├── .gitattributes ├── LICENSE ├── ONNX ├── OFMClassifier │ ├── LICENCE │ └── OFMClassifier.onnx ├── fastpose │ ├── fastpose_resnet152_duc_256_192.onnx │ ├── fastpose_resnet50_256_192.onnx │ └── fastpose_resnet50_256_192_tf.onnx └── openpifpaf_12a4 │ ├── openpifpaf_resnet50_321_193.onnx │ └── openpifpaf_resnet50_641_369.onnx ├── README.md ├── amd64 ├── OFMClassifier │ ├── LICENCE │ └── OFMClassifier.h5 ├── coco_yolo_v3 │ └── yolov3.weights ├── face_mask_classifier │ └── model.h5 ├── face_mask_detector_mobilenet_v1 │ └── mobilenet_face_mask_frozen_inference_graph.pb ├── fastpose_tf │ └── fastpose_tf.tar.gz ├── ped_faster_rcnn_resnet50 │ ├── frozen_inference_graph.pb │ ├── pipeline.config │ └── saved_model │ │ └── saved_model.pb ├── ped_ssd_mobilenet_v2 │ ├── checkpoint │ ├── frozen_inference_graph.pb │ ├── model.ckpt.data-00000-of-00001 │ ├── model.ckpt.index │ ├── model.ckpt.meta │ ├── pipeline.config │ └── saved_model │ │ └── saved_model.pb ├── ped_ssdlite_mobilenet_v2 │ ├── checkpoint │ ├── frozen_inference_graph.pb │ ├── model.ckpt.data-00000-of-00001 │ ├── model.ckpt.index │ ├── model.ckpt.meta │ ├── pipeline.config │ └── saved_model │ │ └── saved_model.pb ├── ped_ssdlite_mobilenet_v3 │ ├── checkpoint │ ├── frozen_inference_graph.pb │ ├── model.ckpt.data-00000-of-00001 │ ├── model.ckpt.index │ ├── model.ckpt.meta │ ├── pipeline.config │ └── saved_model │ │ └── saved_model.pb └── tinyface │ └── tiny_face_detector.pkl ├── edge-tpu ├── OFMClassifier │ ├── LICENCE │ └── OFMClassifier_edgetpu.tflite ├── efficientnet-edgetpu-L │ └── efficientnet-edgetpu-L_quant_edgetpu.tflite ├── efficientnet-edgetpu-M │ └── efficientnet-edgetpu-M_quant_edgetpu.tflite ├── efficientnet-edgetpu-S │ └── efficientnet-edgetpu-S_quant_edgetpu.tflite ├── face_mask_ssd_mobilenet_v1 │ └── mobilenet_v1_face_mask_quantized_edgetpu.tflite ├── inception_v1_224_quant_20181026 │ └── inception_v1_224_quant_edgetpu.tflite ├── inception_v2_224_quant_20181026 │ └── inception_v2_224_quant_edgetpu.tflite ├── inception_v3_quant │ └── inception_v3_quant_edgetpu.tflite ├── inception_v4_299_quant │ └── inception_v4_299_quant_edgetpu.tflite ├── mobilenet_ssd_v1 │ └── mobilenet_ssd_v1_coco_quant_postprocess_edgetpu.tflite ├── mobilenet_ssd_v2 │ └── mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite ├── mobilenet_ssd_v2_face │ └── mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite ├── mobilenet_v1_1.0_224 │ └── mobilenet_v1_1.0_224_quant_edgetpu.tflite ├── mobilenet_v2_1.0_224 │ └── mobilenet_v2_1.0_224_quant_edgetpu.tflite ├── mobilenet_v2_1.0_224_inat_bird │ └── mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite ├── mobilenet_v2_1.0_224_inat_insect │ └── mobilenet_v2_1.0_224_inat_insect_quant_edgetpu.tflite ├── mobilenet_v2_1.0_224_inat_plant │ └── mobilenet_v2_1.0_224_inat_plant_quant_edgetpu.tflite ├── pedestrian_ssd_mobilenet_v2 │ └── ped_ssd_mobilenet_v2_quantized_edgetpu.tflite └── pedestrian_ssdlite_mobilenet_v2 │ └── ped_ssdlite_mobilenet_v2_quantized_edgetpu.tflite ├── jetson-nano ├── OFMClassifier │ ├── LICENCE │ ├── OFMClassifier_nano.trt │ └── OFMClassifier_tx2.trt ├── face_mask_ssd_mobilenet_v1 │ └── TRT_face_mask_ssd_mobilenet_v1.bin ├── pedestrian_ssd_mobilenet_v2 │ └── TRT_ssd_mobilenet_v2_pedestrian.bin └── pedestrian_ssd_mobilenet_v2_softbio │ └── TRT_ssd_mobilenet_v2_pedestrian_softbio.bin └── jetson-tx2 └── pose-estimation-openpifpaf ├── resnet50-193-257.trt └── resnet50-321-481.trt /.gitattributes: -------------------------------------------------------------------------------- 1 | *.h5 filter=lfs diff=lfs merge=lfs -text 2 | *.pb filter=lfs diff=lfs merge=lfs -text 3 | *.tflite filter=lfs diff=lfs merge=lfs -text 4 | *.bin filter=lfs diff=lfs merge=lfs -text 5 | *.pkl filter=lfs diff=lfs merge=lfs -text 6 | *.onnx filter=lfs diff=lfs merge=lfs -text 7 | *.weights filter=lfs diff=lfs merge=lfs -text 8 | *.tar.gz filter=lfs diff=lfs merge=lfs -text 9 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | Apache License 3 | Version 2.0, January 2004 4 | http://www.apache.org/licenses/ 5 | 6 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 7 | 8 | 1. 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To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 2 | -------------------------------------------------------------------------------- /ONNX/OFMClassifier/OFMClassifier.onnx: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:fcaa21940f9d21db6cceb6d67b0c39632a4b5d41787ad8175d4ddffe4dfb1c48 3 | size 1190520 4 | -------------------------------------------------------------------------------- /ONNX/fastpose/fastpose_resnet152_duc_256_192.onnx: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:c57c9e239d94935fab38786d137416ce4b5fdfaaccd18dd79e327cb471b309b7 3 | size 333573478 4 | -------------------------------------------------------------------------------- /ONNX/fastpose/fastpose_resnet50_256_192.onnx: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:26ff5d73ca7aa195b917eb9eb338653c62f3d77733db5a9f68a9c8eb10c2a534 3 | size 162586814 4 | -------------------------------------------------------------------------------- /ONNX/fastpose/fastpose_resnet50_256_192_tf.onnx: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:e67e3e8a462b175400a259a756bb0bc466d33b2ccf85a14ed0262dbb9a5660f2 3 | size 184293167 4 | -------------------------------------------------------------------------------- /ONNX/openpifpaf_12a4/openpifpaf_resnet50_321_193.onnx: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:fd77ca91515c190f6505580eef5efd175afa4dfd44abb7d7060740df48d98d68 3 | size 110093795 4 | -------------------------------------------------------------------------------- /ONNX/openpifpaf_12a4/openpifpaf_resnet50_641_369.onnx: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:6164ecb4ae1ec6735e6292057a785ddfc0a5a918e875aff501d56afa7cdc72a8 3 | size 110205095 4 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | > **Galliot now hosts all Neuralet’s content and expertise gained in three years of work and completing high-quality applications, mainly in Computer Vision and Deep Learning. [More Info](https://galliot.us/blog/neuralet-migrated-to-galliot/)** 2 | 3 | # Neuralet # 4 | 5 | Neuralet is an open-source platform for edge deep learning models on GPU, TPU, and more. We hope to make it easier to start and evaluate various deep learning models on different edge devices. 6 | 7 | We currently provide models for [Coral Dev Board](https://coral.ai/products/dev-board/) and soon will release models for other edge devices such as [NVIDIA Jetson](https://developer.nvidia.com/embedded/jetson-nano-developer-kit) and more. 8 | 9 | ### How to use Neuralet? ### 10 | 11 | Neuralet is a set of docker containers that are packaged to run directly on the device. A separate docker container is built for each model on each device to make it very simple to get started with any model on any device. 12 | 13 | For documentation and more details view [Neuralet's main github repo](https://github.com/neuralet/neuralet). 14 | 15 | ### License ### 16 | This project is licensed under Apache-2.0 but each model may have its own license. Please refer to the License file for each model for more details. If you have any questions, please reach out to us at hello@neuralet.com 17 | 18 | -------------------------------------------------------------------------------- /amd64/OFMClassifier/LICENCE: -------------------------------------------------------------------------------- 1 | This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 2 | -------------------------------------------------------------------------------- /amd64/OFMClassifier/OFMClassifier.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/galliot-us/models/eb3cb3e7c16f3767c7938bad2cbb09daa3cbf8c4/amd64/OFMClassifier/OFMClassifier.h5 -------------------------------------------------------------------------------- /amd64/coco_yolo_v3/yolov3.weights: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:523e4e69e1d015393a1b0a441cef1d9c7659e3eb2d7e15f793f060a21b32f297 3 | size 248007048 4 | -------------------------------------------------------------------------------- /amd64/face_mask_classifier/model.h5: 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-------------------------------------------------------------------------------- /amd64/ped_faster_rcnn_resnet50/frozen_inference_graph.pb: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:dd81f2ac3799a16caa2b89fd8986bce4901cc9985b4ae2d3173aaf36c1dd4617 3 | size 113959750 4 | -------------------------------------------------------------------------------- /amd64/ped_faster_rcnn_resnet50/pipeline.config: -------------------------------------------------------------------------------- 1 | model { 2 | faster_rcnn { 3 | num_classes: 1 4 | image_resizer { 5 | keep_aspect_ratio_resizer { 6 | min_dimension: 600 7 | max_dimension: 1024 8 | } 9 | } 10 | feature_extractor { 11 | type: "faster_rcnn_resnet50" 12 | first_stage_features_stride: 16 13 | } 14 | first_stage_anchor_generator { 15 | grid_anchor_generator { 16 | height_stride: 16 17 | width_stride: 16 18 | scales: 0.25 19 | scales: 0.5 20 | scales: 1.0 21 | scales: 2.0 22 | aspect_ratios: 0.5 23 | aspect_ratios: 1.0 24 | aspect_ratios: 2.0 25 | } 26 | } 27 | first_stage_box_predictor_conv_hyperparams { 28 | op: CONV 29 | regularizer { 30 | l2_regularizer { 31 | weight: 0.0 32 | } 33 | } 34 | initializer { 35 | truncated_normal_initializer { 36 | stddev: 0.0099999998 37 | } 38 | } 39 | } 40 | first_stage_nms_score_threshold: 0.0 41 | first_stage_nms_iou_threshold: 0.69999999 42 | first_stage_max_proposals: 300 43 | first_stage_localization_loss_weight: 2.0 44 | first_stage_objectness_loss_weight: 1.0 45 | initial_crop_size: 14 46 | maxpool_kernel_size: 2 47 | maxpool_stride: 2 48 | second_stage_box_predictor { 49 | mask_rcnn_box_predictor { 50 | fc_hyperparams { 51 | op: FC 52 | regularizer { 53 | l2_regularizer { 54 | weight: 0.0 55 | } 56 | } 57 | initializer { 58 | variance_scaling_initializer { 59 | factor: 1.0 60 | uniform: true 61 | mode: FAN_AVG 62 | } 63 | } 64 | } 65 | use_dropout: false 66 | dropout_keep_probability: 1.0 67 | } 68 | } 69 | second_stage_post_processing { 70 | batch_non_max_suppression { 71 | score_threshold: 0.0 72 | iou_threshold: 0.60000002 73 | max_detections_per_class: 100 74 | max_total_detections: 300 75 | } 76 | score_converter: SOFTMAX 77 | } 78 | second_stage_localization_loss_weight: 2.0 79 | second_stage_classification_loss_weight: 1.0 80 | } 81 | } 82 | train_config { 83 | batch_size: 1 84 | data_augmentation_options { 85 | random_horizontal_flip { 86 | } 87 | } 88 | optimizer { 89 | momentum_optimizer { 90 | learning_rate { 91 | manual_step_learning_rate { 92 | initial_learning_rate: 0.00030000001 93 | schedule { 94 | step: 900000 95 | learning_rate: 2.9999999e-05 96 | } 97 | schedule { 98 | step: 1200000 99 | learning_rate: 3.0000001e-06 100 | } 101 | } 102 | } 103 | momentum_optimizer_value: 0.89999998 104 | } 105 | use_moving_average: false 106 | } 107 | gradient_clipping_by_norm: 10.0 108 | fine_tune_checkpoint: "/work/pedestrian_detection/faster_rcnn_resnet50_coco_2018_01_28/model.ckpt" 109 | from_detection_checkpoint: true 110 | num_steps: 200000 111 | } 112 | train_input_reader { 113 | label_map_path: "/work/pedestrian_detection/Pedestrian-Detection/annotations/label_map.pbtxt" 114 | tf_record_input_reader { 115 | input_path: "/work/pedestrian_detection/Pedestrian-Detection/train.record" 116 | } 117 | } 118 | eval_config { 119 | num_examples: 8000 120 | max_evals: 10 121 | use_moving_averages: false 122 | } 123 | eval_input_reader { 124 | label_map_path: "/work/pedestrian_detection/Pedestrian-Detection/annotations/label_map.pbtxt" 125 | shuffle: false 126 | num_readers: 1 127 | tf_record_input_reader { 128 | input_path: "/work/pedestrian_detection/Pedestrian-Detection/val.record" 129 | } 130 | } 131 | -------------------------------------------------------------------------------- /amd64/ped_faster_rcnn_resnet50/saved_model/saved_model.pb: 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-------------------------------------------------------------------------------- /amd64/ped_ssd_mobilenet_v2/pipeline.config: -------------------------------------------------------------------------------- 1 | model { 2 | ssd { 3 | num_classes: 1 4 | image_resizer { 5 | fixed_shape_resizer { 6 | height: 300 7 | width: 300 8 | } 9 | } 10 | feature_extractor { 11 | type: "ssd_mobilenet_v2" 12 | depth_multiplier: 1.0 13 | min_depth: 16 14 | conv_hyperparams { 15 | regularizer { 16 | l2_regularizer { 17 | weight: 3.9999999e-05 18 | } 19 | } 20 | initializer { 21 | truncated_normal_initializer { 22 | mean: 0.0 23 | stddev: 0.029999999 24 | } 25 | } 26 | activation: RELU_6 27 | batch_norm { 28 | decay: 0.99970001 29 | center: true 30 | scale: true 31 | epsilon: 0.001 32 | train: true 33 | } 34 | } 35 | } 36 | box_coder { 37 | faster_rcnn_box_coder { 38 | y_scale: 10.0 39 | x_scale: 10.0 40 | height_scale: 5.0 41 | width_scale: 5.0 42 | } 43 | } 44 | matcher { 45 | argmax_matcher { 46 | matched_threshold: 0.5 47 | unmatched_threshold: 0.5 48 | ignore_thresholds: false 49 | negatives_lower_than_unmatched: true 50 | force_match_for_each_row: true 51 | } 52 | } 53 | similarity_calculator { 54 | iou_similarity { 55 | } 56 | } 57 | box_predictor { 58 | convolutional_box_predictor { 59 | conv_hyperparams { 60 | regularizer { 61 | l2_regularizer { 62 | weight: 3.9999999e-05 63 | } 64 | } 65 | initializer { 66 | truncated_normal_initializer { 67 | mean: 0.0 68 | stddev: 0.029999999 69 | } 70 | } 71 | activation: RELU_6 72 | batch_norm { 73 | decay: 0.99970001 74 | center: true 75 | scale: true 76 | epsilon: 0.001 77 | train: true 78 | } 79 | } 80 | min_depth: 0 81 | max_depth: 0 82 | num_layers_before_predictor: 0 83 | use_dropout: false 84 | dropout_keep_probability: 0.80000001 85 | kernel_size: 1 86 | box_code_size: 4 87 | apply_sigmoid_to_scores: false 88 | } 89 | } 90 | anchor_generator { 91 | ssd_anchor_generator { 92 | num_layers: 6 93 | min_scale: 0.2 94 | max_scale: 0.94999999 95 | aspect_ratios: 1.0 96 | aspect_ratios: 2.0 97 | aspect_ratios: 0.5 98 | aspect_ratios: 3.0 99 | aspect_ratios: 0.33329999 100 | } 101 | } 102 | post_processing { 103 | batch_non_max_suppression { 104 | score_threshold: 9.9999999e-09 105 | iou_threshold: 0.60000002 106 | max_detections_per_class: 30 107 | max_total_detections: 30 108 | } 109 | score_converter: SIGMOID 110 | } 111 | normalize_loss_by_num_matches: true 112 | loss { 113 | localization_loss { 114 | weighted_smooth_l1 { 115 | } 116 | } 117 | classification_loss { 118 | weighted_sigmoid { 119 | } 120 | } 121 | hard_example_miner { 122 | num_hard_examples: 3000 123 | iou_threshold: 0.99000001 124 | loss_type: CLASSIFICATION 125 | max_negatives_per_positive: 3 126 | min_negatives_per_image: 3 127 | } 128 | classification_weight: 1.0 129 | localization_weight: 1.0 130 | } 131 | } 132 | } 133 | train_config { 134 | batch_size: 24 135 | data_augmentation_options { 136 | random_horizontal_flip { 137 | } 138 | } 139 | data_augmentation_options { 140 | ssd_random_crop { 141 | } 142 | } 143 | optimizer { 144 | rms_prop_optimizer { 145 | learning_rate { 146 | exponential_decay_learning_rate { 147 | initial_learning_rate: 0.0040000002 148 | decay_steps: 800720 149 | decay_factor: 0.94999999 150 | } 151 | } 152 | momentum_optimizer_value: 0.89999998 153 | decay: 0.89999998 154 | epsilon: 1.0 155 | } 156 | } 157 | fine_tune_checkpoint: "/work/pedestrian_detection/ssd_mobilenet_v2_coco_2018_03_29/model.ckpt" 158 | num_steps: 200000 159 | fine_tune_checkpoint_type: "detection" 160 | } 161 | train_input_reader { 162 | label_map_path: "/work/pedestrian_detection/Pedestrian-Detection/annotations/label_map.pbtxt" 163 | tf_record_input_reader { 164 | input_path: "/work/pedestrian_detection/Pedestrian-Detection/train.record" 165 | } 166 | } 167 | eval_config { 168 | num_examples: 8000 169 | max_evals: 10 170 | use_moving_averages: false 171 | } 172 | eval_input_reader { 173 | label_map_path: 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17 | weight: 3.9999999e-05 18 | } 19 | } 20 | initializer { 21 | truncated_normal_initializer { 22 | mean: 0.0 23 | stddev: 0.029999999 24 | } 25 | } 26 | activation: RELU_6 27 | batch_norm { 28 | decay: 0.99970001 29 | center: true 30 | scale: true 31 | epsilon: 0.001 32 | train: true 33 | } 34 | } 35 | use_depthwise: true 36 | } 37 | box_coder { 38 | faster_rcnn_box_coder { 39 | y_scale: 10.0 40 | x_scale: 10.0 41 | height_scale: 5.0 42 | width_scale: 5.0 43 | } 44 | } 45 | matcher { 46 | argmax_matcher { 47 | matched_threshold: 0.5 48 | unmatched_threshold: 0.5 49 | ignore_thresholds: false 50 | negatives_lower_than_unmatched: true 51 | force_match_for_each_row: true 52 | } 53 | } 54 | similarity_calculator { 55 | iou_similarity { 56 | } 57 | } 58 | box_predictor { 59 | convolutional_box_predictor { 60 | conv_hyperparams { 61 | regularizer { 62 | l2_regularizer { 63 | weight: 3.9999999e-05 64 | } 65 | } 66 | initializer { 67 | truncated_normal_initializer { 68 | mean: 0.0 69 | stddev: 0.029999999 70 | } 71 | } 72 | activation: RELU_6 73 | batch_norm { 74 | decay: 0.99970001 75 | center: true 76 | scale: true 77 | epsilon: 0.001 78 | train: true 79 | } 80 | } 81 | min_depth: 0 82 | max_depth: 0 83 | num_layers_before_predictor: 0 84 | use_dropout: false 85 | dropout_keep_probability: 0.80000001 86 | kernel_size: 3 87 | box_code_size: 4 88 | apply_sigmoid_to_scores: false 89 | use_depthwise: true 90 | } 91 | } 92 | anchor_generator { 93 | ssd_anchor_generator { 94 | num_layers: 6 95 | min_scale: 0.2 96 | max_scale: 0.94999999 97 | aspect_ratios: 1.0 98 | aspect_ratios: 2.0 99 | aspect_ratios: 0.5 100 | aspect_ratios: 3.0 101 | aspect_ratios: 0.33329999 102 | } 103 | } 104 | post_processing { 105 | batch_non_max_suppression { 106 | score_threshold: 9.9999999e-09 107 | iou_threshold: 0.60000002 108 | max_detections_per_class: 50 109 | max_total_detections: 50 110 | } 111 | score_converter: SIGMOID 112 | } 113 | normalize_loss_by_num_matches: true 114 | loss { 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override_base_feature_extractor_hyperparams: true 37 | } 38 | box_coder { 39 | faster_rcnn_box_coder { 40 | y_scale: 10.0 41 | x_scale: 10.0 42 | height_scale: 5.0 43 | width_scale: 5.0 44 | } 45 | } 46 | matcher { 47 | argmax_matcher { 48 | matched_threshold: 0.5 49 | unmatched_threshold: 0.5 50 | ignore_thresholds: false 51 | negatives_lower_than_unmatched: true 52 | force_match_for_each_row: true 53 | use_matmul_gather: true 54 | } 55 | } 56 | similarity_calculator { 57 | iou_similarity { 58 | } 59 | } 60 | box_predictor { 61 | convolutional_box_predictor { 62 | conv_hyperparams { 63 | regularizer { 64 | l2_regularizer { 65 | weight: 3.9999999e-05 66 | } 67 | } 68 | initializer { 69 | random_normal_initializer { 70 | mean: 0.0 71 | stddev: 0.029999999 72 | } 73 | } 74 | activation: RELU_6 75 | batch_norm { 76 | decay: 0.97000003 77 | center: true 78 | scale: true 79 | epsilon: 0.001 80 | train: true 81 | } 82 | } 83 | min_depth: 0 84 | max_depth: 0 85 | num_layers_before_predictor: 0 86 | use_dropout: false 87 | dropout_keep_probability: 0.80000001 88 | kernel_size: 3 89 | box_code_size: 4 90 | apply_sigmoid_to_scores: false 91 | class_prediction_bias_init: -4.5999999 92 | use_depthwise: true 93 | } 94 | } 95 | anchor_generator { 96 | ssd_anchor_generator { 97 | num_layers: 6 98 | min_scale: 0.2 99 | max_scale: 0.94999999 100 | aspect_ratios: 1.0 101 | aspect_ratios: 2.0 102 | aspect_ratios: 0.5 103 | aspect_ratios: 3.0 104 | aspect_ratios: 0.33329999 105 | } 106 | } 107 | post_processing { 108 | batch_non_max_suppression { 109 | score_threshold: 9.9999999e-09 110 | iou_threshold: 0.60000002 111 | max_detections_per_class: 50 112 | max_total_detections: 50 113 | use_static_shapes: true 114 | } 115 | score_converter: SIGMOID 116 | } 117 | normalize_loss_by_num_matches: true 118 | loss { 119 | localization_loss { 120 | weighted_smooth_l1 { 121 | delta: 1.0 122 | } 123 | } 124 | classification_loss { 125 | weighted_sigmoid_focal { 126 | gamma: 2.0 127 | alpha: 0.75 128 | } 129 | } 130 | classification_weight: 1.0 131 | localization_weight: 1.0 132 | } 133 | encode_background_as_zeros: true 134 | normalize_loc_loss_by_codesize: true 135 | inplace_batchnorm_update: true 136 | freeze_batchnorm: false 137 | } 138 | } 139 | train_config { 140 | batch_size: 32 141 | data_augmentation_options { 142 | random_horizontal_flip { 143 | } 144 | } 145 | data_augmentation_options { 146 | ssd_random_crop { 147 | } 148 | } 149 | sync_replicas: true 150 | optimizer { 151 | momentum_optimizer { 152 | learning_rate { 153 | cosine_decay_learning_rate { 154 | learning_rate_base: 0.40000001 155 | total_steps: 400000 156 | warmup_learning_rate: 0.13333 157 | warmup_steps: 2000 158 | } 159 | } 160 | momentum_optimizer_value: 0.89999998 161 | } 162 | use_moving_average: false 163 | } 164 | fine_tune_checkpoint: "/work/pedestrian_detection/pretrained_models/ssd_mobilenet_v3_large_coco_2019_08_14/model.ckpt" 165 | num_steps: 400000 166 | startup_delay_steps: 0.0 167 | 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