Safran/KPConv-PyTorch
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HuguesTHOMAS 2021-08-05 20:11:34 +00:00
parent aee5494f32
commit cf1f06381e
2 changed files with 4 additions and 2 deletions
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3
doc/pretrained_models_guide.md
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@ -11,7 +11,8 @@ We provide pretrained weights for S3DIS dataset. The raw weights come with a par
|:-------------|:-------------:|:-----|:-----:| |:-------------|:-------------:|:-----|:-----:|
| [Light_KPFCNN](https://drive.google.com/file/d/14sz0hdObzsf_exxInXdOIbnUTe0foOOz/view?usp=sharing) | rigid | A network with small `in_radius` for light GPU consumption (~8GB) | 65.4% | | [Light_KPFCNN](https://drive.google.com/file/d/14sz0hdObzsf_exxInXdOIbnUTe0foOOz/view?usp=sharing) | rigid | A network with small `in_radius` for light GPU consumption (~8GB) | 65.4% |
| [Heavy_KPFCNN](https://drive.google.com/file/d/1ySQq3SRBgk2Vt5Bvj-0N7jDPi0QTPZiZ/view?usp=sharing) | rigid | A network with better performances but needing bigger GPU (>18GB). | 66.4% | | [Heavy_KPFCNN](https://drive.google.com/file/d/1ySQq3SRBgk2Vt5Bvj-0N7jDPi0QTPZiZ/view?usp=sharing) | rigid | A network with better performances but needing bigger GPU (>18GB). | 66.4% |
| [Deform_KPFCNN](https://drive.google.com/file/d/1ObGr2Srfj0f7Bd3bBbuQzxtjf0ULbpSA/view?usp=sharing) | rigid | Deformable convolution network needing big GPU (>20GB). | 67.3% | | [Deform_KPFCNN](https://drive.google.com/file/d/1ObGr2Srfj0f7Bd3bBbuQzxtjf0ULbpSA/view?usp=sharing) | deform | Deformable convolution network needing big GPU (>20GB). | 67.3% |
| [Deform_Light_KPFCNN](https://drive.google.com/file/d/1gZfv6q6lUT9STFh7Fk4qVa5IVTgwmWIr/view?usp=sharing) | deform | Lighter version of the deformable architecture (~8GB). | 66.7% |

3
plot_convergence.py
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@ -725,7 +725,8 @@ def experiment_name_1():
# Give names to the logs (for plot legends) # Give names to the logs (for plot legends)
logs_names = ['name_log_1', logs_names = ['name_log_1',
'name_log_2', 'name_log_2',
'name_log_3'] 'name_log_3',
'name_log_4']
# safe check log names # safe check log names
logs_names = np.array(logs_names[:len(logs)]) logs_names = np.array(logs_names[:len(logs)])