Training ssd inception_v3 using pretrained model from slim
up vote
2
down vote
favorite
I want to train ssd inception_v3 model using object detection API with pretrained model from SLIM (link)
I try to train object detection ssd inception v3 model using config:
model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 240
width: 320
}
}
feature_extractor {
type: "ssd_inception_v3"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.0299999993294
}
}
activation: RELU_6
batch_norm {
decay: 0.999700009823
center: true
scale: true
epsilon: 0.0010000000475
train: true
}
}
override_base_feature_extractor_hyperparams: true
}
...
I stopped procces after creating files model.ckpt-0.*, loaded and printed all tensors' names.
After that I loaded pretrained model from https://github.com/tensorflow/models/tree/master/research/slim using
reader = pywrap_tensorflow.NewCheckpointReader(os.path.join(folder, 'model.ckpt'))
var_to_shape_map = reader.get_variable_to_shape_map()
When I compared output I didn't see a lot layers for ssd incpetion v3 model. For example:
InceptionV3/AuxLogits/Conv2d_2a_5x5/weights
InceptionV3/Mixed_7c/Branch_3/Conv2d_0b_1x1/weight
In model from ssd_inception_v3 I saw mixed layers before 5c.
What are the differences of Feature Extractor in SSD_inception and SLIM models? In general, is it possible to load weights from SLIM for the classifier in the Object detection API for detection.
python tensorflow
asked Nov 7 at 4:05
Viktor Nikolaev
132
add a comment |
up vote
2
down vote
favorite
I want to train ssd inception_v3 model using object detection API with pretrained model from SLIM (link)
I try to train object detection ssd inception v3 model using config:
model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 240
width: 320
}
}
feature_extractor {
type: "ssd_inception_v3"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.0299999993294
}
}
activation: RELU_6
batch_norm {
decay: 0.999700009823
center: true
scale: true
epsilon: 0.0010000000475
train: true
}
}
override_base_feature_extractor_hyperparams: true
}
...
I stopped procces after creating files model.ckpt-0.*, loaded and printed all tensors' names.
After that I loaded pretrained model from https://github.com/tensorflow/models/tree/master/research/slim using
reader = pywrap_tensorflow.NewCheckpointReader(os.path.join(folder, 'model.ckpt'))
var_to_shape_map = reader.get_variable_to_shape_map()
When I compared output I didn't see a lot layers for ssd incpetion v3 model. For example:
InceptionV3/AuxLogits/Conv2d_2a_5x5/weights
InceptionV3/Mixed_7c/Branch_3/Conv2d_0b_1x1/weight
In model from ssd_inception_v3 I saw mixed layers before 5c.
What are the differences of Feature Extractor in SSD_inception and SLIM models? In general, is it possible to load weights from SLIM for the classifier in the Object detection API for detection.
python tensorflow
asked Nov 7 at 4:05
Viktor Nikolaev
132
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I want to train ssd inception_v3 model using object detection API with pretrained model from SLIM (link)
I try to train object detection ssd inception v3 model using config:
model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 240
width: 320
}
}
feature_extractor {
type: "ssd_inception_v3"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.0299999993294
}
}
activation: RELU_6
batch_norm {
decay: 0.999700009823
center: true
scale: true
epsilon: 0.0010000000475
train: true
}
}
override_base_feature_extractor_hyperparams: true
}
...
I stopped procces after creating files model.ckpt-0.*, loaded and printed all tensors' names.
After that I loaded pretrained model from https://github.com/tensorflow/models/tree/master/research/slim using
reader = pywrap_tensorflow.NewCheckpointReader(os.path.join(folder, 'model.ckpt'))
var_to_shape_map = reader.get_variable_to_shape_map()
When I compared output I didn't see a lot layers for ssd incpetion v3 model. For example:
InceptionV3/AuxLogits/Conv2d_2a_5x5/weights
InceptionV3/Mixed_7c/Branch_3/Conv2d_0b_1x1/weight
In model from ssd_inception_v3 I saw mixed layers before 5c.
What are the differences of Feature Extractor in SSD_inception and SLIM models? In general, is it possible to load weights from SLIM for the classifier in the Object detection API for detection.
python tensorflow
asked Nov 7 at 4:05
Viktor Nikolaev
132
I want to train ssd inception_v3 model using object detection API with pretrained model from SLIM (link)
I try to train object detection ssd inception v3 model using config:
model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 240
width: 320
}
}
feature_extractor {
type: "ssd_inception_v3"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.0299999993294
}
}
activation: RELU_6
batch_norm {
decay: 0.999700009823
center: true
scale: true
epsilon: 0.0010000000475
train: true
}
}
override_base_feature_extractor_hyperparams: true
}
...
I stopped procces after creating files model.ckpt-0.*, loaded and printed all tensors' names.
After that I loaded pretrained model from https://github.com/tensorflow/models/tree/master/research/slim using
reader = pywrap_tensorflow.NewCheckpointReader(os.path.join(folder, 'model.ckpt'))
var_to_shape_map = reader.get_variable_to_shape_map()
When I compared output I didn't see a lot layers for ssd incpetion v3 model. For example:
InceptionV3/AuxLogits/Conv2d_2a_5x5/weights
InceptionV3/Mixed_7c/Branch_3/Conv2d_0b_1x1/weight
In model from ssd_inception_v3 I saw mixed layers before 5c.
What are the differences of Feature Extractor in SSD_inception and SLIM models? In general, is it possible to load weights from SLIM for the classifier in the Object detection API for detection.
python tensorflow
python tensorflow
asked Nov 7 at 4:05
Viktor Nikolaev
132
asked Nov 7 at 4:05
Viktor Nikolaev
132
asked Nov 7 at 4:05
Viktor Nikolaev
132
asked Nov 7 at 4:05
Viktor Nikolaev
132
asked Nov 7 at 4:05
Viktor Nikolaev
132
132
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
up vote
0
down vote
accepted
You can see what happens in ssd_inception_v3_feature_extractor.
It uses the outputs of 'Mixed_5d', 'Mixed_6e', 'Mixed_7c' of InceptionV3 from inception_v3.inception_v3_base (Note the _base) and creating 3 additional feature maps with 512, 256, 128 number of channels (this happens in feature_map_generators.multi_resolution_feature_maps by feature_map_layout).
Loading the weights of the classifier for the detection model can be done by configuration:
train_config{
...
fine_tune_checkpoint: <path_to_inception_checkpoint>
fine_tune_checkpoint_type: "classification"
}
Of course that the checkpoint has to match the model you're using, e.g. ssd_inception_v3.
answered Nov 7 at 9:06
netanel-sam
2016
Thanks you for advices! When I start with the parameter fine_tune_checkpoint_type: "classification" I see no difference with the fact that I get without this parameter. If I understand correctly, I do not need the weight responsible for the classification becouse I want to train detector. If I use the pre-learned model, as I already said, warning drops out, although in a checkpoint these weights should be required, even considering your comment, layer 7c is used when building the feature. [InceptionV3/Mixed_7c_1_Conv2d_3_1x1_256/BatchNorm/beta] is not available in checkpoint ...
– Viktor Nikolaev
Nov 8 at 4:30
fine_tune_checkpoint_type: "classification"is indeed the default, so it is in fact not required in your case. This means that you're loading a checkpoint which was trained for classification, but you're fine-tuning it for detection, so most layers are restored (the feature extraction layers), except final ones which are responsible for the classification based on the feature map(s). Regarding the warning you're receiving, it's standard. See for example in here: github.com/tensorflow/models/issues/4862#issuecomment-408394971
– netanel-sam
Nov 8 at 8:02
thanks for the advices. They are really help a lot! Now I a little bit confused. I obtained very strange results. I compared three inicialization: 1. Start training from scratch. 2. Start training from a pre-trained model (from_detection_checkpoint: false because I do not have a checkpoint for the detector.). 3. Start training by loading weights manually (to do this, you first started manual training to create the 0th step, then interrupt training and manually replace weights from the pre-trained model) As a result of comparing weights, all three models have different weights.
– Viktor Nikolaev
Nov 12 at 5:40
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
You can see what happens in ssd_inception_v3_feature_extractor.
It uses the outputs of 'Mixed_5d', 'Mixed_6e', 'Mixed_7c' of InceptionV3 from inception_v3.inception_v3_base (Note the _base) and creating 3 additional feature maps with 512, 256, 128 number of channels (this happens in feature_map_generators.multi_resolution_feature_maps by feature_map_layout).
Loading the weights of the classifier for the detection model can be done by configuration:
train_config{
...
fine_tune_checkpoint: <path_to_inception_checkpoint>
fine_tune_checkpoint_type: "classification"
}
Of course that the checkpoint has to match the model you're using, e.g. ssd_inception_v3.
answered Nov 7 at 9:06
netanel-sam
2016
Thanks you for advices! When I start with the parameter fine_tune_checkpoint_type: "classification" I see no difference with the fact that I get without this parameter. If I understand correctly, I do not need the weight responsible for the classification becouse I want to train detector. If I use the pre-learned model, as I already said, warning drops out, although in a checkpoint these weights should be required, even considering your comment, layer 7c is used when building the feature. [InceptionV3/Mixed_7c_1_Conv2d_3_1x1_256/BatchNorm/beta] is not available in checkpoint ...
– Viktor Nikolaev
Nov 8 at 4:30
fine_tune_checkpoint_type: "classification"is indeed the default, so it is in fact not required in your case. This means that you're loading a checkpoint which was trained for classification, but you're fine-tuning it for detection, so most layers are restored (the feature extraction layers), except final ones which are responsible for the classification based on the feature map(s). Regarding the warning you're receiving, it's standard. See for example in here: github.com/tensorflow/models/issues/4862#issuecomment-408394971
– netanel-sam
Nov 8 at 8:02
thanks for the advices. They are really help a lot! Now I a little bit confused. I obtained very strange results. I compared three inicialization: 1. Start training from scratch. 2. Start training from a pre-trained model (from_detection_checkpoint: false because I do not have a checkpoint for the detector.). 3. Start training by loading weights manually (to do this, you first started manual training to create the 0th step, then interrupt training and manually replace weights from the pre-trained model) As a result of comparing weights, all three models have different weights.
– Viktor Nikolaev
Nov 12 at 5:40
add a comment |
up vote
0
down vote
accepted
You can see what happens in ssd_inception_v3_feature_extractor.
It uses the outputs of 'Mixed_5d', 'Mixed_6e', 'Mixed_7c' of InceptionV3 from inception_v3.inception_v3_base (Note the _base) and creating 3 additional feature maps with 512, 256, 128 number of channels (this happens in feature_map_generators.multi_resolution_feature_maps by feature_map_layout).
Loading the weights of the classifier for the detection model can be done by configuration:
train_config{
...
fine_tune_checkpoint: <path_to_inception_checkpoint>
fine_tune_checkpoint_type: "classification"
}
Of course that the checkpoint has to match the model you're using, e.g. ssd_inception_v3.
answered Nov 7 at 9:06
netanel-sam
2016
Thanks you for advices! When I start with the parameter fine_tune_checkpoint_type: "classification" I see no difference with the fact that I get without this parameter. If I understand correctly, I do not need the weight responsible for the classification becouse I want to train detector. If I use the pre-learned model, as I already said, warning drops out, although in a checkpoint these weights should be required, even considering your comment, layer 7c is used when building the feature. [InceptionV3/Mixed_7c_1_Conv2d_3_1x1_256/BatchNorm/beta] is not available in checkpoint ...
– Viktor Nikolaev
Nov 8 at 4:30
fine_tune_checkpoint_type: "classification"is indeed the default, so it is in fact not required in your case. This means that you're loading a checkpoint which was trained for classification, but you're fine-tuning it for detection, so most layers are restored (the feature extraction layers), except final ones which are responsible for the classification based on the feature map(s). Regarding the warning you're receiving, it's standard. See for example in here: github.com/tensorflow/models/issues/4862#issuecomment-408394971
– netanel-sam
Nov 8 at 8:02
thanks for the advices. They are really help a lot! Now I a little bit confused. I obtained very strange results. I compared three inicialization: 1. Start training from scratch. 2. Start training from a pre-trained model (from_detection_checkpoint: false because I do not have a checkpoint for the detector.). 3. Start training by loading weights manually (to do this, you first started manual training to create the 0th step, then interrupt training and manually replace weights from the pre-trained model) As a result of comparing weights, all three models have different weights.
– Viktor Nikolaev
Nov 12 at 5:40
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
You can see what happens in ssd_inception_v3_feature_extractor.
It uses the outputs of 'Mixed_5d', 'Mixed_6e', 'Mixed_7c' of InceptionV3 from inception_v3.inception_v3_base (Note the _base) and creating 3 additional feature maps with 512, 256, 128 number of channels (this happens in feature_map_generators.multi_resolution_feature_maps by feature_map_layout).
Loading the weights of the classifier for the detection model can be done by configuration:
train_config{
...
fine_tune_checkpoint: <path_to_inception_checkpoint>
fine_tune_checkpoint_type: "classification"
}
Of course that the checkpoint has to match the model you're using, e.g. ssd_inception_v3.
answered Nov 7 at 9:06
netanel-sam
2016
You can see what happens in ssd_inception_v3_feature_extractor.
It uses the outputs of 'Mixed_5d', 'Mixed_6e', 'Mixed_7c' of InceptionV3 from inception_v3.inception_v3_base (Note the _base) and creating 3 additional feature maps with 512, 256, 128 number of channels (this happens in feature_map_generators.multi_resolution_feature_maps by feature_map_layout).
Loading the weights of the classifier for the detection model can be done by configuration:
train_config{
...
fine_tune_checkpoint: <path_to_inception_checkpoint>
fine_tune_checkpoint_type: "classification"
}
Of course that the checkpoint has to match the model you're using, e.g. ssd_inception_v3.
answered Nov 7 at 9:06
netanel-sam
2016
answered Nov 7 at 9:06
netanel-sam
2016
answered Nov 7 at 9:06
netanel-sam
2016
answered Nov 7 at 9:06
netanel-sam
2016
2016
Thanks you for advices! When I start with the parameter fine_tune_checkpoint_type: "classification" I see no difference with the fact that I get without this parameter. If I understand correctly, I do not need the weight responsible for the classification becouse I want to train detector. If I use the pre-learned model, as I already said, warning drops out, although in a checkpoint these weights should be required, even considering your comment, layer 7c is used when building the feature. [InceptionV3/Mixed_7c_1_Conv2d_3_1x1_256/BatchNorm/beta] is not available in checkpoint ...
– Viktor Nikolaev
Nov 8 at 4:30
fine_tune_checkpoint_type: "classification"is indeed the default, so it is in fact not required in your case. This means that you're loading a checkpoint which was trained for classification, but you're fine-tuning it for detection, so most layers are restored (the feature extraction layers), except final ones which are responsible for the classification based on the feature map(s). Regarding the warning you're receiving, it's standard. See for example in here: github.com/tensorflow/models/issues/4862#issuecomment-408394971
– netanel-sam
Nov 8 at 8:02
thanks for the advices. They are really help a lot! Now I a little bit confused. I obtained very strange results. I compared three inicialization: 1. Start training from scratch. 2. Start training from a pre-trained model (from_detection_checkpoint: false because I do not have a checkpoint for the detector.). 3. Start training by loading weights manually (to do this, you first started manual training to create the 0th step, then interrupt training and manually replace weights from the pre-trained model) As a result of comparing weights, all three models have different weights.
– Viktor Nikolaev
Nov 12 at 5:40
add a comment |
Thanks you for advices! When I start with the parameter fine_tune_checkpoint_type: "classification" I see no difference with the fact that I get without this parameter. If I understand correctly, I do not need the weight responsible for the classification becouse I want to train detector. If I use the pre-learned model, as I already said, warning drops out, although in a checkpoint these weights should be required, even considering your comment, layer 7c is used when building the feature. [InceptionV3/Mixed_7c_1_Conv2d_3_1x1_256/BatchNorm/beta] is not available in checkpoint ...
– Viktor Nikolaev
Nov 8 at 4:30
fine_tune_checkpoint_type: "classification"is indeed the default, so it is in fact not required in your case. This means that you're loading a checkpoint which was trained for classification, but you're fine-tuning it for detection, so most layers are restored (the feature extraction layers), except final ones which are responsible for the classification based on the feature map(s). Regarding the warning you're receiving, it's standard. See for example in here: github.com/tensorflow/models/issues/4862#issuecomment-408394971
– netanel-sam
Nov 8 at 8:02
thanks for the advices. They are really help a lot! Now I a little bit confused. I obtained very strange results. I compared three inicialization: 1. Start training from scratch. 2. Start training from a pre-trained model (from_detection_checkpoint: false because I do not have a checkpoint for the detector.). 3. Start training by loading weights manually (to do this, you first started manual training to create the 0th step, then interrupt training and manually replace weights from the pre-trained model) As a result of comparing weights, all three models have different weights.
– Viktor Nikolaev
Nov 12 at 5:40
Thanks you for advices! When I start with the parameter fine_tune_checkpoint_type: "classification" I see no difference with the fact that I get without this parameter. If I understand correctly, I do not need the weight responsible for the classification becouse I want to train detector. If I use the pre-learned model, as I already said, warning drops out, although in a checkpoint these weights should be required, even considering your comment, layer 7c is used when building the feature. [InceptionV3/Mixed_7c_1_Conv2d_3_1x1_256/BatchNorm/beta] is not available in checkpoint ...
– Viktor Nikolaev
Nov 8 at 4:30
Thanks you for advices! When I start with the parameter fine_tune_checkpoint_type: "classification" I see no difference with the fact that I get without this parameter. If I understand correctly, I do not need the weight responsible for the classification becouse I want to train detector. If I use the pre-learned model, as I already said, warning drops out, although in a checkpoint these weights should be required, even considering your comment, layer 7c is used when building the feature. [InceptionV3/Mixed_7c_1_Conv2d_3_1x1_256/BatchNorm/beta] is not available in checkpoint ...
– Viktor Nikolaev
Nov 8 at 4:30
fine_tune_checkpoint_type: "classification" is indeed the default, so it is in fact not required in your case. This means that you're loading a checkpoint which was trained for classification, but you're fine-tuning it for detection, so most layers are restored (the feature extraction layers), except final ones which are responsible for the classification based on the feature map(s). Regarding the warning you're receiving, it's standard. See for example in here: github.com/tensorflow/models/issues/4862#issuecomment-408394971– netanel-sam
Nov 8 at 8:02
fine_tune_checkpoint_type: "classification" is indeed the default, so it is in fact not required in your case. This means that you're loading a checkpoint which was trained for classification, but you're fine-tuning it for detection, so most layers are restored (the feature extraction layers), except final ones which are responsible for the classification based on the feature map(s). Regarding the warning you're receiving, it's standard. See for example in here: github.com/tensorflow/models/issues/4862#issuecomment-408394971– netanel-sam
Nov 8 at 8:02
thanks for the advices. They are really help a lot! Now I a little bit confused. I obtained very strange results. I compared three inicialization: 1. Start training from scratch. 2. Start training from a pre-trained model (from_detection_checkpoint: false because I do not have a checkpoint for the detector.). 3. Start training by loading weights manually (to do this, you first started manual training to create the 0th step, then interrupt training and manually replace weights from the pre-trained model) As a result of comparing weights, all three models have different weights.
– Viktor Nikolaev
Nov 12 at 5:40
thanks for the advices. They are really help a lot! Now I a little bit confused. I obtained very strange results. I compared three inicialization: 1. Start training from scratch. 2. Start training from a pre-trained model (from_detection_checkpoint: false because I do not have a checkpoint for the detector.). 3. Start training by loading weights manually (to do this, you first started manual training to create the 0th step, then interrupt training and manually replace weights from the pre-trained model) As a result of comparing weights, all three models have different weights.
– Viktor Nikolaev
Nov 12 at 5:40
add a comment |
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