model = Darknet(cfg, img_size).to(device)
@ -35,6 +36,7 @@ def test(cfg,
model = nn.DataParallel(model) model = nn.DataParallel(model)
else: else:
device = next(model.parameters()).device # get model device device = next(model.parameters()).device # get model device
verbose = True
# Configure run # Configure run
data = parse_data_cfg(data) data = parse_data_cfg(data)
@ -156,7 +158,7 @@ def test(cfg,
print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1)) print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))
# Print results per class # Print results per class
if nc > 1 and len(stats): if verbose and nc > 1 and len(stats):
for i, c in enumerate(ap_class): for i, c in enumerate(ap_class):
print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i])) print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i]))