on MacOS last stable version rectlabel vers 2.69 install

Main category /
Sub category / Developer Tools
Developer / Ryo Kawamura
Filesize / 16282
Title / RectLabel


https://macpkg.icu/?id=59522&s=4portfolio&kw=RectLabel+vers.2.69 RectLabel vers.2.69

Labelbox supports Polygon, Rectangle, Line, and Point segmentation. There’s also support for pixel-wise annotation with the Superpixel and Brush tools. This allows you to have super precision and speed when annotating complicates shapes like clouds and trees for instance.
When you export, check on "Convert all objects to rectangles" checkbox to convert all objects to rectangles.
If you uncheck the "Full path" button, each line includes the image name.
把 /Users/yourusername/models-master/research/object_detection/ 文件中的import matplotlib;('Agg')前移到最开始
When you click on the box or the label, four corner points would appear.
Coloring and drawing is a great way to express your emotions, relieve stress and sharpen creativity.

Best! version https://macpkg.icu/?id=59522&kw=ehl8ji-ver-1.67-rectlabel.tar.gz [13514 KB]
Best MacOS https://macpkg.icu/?id=59522&kw=PDcN88-RectLabel-ver-2.52.zip [13839 KB]


Serial key RectLabel
6QT0-GR8P-K7XF-LE19
88OR-A1FT-II3H-UBBQ
U77A-2JNO-J54T-EGZ7
6L41-6KBU-4EN2-U5CY
4DBM-5J7H-M8I6-KXDQ


- ISMN • To learn more about our general condittions of use : Training can be either done locally or on the cloud (AWS, Google Cloud etc.). If you have GPU (at least more than 2 GB) at home then you can do it locally otherwise I would recommend to go with the cloud. In my case, I went with Google Cloud this time and essentially followed all the steps described in their documentation. RectLabel for Mac Free Download GMT+8, 2019-05-07 10:43:23 # SSD with Mobilenet v1 configuration for MSCOCO Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that # should be configured. model { ssd { num_classes: 4 box_coder { faster_rcnn_box_coder { y_scale: 10.0 x_scale: 10.0 height_scale: 5.0 width_scale: 5.0 } } matcher { argmax_matcher { matched_threshold: 0.5 unmatched_threshold: 0.5 ignore_thresholds: false negatives_lower_than_unmatched: true force_match_for_each_row: true } } similarity_calculator { iou_similarity { } } anchor_generator { ssd_anchor_generator { num_layers: 6 min_scale: 0.2 max_scale: 0.95 aspect_ratios: 1.0 aspect_ratios: 2.0 aspect_ratios: 0.5 aspect_ratios: 3.0 aspect_ratios: 0.3333 } } image_resizer { fixed_shape_resizer { height: 300 width: 300 } } box_predictor { convolutional_box_predictor { min_depth: 0 max_depth: 0 num_layers_before_predictor: 0 use_dropout: false dropout_keep_probability: 0.8 kernel_size: 1 box_code_size: 4 apply_sigmoid_to_scores: false conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } } feature_extractor { type: 'ssd_mobilenet_v1' min_depth: 16 depth_multiplier: 1.0 conv_hyperparams { activation: RELU_6, regularizer { l2_regularizer { weight: 0.00004 } } initializer { truncated_normal_initializer { stddev: 0.03 mean: 0.0 } } batch_norm { train: true, scale: true, center: true, decay: 0.9997, epsilon: 0.001, } } } loss { classification_loss { weighted_sigmoid { } } localization_loss { weighted_smooth_l1 { } } hard_example_miner { num_hard_examples: 3000 iou_threshold: 0.99 loss_type: CLASSIFICATION max_negatives_per_positive: 3 min_negatives_per_image: 0 } classification_weight: 1.0 localization_weight: 1.0 } normalize_loss_by_num_matches: true post_processing { batch_non_max_suppression { score_threshold: 1e-8 iou_threshold: 0.6 max_detections_per_class: 100 max_total_detections: 100 } score_converter: SIGMOID } } } train_config: { batch_size: 24 optimizer { rms_prop_optimizer: { learning_rate: { exponential_decay_learning_rate { initial_learning_rate: 0.004 decay_steps: 800720 decay_factor: 0.95 } } momentum_optimizer_value: 0.9 decay: 0.9 epsilon: 1.0 } } fine_tune_checkpoint: "/Users/yourusername/PycharmProjects/TF_OD/models/model/" from_detection_checkpoint: true # Note: The below line limits the training process to 200K steps, which we # empirically found to be sufficient enough to train the pets dataset. This # effectively bypasses the learning rate schedule (the learning rate will # never decay). Remove the below line to train indefinitely. num_steps: 2000 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } } } train_input_reader: { tf_record_input_reader { input_path: "/Users/yourusername/PycharmProjects/TF_OD/data/" } label_map_path: "/Users/yourusername/PycharmProjects/TF_OD/data/" } eval_config: { num_examples: 56 # Note: The below line limits the evaluation process to 10 evaluations. # Remove the below line to evaluate indefinitely. # max_evals: 10 } eval_input_reader: { tf_record_input_reader { input_path: "/Users/yourusername/PycharmProjects/TF_OD/data/" } label_map_path: "/Users/yourusername/PycharmProjects/TF_OD/data/" shuffle: false num_readers: 1 } 6.1 训练之前： When you open the folder, we read all xml files in the folder at first and then start indexing them in an asynchronous way. Duplicate box

Torrent v.1.48 RectLabel uaJr 1.93 Recomended OS X
Latest v 2.53 RectLabel Eab4 1.35 Updated on iMac Pro
Free Jab 1.53 RectLabel 1.48 Updated on MacOS
Download FRwC3H ver. 2.70 RectLabel 2.11 Featured! version
Update 1i7 ver. 2.54 RectLabel 2.59 German version
Get Xmzxt ver 2.32 RectLabel 1.68 for 10.11.6

MacOS p9k4mV-Ultralingua-Spanish-English-Dictionary-v-7.5.app (11759 KB) 8.2
Languages Spanish vAL_AstroImager_vers.3.8.dmg (40123 KB) 3.1
for iMac LIRZ-SYNCOVERY-V-8.31.PKG (24053 KB) 8.34
on 10.14.3 X-Lite.ver..5.5.4.UfHt.pkg (39943 KB) 5.0.3