Pvanet:Deep but Lightweight Neural Neural Networks for Real-time Object Detection #7786
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| import mxnet as mx | ||
| def get_symbol(num_classes, **kwargs): | ||
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| data = mx.sym.Variable(name='data') |
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why put the same file at two places?
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pvanet is a classification net, it is a little different with it's counterpart as in symbol_pvanet, And secondly, I cannot upload pre-trained model(pvanet.py) for it is too large, thus some one else could pretrain with pvanet.py on ImageNet
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How I do: |
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A nice feature! |
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Could you add pvanet to here: https://mxnet.incubator.apache.org/model_zoo/index.html @mli Can we give @qingzhouzhen access to the modelzoo hosting server? |
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OK, I'd like to, but I did not find where to push code in this link https://mxnet.incubator.apache.org/model_zoo/index.html @piiswrong |
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@winstywang Anyone from your side can review this? |
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@qingzhouzhen We decided to postpone merge and pretrained model upload until the result is reasonably close to the original paper. |
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Ok, I will work on this to improve the result, if have any suggestion to my problem(mAP is low), please inform me, I am new to deep learning and mxnet @piiswrong |
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I think the first part(image classfication net) is OK, currently the accuracy is 64.08%(70.6% as the result of the article), Can I push the first part only? @piiswrong |
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6% is a bit far... |
article adress : Pvanet:Deep but Lightweight Neural Neural Networks for Real-time Object Detection
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=0.854931Result 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:reading annotations for 4301/4952
INFO:root:reading annotations for 4401/4952
INFO:root:reading annotations for 4501/4952
INFO:root:reading annotations for 4601/4952
INFO:root:reading annotations for 4701/4952
INFO:root:reading annotations for 4801/4952
INFO:root:reading annotations for 4901/4952
INFO:root:saving annotations cache to data/cache/voc_2007_test_annotations.pkl
INFO:root:AP for aeroplane = 0.5641
INFO:root:AP for bicycle = 0.6811
INFO:root:AP for bird = 0.5965
INFO:root:AP for boat = 0.4123
INFO:root:AP for bottle = 0.3641
INFO:root:AP for bus = 0.7021
INFO:root:AP for car = 0.7254
INFO:root:AP for cat = 0.7801
INFO:root:AP for chair = 0.3233
INFO:root:AP for cow = 0.5812
INFO:root:AP for diningtable = 0.5688
INFO:root:AP for dog = 0.7511
INFO:root:AP for horse = 0.7783
INFO:root:AP for motorbike = 0.6953
INFO:root:AP for person = 0.6612
INFO:root:AP for pottedplant = 0.2878
INFO:root:AP for sheep = 0.5344
INFO:root:AP for sofa = 0.5900
INFO:root:AP for train = 0.6550
INFO:root:AP for tvmonitor = 0.6214
INFO:root:Mean AP = 0.5937
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