pseudo-3d-pytorch
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qijiezhao
About the accuracy
Hi，I used your code to train the P3D199 on UCF101，with the pretrained weight you supported. My experiment top-1 result is 81%, which is far from the paper 93%, could you support the details about your training? Mine: Batch size:32 Learning rate: 0.001, divided by 10 each 10 epoch training data transformation：RandomResizeCrop(160), RandomHorizontalFlip(0.5), Totensor(), Normalize() val data transformation: Resize(182,242), CenterCrop(160), Totensor(), Normalize() Looking forward to your reply
Hi~ I have the same issue. On training set, the accuracy is 89@Top1, 98@Top5. But on test set, it is 42@Top1, 68@Top5.
The parameters: BS: 64 (on 4 GTX1080) LR: 0.05, decay by 0.1 every 40 epoch Training: Resize(182,242)-> RandomCrop(160)->RandomHorizontalFlip(0.5)->ToTensor() Test: Resize(182,242)-> CenterCrop(160)->ToTensor(). The final result is the average of 20 clips of each video.
I have debug for weeks.... Can you help me?
The full code/parameter/instruction are located in this repo: https://github.com/jinyu121/video_experiment
hi，@jinyu121 , maybe you can have a try with my parameters, especially the base LR and Droupout( 0.8). Besides, i think the test data augmentation should not do RandomHorizontalFlip.
Hi, @bbp94 , could you share your parameters about slow_rate, lr_mult and decay_mult in p3d_model.py? Thanks~
UPDATE: With pre-trained model, I can get 89.8@Top1, 98.26@Top5. But I can not get such result from scratch. Can anyone share your parameters? THX.
@jinyu121 Some of my parameters: self.optimizer = optim.SGD(params=params, lr=0.001, momentum=0.9, weight_decay=0.0001) self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=20, gamma=0.1) and I train the model with pre_trained weight. On training set, I got 90@Top1. On test set, got 81@Top1.
UPDATE: With pre-trained model, I can get 89.8@Top1, 98.26@Top5. But I can not get such result from scratch. Can anyone share your parameters? THX.
Could you tell me whether your result 89.8@Top1 is on test set? If it was, could you please share your parameters?
{
"net": {
"name": "p3d",
"input_size": 160
},
"data": {
"train": {
"annotation": "data/ucf101-train.json", // Which is converted from trainlist01.txt by my video2frame tool
"dataset": "data/ucf101-train",
"n_frames": 16,
"n_clips": 1,
"flip": 0.5,
"drop_last_iter": true
},
"val": {
"annotation": "data/ucf101-test.json", // which is converted from test01.txt by the video2frame tool
"dataset": "data/ucf101-test",
"n_frames": 16,
"n_clips": 20
},
"num_workers": 32
},
"train": {
"pre_train": "data/p3d_rgb_199.checkpoint.pth.tar",
"batch_size": 64,
"epoch": 100,
"learning_rate": 0.001,
"decay": {
"step": 40,
"scale": 0.1
},
"momentum": 0.9,
"weight_decay": 0.00001,
"dropout": 0.5,
"show_freq": 20
},
"val": {
"batch_size": 1,
"show_freq": 100,
"val_freq": 10,
"feature_mean": false // Which means we average the result after cls layer. If true, average the feature before the cls layer
}
}
Under this setting, I got 89.8@Top1, 98.26@Top5 on testlist01.txt.
@jinyu121 Thank you for your help. But I can not achieve your result with your parameters, only got 82.7@Top1.
your data processing is still as follow?
Training: Resize(182,242)-> RandomCrop(160)->RandomHorizontalFlip(0.5)->ToTensor() Test: Resize(182,242)-> CenterCrop(160)->ToTensor().
The repo you have provided is invalid，could you please provide new one? or provide your training log?
Due to some personal code are added, I have to make it personal. Sorry for that.
The frames are resized into 182x242 using the video2frame tool.
train_transform = transforms.Compose([
NumpyToFloat(), # From 0-255 to 0-1
NumpyRandomCrop(self.cfg.net.input_size), # 160x160
NumpyRandomFlip(-2, self.cfg.data.train.flip), # Random horizontal flip. -1 means vertical.
NumpyImageNetNorm(), # in RGB order
NumpyToTensor(is_copy=True)
])
val_transform = transforms.Compose([
NumpyToFloat(),
NumpyCenterCrop(self.cfg.net.input_size),
NumpyImageNetNorm(),
NumpyToTensor(is_copy=True)
])
There are NO TRICKS in these transformers.
Log... Well, I didn't log them in files, just tensorboard record. If you use the pre-trained model, the P3D net will raise its accuracy quickly. It will get ~80%@Top1 in just 5 epochs.
Good luck!
Why you summoned me? :D
Man I was confused about the Email for quite a while. Until I got the part with the @top1
@jinyu121
@jinyu121 Sorry for disturb you again. I used the default slow_rate, lr_mult and decay_mult in p3d_model.py, if you have changed they on UCF101 , can you please share the parameter？
