qingzhouzhen opened a new pull request #7786: Pvanet:Deep but Lightweight Neural Neural Networks for Real-time Object Detection URL: https://github.com/apache/incubator-mxnet/pull/7786 article adress : [Pvanet:Deep but Lightweight Neural Neural Networks for Real-time Object Detection](https://arxiv.org/abs/1608.08021) Result of classification network: `INFO:root:Epoch[82] Batch [2000] Speed: 586.96 samples/sec accuracy=0.668164 top_k_accuracy_5=0.874805 INFO:root:Epoch[82] Batch [2050] Speed: 586.14 samples/sec accuracy=0.664766 top_k_accuracy_5=0.876250 INFO:root:Epoch[82] Batch [2100] Speed: 589.28 samples/sec accuracy=0.668438 top_k_accuracy_5=0.870938 INFO:root:Epoch[82] Batch [2150] Speed: 587.12 samples/sec accuracy=0.669766 top_k_accuracy_5=0.877266 INFO:root:Epoch[82] Batch [2200] Speed: 590.23 samples/sec accuracy=0.664297 top_k_accuracy_5=0.874922 INFO:root:Epoch[82] Batch [2250] Speed: 584.57 samples/sec accuracy=0.672266 top_k_accuracy_5=0.876836 INFO:root:Epoch[82] Batch [2300] Speed: 590.03 samples/sec accuracy=0.674492 top_k_accuracy_5=0.876172 INFO:root:Epoch[82] Batch [2350] Speed: 588.57 samples/sec accuracy=0.670820 top_k_accuracy_5=0.874453 INFO:root:Epoch[82] Batch [2400] Speed: 587.81 samples/sec accuracy=0.673672 top_k_accuracy_5=0.876094 INFO:root:Epoch[82] Batch [2450] Speed: 591.53 samples/sec accuracy=0.671406 top_k_accuracy_5=0.873828 INFO:root:Epoch[82] Batch [2500] Speed: 582.21 samples/sec accuracy=0.671992 top_k_accuracy_5=0.874805 INFO:root:Epoch[82] Train-accuracy=0.663086 INFO:root:Epoch[82] Train-top_k_accuracy_5=0.849609 INFO:root:Epoch[82] Time cost=2180.302 INFO:root:Saved checkpoint to "pvanet-models/pvanet-0083.params" INFO:root:Epoch[82] Validation-accuracy=0.640804 INFO:root:Epoch[82] Validation-top_k_accuracy_5=0.854931` Result of rpn and faster-rcnn training: `INFO:root:Epoch[9] Batch [9940] Speed: 2.57 samples/sec RPNAcc=0.991533 RPNLogLoss=0.023411 RPNL1Loss=0.322666 RCNNAcc=0.943286 RCNNLogLoss=0.157866RCNNL1Loss=0.834379 INFO:root:Epoch[9] Batch [9960] Speed: 2.66 samples/sec RPNAcc=0.991529 RPNLogLoss=0.023437 RPNL1Loss=0.322645 RCNNAcc=0.943291 RCNNLogLoss=0.157865RCNNL1Loss=0.834185 INFO:root:Epoch[9] Batch [9980] Speed: 2.48 samples/sec RPNAcc=0.991519 RPNLogLoss=0.023454 RPNL1Loss=0.322520 RCNNAcc=0.943320 RCNNLogLoss=0.157764RCNNL1Loss=0.833864 INFO:root:Epoch[9] Batch [10000] Speed: 2.63 samples/sec RPNAcc=0.991529 RPNLogLoss=0.023437 RPNL1Loss=0.322378 RCNNAcc=0.943317 RCNNLogLoss=0.157765 RCNNL1Loss=0.833716 INFO:root:Epoch[9] Batch [10020] Speed: 2.49 samples/sec RPNAcc=0.991519 RPNLogLoss=0.023461 RPNL1Loss=0.322260 RCNNAcc=0.943335 RCNNLogLoss=0.157711 RCNNL1Loss=0.833482 INFO:root:Epoch[9] Train-RPNAcc=0.991520 INFO:root:Epoch[9] Train-RPNLogLoss=0.023459 INFO:root:Epoch[9] Train-RPNL1Loss=0.322241 INFO:root:Epoch[9] Train-RCNNAcc=0.943339 INFO:root:Epoch[9] Train-RCNNLogLoss=0.157700 INFO:root:Epoch[9] Train-RCNNL1Loss=0.833458 INFO:root:Epoch[9] Time cost=3940.801 INFO:root:Saved checkpoint to "model/e2e-0010.params"` Result of the whole Object Detection network: `INFO:root:Writing dog VOC results file INFO:root:Writing horse VOC results file INFO:root:Writing motorbike VOC results file INFO:root:Writing person VOC results file INFO:root:Writing pottedplant VOC results file INFO:root:Writing sheep VOC results file INFO:root:Writing sofa VOC results file INFO:root:Writing train VOC results file INFO:root:Writing tvmonitor VOC results file INFO:root:VOC07 metric? Y INFO:root:AP for aeroplane = 0.4146 INFO:root:AP for bicycle = 0.5890 INFO:root:AP for bird = 0.4116 INFO:root:AP for boat = 0.2624 INFO:root:AP for bottle = 0.2624 INFO:root:AP for bus = 0.6334 INFO:root:AP for car = 0.5050 INFO:root:AP for cat = 0.7120 INFO:root:AP for chair = 0.2914 INFO:root:AP for cow = 0.4002 INFO:root:AP for diningtable = 0.5540 INFO:root:AP for dog = 0.6645 INFO:root:AP for horse = 0.7407 INFO:root:AP for motorbike = 0.5743 INFO:root:AP for person = 0.5975 INFO:root:AP for pottedplant = 0.2069 INFO:root:AP for sheep = 0.3671 INFO:root:AP for sofa = 0.5193 INFO:root:AP for train = 0.6205 INFO:root:AP for tvmonitor = 0.4492 INFO:root:Mean AP = 0.4888` Mark: 1 Currently, the classification network result is 64%(70.6% as article), result of object detection is poor than article's, I will improve it in the days ahead 2 There has no 4 contributors as show, actually, it is just me , but I used different computers and proxy, next time I will pay attention to it ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
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