diff --git "a/source_code/sam3/examples/saco_gold_silver_eval_example.ipynb" "b/source_code/sam3/examples/saco_gold_silver_eval_example.ipynb" new file mode 100644--- /dev/null +++ "b/source_code/sam3/examples/saco_gold_silver_eval_example.ipynb" @@ -0,0 +1,2214 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "417b89e9", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) Meta Platforms, Inc. and affiliates." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0e0d2e74", + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "import json\n", + "import os\n", + "\n", + "import numpy as np\n", + "\n", + "from pycocotools.coco import COCO\n", + "from sam3.eval.cgf1_eval import CGF1Evaluator" + ] + }, + { + "cell_type": "markdown", + "id": "1ceba210-cb61-4998-a153-c13c612e6182", + "metadata": {}, + "source": [ + "# SA-Co/Gold" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b31ab5d3", + "metadata": {}, + "outputs": [], + "source": [ + "# Update to the directory where the GT annotation and PRED files exist\n", + "GT_DIR = # PUT YOUR PATH HERE\n", + "PRED_DIR = # PUT YOUR PATH HERE" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "25613248", + "metadata": {}, + "outputs": [], + "source": [ + "# Relative file names for GT files for 7 SA-Co/Gold subsets\n", + "saco_gold_gts = {\n", + " # MetaCLIP Captioner\n", + " \"metaclip_nps\": [\n", + " \"gold_metaclip_merged_a_release_test.json\",\n", + " \"gold_metaclip_merged_b_release_test.json\",\n", + " \"gold_metaclip_merged_c_release_test.json\",\n", + " ],\n", + " # SA-1B captioner\n", + " \"sa1b_nps\": [\n", + " \"gold_sa1b_merged_a_release_test.json\",\n", + " \"gold_sa1b_merged_b_release_test.json\",\n", + " \"gold_sa1b_merged_c_release_test.json\",\n", + " ],\n", + " # Crowded\n", + " \"crowded\": [\n", + " \"gold_crowded_merged_a_release_test.json\",\n", + " \"gold_crowded_merged_b_release_test.json\",\n", + " \"gold_crowded_merged_c_release_test.json\",\n", + " ],\n", + " # FG Food\n", + " \"fg_food\": [\n", + " \"gold_fg_food_merged_a_release_test.json\",\n", + " \"gold_fg_food_merged_b_release_test.json\",\n", + " \"gold_fg_food_merged_c_release_test.json\",\n", + " ],\n", + " # FG Sports\n", + " \"fg_sports_equipment\": [\n", + " \"gold_fg_sports_equipment_merged_a_release_test.json\",\n", + " \"gold_fg_sports_equipment_merged_b_release_test.json\",\n", + " \"gold_fg_sports_equipment_merged_c_release_test.json\",\n", + " ],\n", + " # Attributes\n", + " \"attributes\": [\n", + " \"gold_attributes_merged_a_release_test.json\",\n", + " \"gold_attributes_merged_b_release_test.json\",\n", + " \"gold_attributes_merged_c_release_test.json\",\n", + " ],\n", + " # Wiki common\n", + " \"wiki_common\": [\n", + " \"gold_wiki_common_merged_a_release_test.json\",\n", + " \"gold_wiki_common_merged_b_release_test.json\",\n", + " \"gold_wiki_common_merged_c_release_test.json\",\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "2703e989", + "metadata": {}, + "source": [ + "## Run offline evaluation for all 7 SA-Co/Gold subsets" + ] + }, + { + "cell_type": "markdown", + "id": "0314ddca-46e7-47fd-9f66-346c4f8baf96", + "metadata": {}, + "source": [ + "We assume the inference has already been run for all 7 datasets. With the default configurations, the predictions are dumped in a predictable folder" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cc28d29f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing subset: metaclip_nps\n", + "loading annotations into memory...\n", + "Done (t=0.28s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.26s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.27s)\n", + "creating index...\n", + "index created!\n", + "Loaded 26221 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33057/33057 [00:10<00:00, 3171.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.473\n", + " Average precision @[ IoU=0.50:0.95] = 0.609\n", + " Average recall @[ IoU=0.50:0.95] = 0.532\n", + " Average F1 @[ IoU=0.50:0.95] = 0.568\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.759\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.586\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.652\n", + " Average IL_precision = 0.916\n", + " Average IL_recall = 0.760\n", + " Average IL_F1 = 0.830\n", + " Average IL_FPR = 0.013\n", + " Average IL_MCC = 0.807\n", + " Average cgF1 @[ IoU=0.50 ] = 0.568\n", + " Average precision @[ IoU=0.50 ] = 0.732\n", + " Average recall @[ IoU=0.50 ] = 0.639\n", + " Average F1 @[ IoU=0.50 ] = 0.682\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.872\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.704\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.783\n", + " Average cgF1 @[ IoU=0.75 ] = 0.515\n", + " Average precision @[ IoU=0.75 ] = 0.664\n", + " Average recall @[ IoU=0.75 ] = 0.580\n", + " Average F1 @[ IoU=0.75 ] = 0.619\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.815\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.638\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.710\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.47258639187690543, 'cgF1_eval_segm_precision': 0.6094191949336503, 'cgF1_eval_segm_recall': 0.5321857924434281, 'cgF1_eval_segm_F1': 0.5681401781600861, 'cgF1_eval_segm_positive_macro_F1': 0.7594764589657407, 'cgF1_eval_segm_positive_micro_F1': 0.5858321816359353, 'cgF1_eval_segm_positive_micro_precision': 0.6516289867940714, 'cgF1_eval_segm_IL_precision': 0.9157483928108933, 'cgF1_eval_segm_IL_recall': 0.7596648256028061, 'cgF1_eval_segm_IL_F1': 0.8304356444812764, 'cgF1_eval_segm_IL_FPR': 0.013198590231849291, 'cgF1_eval_segm_IL_MCC': 0.8066924397311339, 'cgF1_eval_segm_cgF1@0.5': 0.5675504156248331, 'cgF1_eval_segm_precision@0.5': 0.7318687877530184, 'cgF1_eval_segm_recall@0.5': 0.6391170051959989, 'cgF1_eval_segm_F1@0.5': 0.6823056445199827, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8723414712702943, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7035524168467409, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7825597234127607, 'cgF1_eval_segm_cgF1@0.75': 0.5149222822957974, 'cgF1_eval_segm_precision@0.75': 0.6640084211254257, 'cgF1_eval_segm_recall@0.75': 0.5798567730119127, 'cgF1_eval_segm_F1@0.75': 0.6190362594405769, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8153807596867352, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6383130136529085, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.7099991898479673}\n", + "loading annotations into memory...\n", + "Done (t=0.22s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.23s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.21s)\n", + "creating index...\n", + "index created!\n", + "Loaded 26221 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 33057/33057 [00:08<00:00, 3762.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.500\n", + " Average precision @[ IoU=0.50:0.95] = 0.645\n", + " Average recall @[ IoU=0.50:0.95] = 0.563\n", + " Average F1 @[ IoU=0.50:0.95] = 0.601\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.813\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.620\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.690\n", + " Average IL_precision = 0.916\n", + " Average IL_recall = 0.760\n", + " Average IL_F1 = 0.831\n", + " Average IL_FPR = 0.013\n", + " Average IL_MCC = 0.807\n", + " Average cgF1 @[ IoU=0.50 ] = 0.571\n", + " Average precision @[ IoU=0.50 ] = 0.736\n", + " Average recall @[ IoU=0.50 ] = 0.642\n", + " Average F1 @[ IoU=0.50 ] = 0.686\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.878\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.707\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.787\n", + " Average cgF1 @[ IoU=0.75 ] = 0.530\n", + " Average precision @[ IoU=0.75 ] = 0.683\n", + " Average recall @[ IoU=0.75 ] = 0.596\n", + " Average F1 @[ IoU=0.75 ] = 0.636\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.842\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.656\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.731\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.5003430734030954, 'cgF1_eval_bbox_precision': 0.6454559084753544, 'cgF1_eval_bbox_recall': 0.5625467145593948, 'cgF1_eval_bbox_F1': 0.6011063859782098, 'cgF1_eval_bbox_positive_macro_F1': 0.8129272614186818, 'cgF1_eval_bbox_positive_micro_F1': 0.6198941576942177, 'cgF1_eval_bbox_positive_micro_precision': 0.6903838360796344, 'cgF1_eval_bbox_IL_precision': 0.916437098044895, 'cgF1_eval_bbox_IL_recall': 0.7598020554320895, 'cgF1_eval_bbox_IL_F1': 0.830800752892585, 'cgF1_eval_bbox_IL_FPR': 0.013092112361504437, 'cgF1_eval_bbox_IL_MCC': 0.8071427471815361, 'cgF1_eval_bbox_cgF1@0.5': 0.5705998939047701, 'cgF1_eval_bbox_precision@0.5': 0.7360818482088336, 'cgF1_eval_bbox_recall@0.5': 0.6415316986326687, 'cgF1_eval_bbox_F1@0.5': 0.6855123686584659, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8775997789207387, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7069380179618158, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7873179304150807, 'cgF1_eval_bbox_cgF1@0.75': 0.5297149268767383, 'cgF1_eval_bbox_precision@0.75': 0.6833433592887199, 'cgF1_eval_bbox_recall@0.75': 0.59556749986514, 'cgF1_eval_bbox_F1@0.75': 0.6363934960024782, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8423507795177905, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6562840696103028, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7309084997915145}\n", + "Processing subset: sa1b_nps\n", + "loading annotations into memory...\n", + "Done (t=0.42s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.42s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.44s)\n", + "creating index...\n", + "index created!\n", + "Loaded 50994 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12893/12893 [00:12<00:00, 1019.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.537\n", + " Average precision @[ IoU=0.50:0.95] = 0.613\n", + " Average recall @[ IoU=0.50:0.95] = 0.624\n", + " Average F1 @[ IoU=0.50:0.95] = 0.618\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.749\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.626\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.627\n", + " Average IL_precision = 0.957\n", + " Average IL_recall = 0.918\n", + " Average IL_F1 = 0.937\n", + " Average IL_FPR = 0.055\n", + " Average IL_MCC = 0.858\n", + " Average cgF1 @[ IoU=0.50 ] = 0.662\n", + " Average precision @[ IoU=0.50 ] = 0.755\n", + " Average recall @[ IoU=0.50 ] = 0.769\n", + " Average F1 @[ IoU=0.50 ] = 0.762\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.868\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.771\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.773\n", + " Average cgF1 @[ IoU=0.75 ] = 0.584\n", + " Average precision @[ IoU=0.75 ] = 0.666\n", + " Average recall @[ IoU=0.75 ] = 0.679\n", + " Average F1 @[ IoU=0.75 ] = 0.672\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.803\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.680\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.682\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.5368885175297502, 'cgF1_eval_segm_precision': 0.6126733177941744, 'cgF1_eval_segm_recall': 0.623866410830199, 'cgF1_eval_segm_F1': 0.6181692134008732, 'cgF1_eval_segm_positive_macro_F1': 0.7490377907736454, 'cgF1_eval_segm_positive_micro_F1': 0.6255271013781429, 'cgF1_eval_segm_positive_micro_precision': 0.6272971894411186, 'cgF1_eval_segm_IL_precision': 0.9571307299155163, 'cgF1_eval_segm_IL_recall': 0.9180350114102273, 'cgF1_eval_segm_IL_F1': 0.9371748135185498, 'cgF1_eval_segm_IL_FPR': 0.054851556832937805, 'cgF1_eval_segm_IL_MCC': 0.8582977721459122, 'cgF1_eval_segm_cgF1@0.5': 0.6617782611463625, 'cgF1_eval_segm_precision@0.5': 0.7551805547951372, 'cgF1_eval_segm_recall@0.5': 0.7689771507351261, 'cgF1_eval_segm_F1@0.5': 0.7619664171091266, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8675783572433642, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7710357437976187, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7732059252215057, 'cgF1_eval_segm_cgF1@0.75': 0.584028878830561, 'cgF1_eval_segm_precision@0.75': 0.6664635050035673, 'cgF1_eval_segm_recall@0.75': 0.6786393053852088, 'cgF1_eval_segm_F1@0.75': 0.6724463056533416, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8029021282211246, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6804501861520328, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6823712921904396}\n", + "loading annotations into memory...\n", + "Done (t=0.35s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.35s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.38s)\n", + "creating index...\n", + "index created!\n", + "Loaded 50994 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12893/12893 [00:07<00:00, 1636.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.554\n", + " Average precision @[ IoU=0.50:0.95] = 0.633\n", + " Average recall @[ IoU=0.50:0.95] = 0.642\n", + " Average F1 @[ IoU=0.50:0.95] = 0.637\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.786\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.645\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.648\n", + " Average IL_precision = 0.957\n", + " Average IL_recall = 0.918\n", + " Average IL_F1 = 0.937\n", + " Average IL_FPR = 0.055\n", + " Average IL_MCC = 0.858\n", + " Average cgF1 @[ IoU=0.50 ] = 0.656\n", + " Average precision @[ IoU=0.50 ] = 0.749\n", + " Average recall @[ IoU=0.50 ] = 0.760\n", + " Average F1 @[ IoU=0.50 ] = 0.755\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.863\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.764\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.768\n", + " Average cgF1 @[ IoU=0.75 ] = 0.589\n", + " Average precision @[ IoU=0.75 ] = 0.673\n", + " Average recall @[ IoU=0.75 ] = 0.683\n", + " Average F1 @[ IoU=0.75 ] = 0.678\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.817\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.686\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.690\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.5536231468412395, 'cgF1_eval_bbox_precision': 0.6328165449001351, 'cgF1_eval_bbox_recall': 0.6417390943649743, 'cgF1_eval_bbox_F1': 0.6371965950434251, 'cgF1_eval_bbox_positive_macro_F1': 0.785505899426481, 'cgF1_eval_bbox_positive_micro_F1': 0.6449030271971777, 'cgF1_eval_bbox_positive_micro_precision': 0.6481993029451039, 'cgF1_eval_bbox_IL_precision': 0.957272212654602, 'cgF1_eval_bbox_IL_recall': 0.9180461328469408, 'cgF1_eval_bbox_IL_F1': 0.9372484265333024, 'cgF1_eval_bbox_IL_FPR': 0.05468042729410095, 'cgF1_eval_bbox_IL_MCC': 0.8584595256861315, 'cgF1_eval_bbox_cgF1@0.5': 0.655649783867308, 'cgF1_eval_bbox_precision@0.5': 0.7494286121079209, 'cgF1_eval_bbox_recall@0.5': 0.7599953615328335, 'cgF1_eval_bbox_F1@0.5': 0.7546250062240573, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8630646236311263, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7637515389479476, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7676460229909632, 'cgF1_eval_bbox_cgF1@0.75': 0.5891939922914327, 'cgF1_eval_bbox_precision@0.75': 0.6734724947681224, 'cgF1_eval_bbox_recall@0.75': 0.6829682826014282, 'cgF1_eval_bbox_F1@0.75': 0.6781371571048335, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8172625456348024, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6863386970055625, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6898435339270219}\n", + "Processing subset: crowded\n", + "loading annotations into memory...\n", + "Done (t=0.42s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.42s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.42s)\n", + "creating index...\n", + "index created!\n", + "Loaded 82963 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20241/20241 [00:10<00:00, 2003.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.611\n", + " Average precision @[ IoU=0.50:0.95] = 0.643\n", + " Average recall @[ IoU=0.50:0.95] = 0.686\n", + " Average F1 @[ IoU=0.50:0.95] = 0.664\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.689\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.677\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.669\n", + " Average IL_precision = 0.933\n", + " Average IL_recall = 0.913\n", + " Average IL_F1 = 0.923\n", + " Average IL_FPR = 0.018\n", + " Average IL_MCC = 0.902\n", + " Average cgF1 @[ IoU=0.50 ] = 0.735\n", + " Average precision @[ IoU=0.50 ] = 0.773\n", + " Average recall @[ IoU=0.50 ] = 0.825\n", + " Average F1 @[ IoU=0.50 ] = 0.798\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.830\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.815\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.805\n", + " Average cgF1 @[ IoU=0.75 ] = 0.667\n", + " Average precision @[ IoU=0.75 ] = 0.702\n", + " Average recall @[ IoU=0.75 ] = 0.748\n", + " Average F1 @[ IoU=0.75 ] = 0.724\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.748\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.739\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.730\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.6107822436866207, 'cgF1_eval_segm_precision': 0.6429349935778358, 'cgF1_eval_segm_recall': 0.6856112974213837, 'cgF1_eval_segm_F1': 0.663537765323902, 'cgF1_eval_segm_positive_macro_F1': 0.6891461338399572, 'cgF1_eval_segm_positive_micro_F1': 0.6772901260675144, 'cgF1_eval_segm_positive_micro_precision': 0.6692661193229303, 'cgF1_eval_segm_IL_precision': 0.9330264669889466, 'cgF1_eval_segm_IL_recall': 0.9132687540181867, 'cgF1_eval_segm_IL_F1': 0.9230413942007347, 'cgF1_eval_segm_IL_FPR': 0.018415390455738805, 'cgF1_eval_segm_IL_MCC': 0.901802964754481, 'cgF1_eval_segm_cgF1@0.5': 0.7346796584523991, 'cgF1_eval_segm_precision@0.5': 0.77334498201075, 'cgF1_eval_segm_recall@0.5': 0.8246775518006043, 'cgF1_eval_segm_F1@0.5': 0.7981368549267828, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.829596777967627, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8146786905412517, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8050169926635622, 'cgF1_eval_segm_cgF1@0.75': 0.6666293625039935, 'cgF1_eval_segm_precision@0.75': 0.7017176741205416, 'cgF1_eval_segm_recall@0.75': 0.7482958149470458, 'cgF1_eval_segm_F1@0.75': 0.7242086950813854, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.748186632903701, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7392184197193072, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.7304562192291248}\n", + "loading annotations into memory...\n", + "Done (t=0.36s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.37s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.37s)\n", + "creating index...\n", + "index created!\n", + "Loaded 82963 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20241/20241 [00:07<00:00, 2785.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.617\n", + " Average precision @[ IoU=0.50:0.95] = 0.650\n", + " Average recall @[ IoU=0.50:0.95] = 0.692\n", + " Average F1 @[ IoU=0.50:0.95] = 0.670\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.731\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.684\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.677\n", + " Average IL_precision = 0.933\n", + " Average IL_recall = 0.913\n", + " Average IL_F1 = 0.923\n", + " Average IL_FPR = 0.018\n", + " Average IL_MCC = 0.902\n", + " Average cgF1 @[ IoU=0.50 ] = 0.730\n", + " Average precision @[ IoU=0.50 ] = 0.769\n", + " Average recall @[ IoU=0.50 ] = 0.818\n", + " Average F1 @[ IoU=0.50 ] = 0.793\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.829\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.809\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.801\n", + " Average cgF1 @[ IoU=0.75 ] = 0.668\n", + " Average precision @[ IoU=0.75 ] = 0.704\n", + " Average recall @[ IoU=0.75 ] = 0.749\n", + " Average F1 @[ IoU=0.75 ] = 0.726\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.776\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.741\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.733\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.6170650320984323, 'cgF1_eval_bbox_precision': 0.6504449386041414, 'cgF1_eval_bbox_recall': 0.691669313906474, 'cgF1_eval_bbox_F1': 0.6703740536459668, 'cgF1_eval_bbox_positive_macro_F1': 0.7305480221708001, 'cgF1_eval_bbox_positive_micro_F1': 0.6842570452919619, 'cgF1_eval_bbox_positive_micro_precision': 0.677099838003926, 'cgF1_eval_bbox_IL_precision': 0.9330264669889466, 'cgF1_eval_bbox_IL_recall': 0.9132687540181867, 'cgF1_eval_bbox_IL_F1': 0.9230413942007347, 'cgF1_eval_bbox_IL_FPR': 0.018415390455738805, 'cgF1_eval_bbox_IL_MCC': 0.901802964754481, 'cgF1_eval_bbox_cgF1@0.5': 0.7298487590689315, 'cgF1_eval_bbox_precision@0.5': 0.7693209759495023, 'cgF1_eval_bbox_recall@0.5': 0.818079563737977, 'cgF1_eval_bbox_F1@0.5': 0.7929014858831388, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8289958931744248, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.8093217560752147, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8008473542838228, 'cgF1_eval_bbox_cgF1@0.75': 0.6681534554056624, 'cgF1_eval_bbox_precision@0.75': 0.7042930379997401, 'cgF1_eval_bbox_recall@0.75': 0.7489302375506022, 'cgF1_eval_bbox_F1@0.75': 0.7258761503763029, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7757641647406367, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7409084706076228, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7331546048467947}\n", + "Processing subset: fg_food\n", + "loading annotations into memory...\n", + "Done (t=0.12s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.12s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.10s)\n", + "creating index...\n", + "index created!\n", + "Loaded 10846 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13794/13794 [00:03<00:00, 3963.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.534\n", + " Average precision @[ IoU=0.50:0.95] = 0.734\n", + " Average recall @[ IoU=0.50:0.95] = 0.583\n", + " Average F1 @[ IoU=0.50:0.95] = 0.650\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.825\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.673\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.795\n", + " Average IL_precision = 0.917\n", + " Average IL_recall = 0.713\n", + " Average IL_F1 = 0.802\n", + " Average IL_FPR = 0.006\n", + " Average IL_MCC = 0.794\n", + " Average cgF1 @[ IoU=0.50 ] = 0.582\n", + " Average precision @[ IoU=0.50 ] = 0.800\n", + " Average recall @[ IoU=0.50 ] = 0.635\n", + " Average F1 @[ IoU=0.50 ] = 0.708\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.885\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.733\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.866\n", + " Average cgF1 @[ IoU=0.75 ] = 0.561\n", + " Average precision @[ IoU=0.75 ] = 0.771\n", + " Average recall @[ IoU=0.75 ] = 0.612\n", + " Average F1 @[ IoU=0.75 ] = 0.682\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.854\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.706\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.835\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.5340544897290387, 'cgF1_eval_segm_precision': 0.734205779536348, 'cgF1_eval_segm_recall': 0.5831005937305522, 'cgF1_eval_segm_F1': 0.6499373841120774, 'cgF1_eval_segm_positive_macro_F1': 0.8250979043741161, 'cgF1_eval_segm_positive_micro_F1': 0.6727883081524807, 'cgF1_eval_segm_positive_micro_precision': 0.7952174681839976, 'cgF1_eval_segm_IL_precision': 0.9171210458919072, 'cgF1_eval_segm_IL_recall': 0.7133163691999013, 'cgF1_eval_segm_IL_F1': 0.8024804230776101, 'cgF1_eval_segm_IL_FPR': 0.006024573919459011, 'cgF1_eval_segm_IL_MCC': 0.7937927625341562, 'cgF1_eval_segm_cgF1@0.5': 0.5816253704026515, 'cgF1_eval_segm_precision@0.5': 0.7996003836243679, 'cgF1_eval_segm_recall@0.5': 0.6350364862736196, 'cgF1_eval_segm_F1@0.5': 0.7078307231011086, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8854304579823623, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.732716897727604, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8660462915809068, 'cgF1_eval_segm_cgF1@0.75': 0.5605505836352753, 'cgF1_eval_segm_precision@0.75': 0.7706293552321807, 'cgF1_eval_segm_recall@0.75': 0.6120279179303726, 'cgF1_eval_segm_F1@0.75': 0.6821828949054192, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8542637117087882, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7061674155931289, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8346678027555117}\n", + "loading annotations into memory...\n", + "Done (t=0.09s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.08s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.08s)\n", + "creating index...\n", + "index created!\n", + "Loaded 10846 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13794/13794 [00:03<00:00, 4490.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.538\n", + " Average precision @[ IoU=0.50:0.95] = 0.737\n", + " Average recall @[ IoU=0.50:0.95] = 0.588\n", + " Average F1 @[ IoU=0.50:0.95] = 0.654\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.859\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.676\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.797\n", + " Average IL_precision = 0.919\n", + " Average IL_recall = 0.714\n", + " Average IL_F1 = 0.804\n", + " Average IL_FPR = 0.006\n", + " Average IL_MCC = 0.795\n", + " Average cgF1 @[ IoU=0.50 ] = 0.583\n", + " Average precision @[ IoU=0.50 ] = 0.798\n", + " Average recall @[ IoU=0.50 ] = 0.637\n", + " Average F1 @[ IoU=0.50 ] = 0.708\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.898\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.733\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.864\n", + " Average cgF1 @[ IoU=0.75 ] = 0.560\n", + " Average precision @[ IoU=0.75 ] = 0.766\n", + " Average recall @[ IoU=0.75 ] = 0.612\n", + " Average F1 @[ IoU=0.75 ] = 0.680\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.875\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.704\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.829\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.5378561873516663, 'cgF1_eval_bbox_precision': 0.7365034817881422, 'cgF1_eval_bbox_recall': 0.5876295139864577, 'cgF1_eval_bbox_F1': 0.6536480883299698, 'cgF1_eval_bbox_positive_macro_F1': 0.8586355938093078, 'cgF1_eval_bbox_positive_micro_F1': 0.6764422338399003, 'cgF1_eval_bbox_positive_micro_precision': 0.7970161989834335, 'cgF1_eval_bbox_IL_precision': 0.9193020709713172, 'cgF1_eval_bbox_IL_recall': 0.7138018622237071, 'cgF1_eval_bbox_IL_F1': 0.8036220047681086, 'cgF1_eval_bbox_IL_FPR': 0.005866962657110365, 'cgF1_eval_bbox_IL_MCC': 0.7951250830369732, 'cgF1_eval_bbox_cgF1@0.5': 0.5827707578455898, 'cgF1_eval_bbox_precision@0.5': 0.7980019820579024, 'cgF1_eval_bbox_recall@0.5': 0.6366969450550463, 'cgF1_eval_bbox_F1@0.5': 0.7082322331345615, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8975145206561829, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7329296613555469, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8635675488958368, 'cgF1_eval_bbox_cgF1@0.75': 0.5597151953142597, 'cgF1_eval_bbox_precision@0.75': 0.7664335511202087, 'cgF1_eval_bbox_recall@0.75': 0.6115096347599293, 'cgF1_eval_bbox_F1@0.75': 0.6802130814411795, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8745268555395833, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7039335159399481, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.829405387472316}\n", + "Processing subset: fg_sports_equipment\n", + "loading annotations into memory...\n", + "Done (t=0.08s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.08s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.08s)\n", + "creating index...\n", + "index created!\n", + "Loaded 6562 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12107/12107 [00:03<00:00, 3306.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.655\n", + " Average precision @[ IoU=0.50:0.95] = 0.733\n", + " Average recall @[ IoU=0.50:0.95] = 0.701\n", + " Average F1 @[ IoU=0.50:0.95] = 0.717\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.840\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.738\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.778\n", + " Average IL_precision = 0.962\n", + " Average IL_recall = 0.850\n", + " Average IL_F1 = 0.903\n", + " Average IL_FPR = 0.006\n", + " Average IL_MCC = 0.888\n", + " Average cgF1 @[ IoU=0.50 ] = 0.737\n", + " Average precision @[ IoU=0.50 ] = 0.825\n", + " Average recall @[ IoU=0.50 ] = 0.789\n", + " Average F1 @[ IoU=0.50 ] = 0.807\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.920\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.830\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.875\n", + " Average cgF1 @[ IoU=0.75 ] = 0.697\n", + " Average precision @[ IoU=0.75 ] = 0.780\n", + " Average recall @[ IoU=0.75 ] = 0.746\n", + " Average F1 @[ IoU=0.75 ] = 0.763\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.879\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.785\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.827\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.6552238992458554, 'cgF1_eval_segm_precision': 0.7333019582948739, 'cgF1_eval_segm_recall': 0.7013045276359755, 'cgF1_eval_segm_F1': 0.7168964364723446, 'cgF1_eval_segm_positive_macro_F1': 0.8401797467726777, 'cgF1_eval_segm_positive_micro_F1': 0.7375018641548449, 'cgF1_eval_segm_positive_micro_precision': 0.7777500429595898, 'cgF1_eval_segm_IL_precision': 0.9624470012341326, 'cgF1_eval_segm_IL_recall': 0.8497326198664531, 'cgF1_eval_segm_IL_F1': 0.9025839944636483, 'cgF1_eval_segm_IL_FPR': 0.0060564618534671814, 'cgF1_eval_segm_IL_MCC': 0.8884369397448539, 'cgF1_eval_segm_cgF1@0.5': 0.7373951194278512, 'cgF1_eval_segm_precision@0.5': 0.8252586823786763, 'cgF1_eval_segm_recall@0.5': 0.7892487451810898, 'cgF1_eval_segm_F1@0.5': 0.8068021593354898, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.920431978172454, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8299915125541946, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8752805967752407, 'cgF1_eval_segm_cgF1@0.75': 0.6970450572765126, 'cgF1_eval_segm_precision@0.75': 0.7801034623682158, 'cgF1_eval_segm_recall@0.75': 0.7460638608622613, 'cgF1_eval_segm_F1@0.75': 0.7626540805113754, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8790410692537068, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7845746007327135, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8273883555153815}\n", + "loading annotations into memory...\n", + "Done (t=0.06s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.06s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.06s)\n", + "creating index...\n", + "index created!\n", + "Loaded 6562 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12107/12107 [00:03<00:00, 3661.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.681\n", + " Average precision @[ IoU=0.50:0.95] = 0.760\n", + " Average recall @[ IoU=0.50:0.95] = 0.730\n", + " Average F1 @[ IoU=0.50:0.95] = 0.745\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.879\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.766\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.806\n", + " Average IL_precision = 0.962\n", + " Average IL_recall = 0.850\n", + " Average IL_F1 = 0.903\n", + " Average IL_FPR = 0.006\n", + " Average IL_MCC = 0.888\n", + " Average cgF1 @[ IoU=0.50 ] = 0.735\n", + " Average precision @[ IoU=0.50 ] = 0.821\n", + " Average recall @[ IoU=0.50 ] = 0.788\n", + " Average F1 @[ IoU=0.50 ] = 0.804\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.921\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.827\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.870\n", + " Average cgF1 @[ IoU=0.75 ] = 0.711\n", + " Average precision @[ IoU=0.75 ] = 0.794\n", + " Average recall @[ IoU=0.75 ] = 0.762\n", + " Average F1 @[ IoU=0.75 ] = 0.777\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.901\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.800\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.842\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.6806904643208772, 'cgF1_eval_bbox_precision': 0.7600893518323295, 'cgF1_eval_bbox_recall': 0.7300429019642443, 'cgF1_eval_bbox_F1': 0.744713228137678, 'cgF1_eval_bbox_positive_macro_F1': 0.8793127218535274, 'cgF1_eval_bbox_positive_micro_F1': 0.766166324102149, 'cgF1_eval_bbox_positive_micro_precision': 0.806161117331975, 'cgF1_eval_bbox_IL_precision': 0.9624470012341326, 'cgF1_eval_bbox_IL_recall': 0.8497326198664531, 'cgF1_eval_bbox_IL_F1': 0.9025839944636483, 'cgF1_eval_bbox_IL_FPR': 0.0060564618534671814, 'cgF1_eval_bbox_IL_MCC': 0.8884369397448539, 'cgF1_eval_bbox_cgF1@0.5': 0.7348434113860801, 'cgF1_eval_bbox_precision@0.5': 0.8205550136275866, 'cgF1_eval_bbox_recall@0.5': 0.7881183467784425, 'cgF1_eval_bbox_F1@0.5': 0.8039596834419491, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9208045886576268, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.8271193806924737, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8702918216440054, 'cgF1_eval_bbox_cgF1@0.75': 0.7106209729640045, 'cgF1_eval_bbox_precision@0.75': 0.7935089183088213, 'cgF1_eval_bbox_recall@0.75': 0.7621413877989296, 'cgF1_eval_bbox_F1@0.75': 0.7774589352243091, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.9005662678327496, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.799855275229871, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8416063646394022}\n", + "Processing subset: attributes\n", + "loading annotations into memory...\n", + "Done (t=0.07s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.06s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.06s)\n", + "creating index...\n", + "index created!\n", + "Loaded 5834 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9222/9222 [00:03<00:00, 2820.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.549\n", + " Average precision @[ IoU=0.50:0.95] = 0.643\n", + " Average recall @[ IoU=0.50:0.95] = 0.670\n", + " Average F1 @[ IoU=0.50:0.95] = 0.656\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.872\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.720\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.778\n", + " Average IL_precision = 0.797\n", + " Average IL_recall = 0.819\n", + " Average IL_F1 = 0.808\n", + " Average IL_FPR = 0.048\n", + " Average IL_MCC = 0.763\n", + " Average cgF1 @[ IoU=0.50 ] = 0.600\n", + " Average precision @[ IoU=0.50 ] = 0.703\n", + " Average recall @[ IoU=0.50 ] = 0.733\n", + " Average F1 @[ IoU=0.50 ] = 0.717\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.930\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.787\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.850\n", + " Average cgF1 @[ IoU=0.75 ] = 0.569\n", + " Average precision @[ IoU=0.75 ] = 0.667\n", + " Average recall @[ IoU=0.75 ] = 0.695\n", + " Average F1 @[ IoU=0.75 ] = 0.680\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.890\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.746\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.806\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.5492693860624807, 'cgF1_eval_segm_precision': 0.6432095082730505, 'cgF1_eval_segm_recall': 0.670390051111405, 'cgF1_eval_segm_F1': 0.6564685998234858, 'cgF1_eval_segm_positive_macro_F1': 0.8717482377036931, 'cgF1_eval_segm_positive_micro_F1': 0.7200264336735771, 'cgF1_eval_segm_positive_micro_precision': 0.7777168057004865, 'cgF1_eval_segm_IL_precision': 0.7970687706893637, 'cgF1_eval_segm_IL_recall': 0.8187608565033231, 'cgF1_eval_segm_IL_F1': 0.807768708426457, 'cgF1_eval_segm_IL_FPR': 0.0480320213411565, 'cgF1_eval_segm_IL_MCC': 0.7628461406064031, 'cgF1_eval_segm_cgF1@0.5': 0.6002145763651893, 'cgF1_eval_segm_precision@0.5': 0.7028636035479556, 'cgF1_eval_segm_recall@0.5': 0.7325649901724294, 'cgF1_eval_segm_F1@0.5': 0.7173570363948575, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.9296877722553353, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.786809481513619, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.849845702782115, 'cgF1_eval_segm_cgF1@0.75': 0.5692213046663587, 'cgF1_eval_segm_precision@0.75': 0.6665721387419297, 'cgF1_eval_segm_recall@0.75': 0.6947399321885362, 'cgF1_eval_segm_F1@0.75': 0.6803146414763769, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8904105590365932, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.7461810113031079, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8059650049377783}\n", + "loading annotations into memory...\n", + "Done (t=0.05s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.05s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.06s)\n", + "creating index...\n", + "index created!\n", + "Loaded 5834 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9222/9222 [00:02<00:00, 3370.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.565\n", + " Average precision @[ IoU=0.50:0.95] = 0.660\n", + " Average recall @[ IoU=0.50:0.95] = 0.689\n", + " Average F1 @[ IoU=0.50:0.95] = 0.674\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.901\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.739\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.798\n", + " Average IL_precision = 0.798\n", + " Average IL_recall = 0.819\n", + " Average IL_F1 = 0.808\n", + " Average IL_FPR = 0.048\n", + " Average IL_MCC = 0.764\n", + " Average cgF1 @[ IoU=0.50 ] = 0.602\n", + " Average precision @[ IoU=0.50 ] = 0.703\n", + " Average recall @[ IoU=0.50 ] = 0.734\n", + " Average F1 @[ IoU=0.50 ] = 0.718\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.934\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.788\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.850\n", + " Average cgF1 @[ IoU=0.75 ] = 0.580\n", + " Average precision @[ IoU=0.75 ] = 0.679\n", + " Average recall @[ IoU=0.75 ] = 0.708\n", + " Average F1 @[ IoU=0.75 ] = 0.693\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.911\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.760\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.820\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.5646399041045982, 'cgF1_eval_bbox_precision': 0.6603061905215142, 'cgF1_eval_bbox_recall': 0.6892275617275063, 'cgF1_eval_bbox_F1': 0.6744070010794116, 'cgF1_eval_bbox_positive_macro_F1': 0.9009593951299972, 'cgF1_eval_bbox_positive_micro_F1': 0.7394009518446127, 'cgF1_eval_bbox_positive_micro_precision': 0.7975684658366964, 'cgF1_eval_bbox_IL_precision': 0.7981961664046244, 'cgF1_eval_bbox_IL_recall': 0.8189705027073623, 'cgF1_eval_bbox_IL_F1': 0.8084493997068858, 'cgF1_eval_bbox_IL_FPR': 0.04777792605795036, 'cgF1_eval_bbox_IL_MCC': 0.7636450868719721, 'cgF1_eval_bbox_cgF1@0.5': 0.6015189050487545, 'cgF1_eval_bbox_precision@0.5': 0.7034306576855498, 'cgF1_eval_bbox_recall@0.5': 0.7342408779449281, 'cgF1_eval_bbox_F1@0.5': 0.7184556528947154, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9340793389274347, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7876943299834277, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8496575051487155, 'cgF1_eval_bbox_cgF1@0.75': 0.5804244074542156, 'cgF1_eval_bbox_precision@0.75': 0.678763802700204, 'cgF1_eval_bbox_recall@0.75': 0.7084936162032075, 'cgF1_eval_bbox_F1@0.75': 0.693260171561296, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.9107485801358162, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.7600708986837572, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8198629856211306}\n", + "Processing subset: wiki_common\n", + "loading annotations into memory...\n", + "Done (t=0.23s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.21s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.21s)\n", + "creating index...\n", + "index created!\n", + "Loaded 8045 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 65452/65452 [00:11<00:00, 5775.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.425\n", + " Average precision @[ IoU=0.50:0.95] = 0.677\n", + " Average recall @[ IoU=0.50:0.95] = 0.509\n", + " Average F1 @[ IoU=0.50:0.95] = 0.581\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.811\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.608\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.757\n", + " Average IL_precision = 0.822\n", + " Average IL_recall = 0.607\n", + " Average IL_F1 = 0.698\n", + " Average IL_FPR = 0.004\n", + " Average IL_MCC = 0.699\n", + " Average cgF1 @[ IoU=0.50 ] = 0.482\n", + " Average precision @[ IoU=0.50 ] = 0.767\n", + " Average recall @[ IoU=0.50 ] = 0.577\n", + " Average F1 @[ IoU=0.50 ] = 0.658\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.905\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.689\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.857\n", + " Average cgF1 @[ IoU=0.75 ] = 0.450\n", + " Average precision @[ IoU=0.75 ] = 0.717\n", + " Average recall @[ IoU=0.75 ] = 0.539\n", + " Average F1 @[ IoU=0.75 ] = 0.615\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.844\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.644\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.801\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.42532135099113527, 'cgF1_eval_segm_precision': 0.6770954780038412, 'cgF1_eval_segm_recall': 0.5089505000184188, 'cgF1_eval_segm_F1': 0.5810550953744452, 'cgF1_eval_segm_positive_macro_F1': 0.8110173727833955, 'cgF1_eval_segm_positive_micro_F1': 0.6084924575531054, 'cgF1_eval_segm_positive_micro_precision': 0.7565875096115964, 'cgF1_eval_segm_IL_precision': 0.8222698066935975, 'cgF1_eval_segm_IL_recall': 0.6066350707705976, 'cgF1_eval_segm_IL_F1': 0.6981813291457964, 'cgF1_eval_segm_IL_FPR': 0.003917989709314776, 'cgF1_eval_segm_IL_MCC': 0.6989755513181787, 'cgF1_eval_segm_cgF1@0.5': 0.4817908882049053, 'cgF1_eval_segm_precision@0.5': 0.766985739738834, 'cgF1_eval_segm_recall@0.5': 0.5765180664001743, 'cgF1_eval_segm_F1@0.5': 0.6582016716689654, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.9051793905267748, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.6892814595536413, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.8570310238186531, 'cgF1_eval_segm_cgF1@0.75': 0.4501092776435151, 'cgF1_eval_segm_precision@0.75': 0.7165538006875134, 'cgF1_eval_segm_recall@0.75': 0.5386100291574232, 'cgF1_eval_segm_F1@0.75': 0.6149194365440128, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8441369689666682, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.643955681703983, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.8006782989648239}\n", + "loading annotations into memory...\n", + "Done (t=0.18s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.19s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.18s)\n", + "creating index...\n", + "index created!\n", + "Loaded 8045 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████��█████████████████████████████████████████████████████████████████████████████| 65452/65452 [00:10<00:00, 6310.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.443\n", + " Average precision @[ IoU=0.50:0.95] = 0.706\n", + " Average recall @[ IoU=0.50:0.95] = 0.529\n", + " Average F1 @[ IoU=0.50:0.95] = 0.605\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.865\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.633\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.788\n", + " Average IL_precision = 0.822\n", + " Average IL_recall = 0.607\n", + " Average IL_F1 = 0.698\n", + " Average IL_FPR = 0.004\n", + " Average IL_MCC = 0.699\n", + " Average cgF1 @[ IoU=0.50 ] = 0.483\n", + " Average precision @[ IoU=0.50 ] = 0.770\n", + " Average recall @[ IoU=0.50 ] = 0.578\n", + " Average F1 @[ IoU=0.50 ] = 0.660\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.909\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.691\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.860\n", + " Average cgF1 @[ IoU=0.75 ] = 0.459\n", + " Average precision @[ IoU=0.75 ] = 0.732\n", + " Average recall @[ IoU=0.75 ] = 0.549\n", + " Average F1 @[ IoU=0.75 ] = 0.628\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.875\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.657\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.818\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.4427363803296873, 'cgF1_eval_bbox_precision': 0.7055568361999337, 'cgF1_eval_bbox_recall': 0.5294148470670139, 'cgF1_eval_bbox_F1': 0.6048754410558315, 'cgF1_eval_bbox_positive_macro_F1': 0.8646998403591537, 'cgF1_eval_bbox_positive_micro_F1': 0.6334075340614461, 'cgF1_eval_bbox_positive_micro_precision': 0.7883902742397528, 'cgF1_eval_bbox_IL_precision': 0.8222698066935975, 'cgF1_eval_bbox_IL_recall': 0.6066350707705976, 'cgF1_eval_bbox_IL_F1': 0.6981813291457964, 'cgF1_eval_bbox_IL_FPR': 0.003917989709314776, 'cgF1_eval_bbox_IL_MCC': 0.6989755513181787, 'cgF1_eval_bbox_cgF1@0.5': 0.4830441402948427, 'cgF1_eval_bbox_precision@0.5': 0.769787514130574, 'cgF1_eval_bbox_recall@0.5': 0.5776103612892368, 'cgF1_eval_bbox_F1@0.5': 0.6599450021609545, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9093257078072422, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.6910744437110613, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.8601617307549769, 'cgF1_eval_bbox_cgF1@0.75': 0.4594543570790383, 'cgF1_eval_bbox_precision@0.75': 0.7321970410413953, 'cgF1_eval_bbox_recall@0.75': 0.5494043351540936, 'cgF1_eval_bbox_F1@0.75': 0.6277159968872291, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8750107505715273, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6573253616847772, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.8181580793592987}\n" + ] + } + ], + "source": [ + "results_gold = {}\n", + "results_gold_bbox = {}\n", + "\n", + "for subset_name, gts in saco_gold_gts.items():\n", + " print(\"Processing subset: \", subset_name)\n", + " gt_paths = [os.path.join(GT_DIR, gt) for gt in gts]\n", + " pred_path = os.path.join(PRED_DIR, f\"gold_{subset_name}/dumps/gold_{subset_name}/coco_predictions_segm.json\")\n", + " \n", + " evaluator = CGF1Evaluator(gt_path=gt_paths, verbose=True, iou_type=\"segm\") \n", + " summary = evaluator.evaluate(pred_path)\n", + " print(summary)\n", + "\n", + " cur_results = {}\n", + " cur_results[\"cgf1\"] = summary[\"cgF1_eval_segm_cgF1\"] * 100\n", + " cur_results[\"il_mcc\"] = summary[\"cgF1_eval_segm_IL_MCC\"]\n", + " cur_results[\"pmf1\"] = summary[\"cgF1_eval_segm_positive_micro_F1\"] * 100\n", + " results_gold[subset_name] = cur_results\n", + "\n", + " # Also eval bbox \n", + " evaluator = CGF1Evaluator(gt_path=gt_paths, verbose=True, iou_type=\"bbox\") \n", + " summary = evaluator.evaluate(pred_path)\n", + " print(summary)\n", + "\n", + " cur_results = {}\n", + " cur_results[\"cgf1\"] = summary[\"cgF1_eval_bbox_cgF1\"] * 100\n", + " cur_results[\"il_mcc\"] = summary[\"cgF1_eval_bbox_IL_MCC\"]\n", + " cur_results[\"pmf1\"] = summary[\"cgF1_eval_bbox_positive_micro_F1\"] * 100\n", + " results_gold_bbox[subset_name] = cur_results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c808a7cf-ecda-445a-a827-53a16dee4504", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute averages\n", + "METRICS = [\"cgf1\", \"il_mcc\", \"pmf1\"]\n", + "avg_stats, avg_stats_bbox = {}, {}\n", + "for key in METRICS:\n", + " avg_stats[key] = sum(res[key] for res in results_gold.values()) / len(results_gold)\n", + " avg_stats_bbox[key] = sum(res[key] for res in results_gold_bbox.values()) / len(results_gold_bbox)\n", + "results_gold[\"Average\"] = avg_stats\n", + "results_gold_bbox[\"Average\"] = avg_stats_bbox" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c26b1fb2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
metaclip_npssa1b_npscrowdedfg_foodfg_sports_equipmentattributeswiki_commonAverage
cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1
47.260.8158.5853.690.8662.5561.080.967.7353.410.7967.2865.520.8973.7554.930.7672.042.530.760.8554.060.8266.11
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pretty print segmentation results\n", + "from IPython.display import HTML, display\n", + "\n", + "row1, row2, row3 = \"\", \"\", \"\"\n", + "for subset in results_gold:\n", + " row1 += f'{subset}'\n", + " row2 += \"\" + \"\".join(METRICS) + \"\"\n", + " row3 += \"\" + \"\".join([str(round(results_gold[subset][k], 2)) for k in METRICS]) + \"\"\n", + "\n", + "display(HTML(\n", + " f\"{row1}{row2}{row3}
\"\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d3c85735-4aea-436d-a233-6f18cff29147", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
metaclip_npssa1b_npscrowdedfg_foodfg_sports_equipmentattributeswiki_commonAverage
cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1cgf1il_mccpmf1
50.030.8161.9955.360.8664.4961.710.968.4353.790.867.6468.070.8976.6256.460.7673.9444.270.763.3455.670.8268.06
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pretty print bbox detection results\n", + "from IPython.display import HTML, display\n", + "\n", + "row1, row2, row3 = \"\", \"\", \"\"\n", + "for subset in results_gold:\n", + " row1 += f'{subset}'\n", + " row2 += \"\" + \"\".join(METRICS) + \"\"\n", + " row3 += \"\" + \"\".join([str(round(results_gold_bbox[subset][k], 2)) for k in METRICS]) + \"\"\n", + "\n", + "display(HTML(\n", + " f\"{row1}{row2}{row3}
\"\n", + "))" + ] + }, + { + "cell_type": "markdown", + "id": "ef79428e-9212-4e26-8122-3e82841447de", + "metadata": {}, + "source": [ + "# SA-Co/Silver" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "55995b3b-1184-4d1f-b9bb-ad412eb734a3", + "metadata": {}, + "outputs": [], + "source": [ + "# Update to the directory where the GT annotation and PRED files exist\n", + "GT_DIR = # PUT YOUR PATH HERE\n", + "PRED_DIR = # PUT YOUR PATH HERE" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cd254a73-7272-4df1-90d1-b818f5a7c6f7", + "metadata": {}, + "outputs": [], + "source": [ + "saco_silver_gts = {\n", + " \"bdd100k\": \"silver_bdd100k_merged_test.json\",\n", + " \"droid\": \"silver_droid_merged_test.json\",\n", + " \"ego4d\": \"silver_ego4d_merged_test.json\",\n", + " \"food_rec\": \"silver_food_rec_merged_test.json\",\n", + " \"geode\": \"silver_geode_merged_test.json\",\n", + " \"inaturalist\": \"silver_inaturalist_merged_test.json\",\n", + " \"nga_art\": \"silver_nga_art_merged_test.json\",\n", + " \"sav\": \"silver_sav_merged_test.json\",\n", + " \"yt1b\": \"silver_yt1b_merged_test.json\",\n", + " \"fathomnet\": \"silver_fathomnet_test.json\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c736eabf-6e52-4f52-b4ab-111eaa490584", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing subset: bdd100k\n", + "loading annotations into memory...\n", + "Done (t=0.12s)\n", + "creating index...\n", + "index created!\n", + "Loaded 31278 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5439/5439 [00:01<00:00, 3496.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.466\n", + " Average precision @[ IoU=0.50:0.95] = 0.514\n", + " Average recall @[ IoU=0.50:0.95] = 0.644\n", + " Average F1 @[ IoU=0.50:0.95] = 0.572\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.669\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.601\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.564\n", + " Average IL_precision = 0.870\n", + " Average IL_recall = 0.952\n", + " Average IL_F1 = 0.909\n", + " Average IL_FPR = 0.196\n", + " Average IL_MCC = 0.775\n", + " Average cgF1 @[ IoU=0.50 ] = 0.563\n", + " Average precision @[ IoU=0.50 ] = 0.621\n", + " Average recall @[ IoU=0.50 ] = 0.779\n", + " Average F1 @[ IoU=0.50 ] = 0.691\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.769\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.726\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.681\n", + " Average cgF1 @[ IoU=0.75 ] = 0.507\n", + " Average precision @[ IoU=0.75 ] = 0.560\n", + " Average recall @[ IoU=0.75 ] = 0.701\n", + " Average F1 @[ IoU=0.75 ] = 0.623\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.708\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.655\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.614\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.4660589364507352, 'cgF1_eval_segm_precision': 0.5141618280038143, 'cgF1_eval_segm_recall': 0.6443148170650035, 'cgF1_eval_segm_F1': 0.5718777188484898, 'cgF1_eval_segm_positive_macro_F1': 0.6686547809892744, 'cgF1_eval_segm_positive_micro_F1': 0.6012626762403565, 'cgF1_eval_segm_positive_micro_precision': 0.5636910131863614, 'cgF1_eval_segm_IL_precision': 0.8697795821143331, 'cgF1_eval_segm_IL_recall': 0.9523658301199219, 'cgF1_eval_segm_IL_F1': 0.9092006527905545, 'cgF1_eval_segm_IL_FPR': 0.19606986891001316, 'cgF1_eval_segm_IL_MCC': 0.7751336560003381, 'cgF1_eval_segm_cgF1@0.5': 0.5631327201709976, 'cgF1_eval_segm_precision@0.5': 0.6212459311276801, 'cgF1_eval_segm_recall@0.5': 0.7785057868277604, 'cgF1_eval_segm_F1@0.5': 0.6909925475999513, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.7694734183454693, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7264975734336478, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.6810905230262809, 'cgF1_eval_segm_cgF1@0.75': 0.5073531550281276, 'cgF1_eval_segm_precision@0.75': 0.559714336799628, 'cgF1_eval_segm_recall@0.75': 0.7013983164091225, 'cgF1_eval_segm_F1@0.75': 0.6225479779361666, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7080380721802793, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6545363513771957, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6136315930539561}\n", + "loading annotations into memory...\n", + "Done (t=0.09s)\n", + "creating index...\n", + "index created!\n", + "Loaded 31278 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5439/5439 [00:01<00:00, 4945.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.462\n", + " Average precision @[ IoU=0.50:0.95] = 0.510\n", + " Average recall @[ IoU=0.50:0.95] = 0.639\n", + " Average F1 @[ IoU=0.50:0.95] = 0.567\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.673\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.596\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.559\n", + " Average IL_precision = 0.870\n", + " Average IL_recall = 0.952\n", + " Average IL_F1 = 0.909\n", + " Average IL_FPR = 0.196\n", + " Average IL_MCC = 0.775\n", + " Average cgF1 @[ IoU=0.50 ] = 0.562\n", + " Average precision @[ IoU=0.50 ] = 0.620\n", + " Average recall @[ IoU=0.50 ] = 0.777\n", + " Average F1 @[ IoU=0.50 ] = 0.689\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.769\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.725\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.679\n", + " Average cgF1 @[ IoU=0.75 ] = 0.507\n", + " Average precision @[ IoU=0.75 ] = 0.559\n", + " Average recall @[ IoU=0.75 ] = 0.700\n", + " Average F1 @[ IoU=0.75 ] = 0.622\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.714\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.653\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.613\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.4621052346722842, 'cgF1_eval_bbox_precision': 0.5098004175871937, 'cgF1_eval_bbox_recall': 0.6388493756384388, 'cgF1_eval_bbox_F1': 0.5670263110638136, 'cgF1_eval_bbox_positive_macro_F1': 0.673487715877281, 'cgF1_eval_bbox_positive_micro_F1': 0.5961620052169204, 'cgF1_eval_bbox_positive_micro_precision': 0.5589094683054214, 'cgF1_eval_bbox_IL_precision': 0.8697795821143331, 'cgF1_eval_bbox_IL_recall': 0.9523658301199219, 'cgF1_eval_bbox_IL_F1': 0.9092006527905545, 'cgF1_eval_bbox_IL_FPR': 0.19606986891001316, 'cgF1_eval_bbox_IL_MCC': 0.7751336560003381, 'cgF1_eval_bbox_cgF1@0.5': 0.5618032590322063, 'cgF1_eval_bbox_precision@0.5': 0.6197793749934364, 'cgF1_eval_bbox_recall@0.5': 0.7766679921960209, 'cgF1_eval_bbox_F1@0.5': 0.6893612262495565, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.7694834511677656, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7247824354977579, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.6794826936072514, 'cgF1_eval_bbox_cgF1@0.75': 0.5065439178140905, 'cgF1_eval_bbox_precision@0.75': 0.5588216504570449, 'cgF1_eval_bbox_recall@0.75': 0.7002796588071941, 'cgF1_eval_bbox_F1@0.75': 0.621554999724056, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7144152159611694, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6534923543738753, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6126529142771555}\n", + "Processing subset: droid\n", + "loading annotations into memory...\n", + "Done (t=0.15s)\n", + "creating index...\n", + "index created!\n", + "Loaded 27006 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9415/9415 [00:02<00:00, 4431.41it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.456\n", + " Average precision @[ IoU=0.50:0.95] = 0.501\n", + " Average recall @[ IoU=0.50:0.95] = 0.651\n", + " Average F1 @[ IoU=0.50:0.95] = 0.566\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.717\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.603\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.562\n", + " Average IL_precision = 0.869\n", + " Average IL_recall = 0.881\n", + " Average IL_F1 = 0.875\n", + " Average IL_FPR = 0.125\n", + " Average IL_MCC = 0.755\n", + " Average cgF1 @[ IoU=0.50 ] = 0.517\n", + " Average precision @[ IoU=0.50 ] = 0.568\n", + " Average recall @[ IoU=0.50 ] = 0.739\n", + " Average F1 @[ IoU=0.50 ] = 0.642\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.782\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.685\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.638\n", + " Average cgF1 @[ IoU=0.75 ] = 0.481\n", + " Average precision @[ IoU=0.75 ] = 0.528\n", + " Average recall @[ IoU=0.75 ] = 0.687\n", + " Average F1 @[ IoU=0.75 ] = 0.597\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.740\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.636\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.593\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.4557789681577404, 'cgF1_eval_segm_precision': 0.5005361319569939, 'cgF1_eval_segm_recall': 0.651409427375482, 'cgF1_eval_segm_F1': 0.5660435085175799, 'cgF1_eval_segm_positive_macro_F1': 0.7165954275000161, 'cgF1_eval_segm_positive_micro_F1': 0.6034929143710873, 'cgF1_eval_segm_positive_micro_precision': 0.5622289982156015, 'cgF1_eval_segm_IL_precision': 0.8689550948037661, 'cgF1_eval_segm_IL_recall': 0.8807439823018065, 'cgF1_eval_segm_IL_F1': 0.874809323784164, 'cgF1_eval_segm_IL_FPR': 0.12528379770375977, 'cgF1_eval_segm_IL_MCC': 0.7552349949836895, 'cgF1_eval_segm_cgF1@0.5': 0.516971131551212, 'cgF1_eval_segm_precision@0.5': 0.5677317852045766, 'cgF1_eval_segm_recall@0.5': 0.7388594219103226, 'cgF1_eval_segm_F1@0.5': 0.6420399499626908, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.7817224981491459, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.6845169185551006, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.637706755759541, 'cgF1_eval_segm_cgF1@0.75': 0.4806464217139674, 'cgF1_eval_segm_precision@0.75': 0.5278433016810116, 'cgF1_eval_segm_recall@0.75': 0.686947616643896, 'cgF1_eval_segm_F1@0.75': 0.5969271686567055, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7403811012887065, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6364196904360181, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.5929018741536706}\n", + "loading annotations into memory...\n", + "Done (t=0.12s)\n", + "creating index...\n", + "index created!\n", + "Loaded 27006 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9415/9415 [00:01<00:00, 5301.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.461\n", + " Average precision @[ IoU=0.50:0.95] = 0.506\n", + " Average recall @[ IoU=0.50:0.95] = 0.659\n", + " Average F1 @[ IoU=0.50:0.95] = 0.573\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.726\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.611\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.569\n", + " Average IL_precision = 0.869\n", + " Average IL_recall = 0.881\n", + " Average IL_F1 = 0.875\n", + " Average IL_FPR = 0.125\n", + " Average IL_MCC = 0.755\n", + " Average cgF1 @[ IoU=0.50 ] = 0.516\n", + " Average precision @[ IoU=0.50 ] = 0.566\n", + " Average recall @[ IoU=0.50 ] = 0.737\n", + " Average F1 @[ IoU=0.50 ] = 0.641\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.778\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.683\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.636\n", + " Average cgF1 @[ IoU=0.75 ] = 0.484\n", + " Average precision @[ IoU=0.75 ] = 0.532\n", + " Average recall @[ IoU=0.75 ] = 0.692\n", + " Average F1 @[ IoU=0.75 ] = 0.601\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.743\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.641\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.597\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.46120814518784153, 'cgF1_eval_bbox_precision': 0.5064979590642793, 'cgF1_eval_bbox_recall': 0.6591682885927223, 'cgF1_eval_bbox_F1': 0.5727861714864271, 'cgF1_eval_bbox_positive_macro_F1': 0.7257891003555039, 'cgF1_eval_bbox_positive_micro_F1': 0.6106816398223206, 'cgF1_eval_bbox_positive_micro_precision': 0.5689256418104573, 'cgF1_eval_bbox_IL_precision': 0.8689550948037661, 'cgF1_eval_bbox_IL_recall': 0.8807439823018065, 'cgF1_eval_bbox_IL_F1': 0.874809323784164, 'cgF1_eval_bbox_IL_FPR': 0.12528379770375977, 'cgF1_eval_bbox_IL_MCC': 0.7552349949836895, 'cgF1_eval_bbox_cgF1@0.5': 0.5157342686709716, 'cgF1_eval_bbox_precision@0.5': 0.5663735751921255, 'cgF1_eval_bbox_recall@0.5': 0.7370918156378099, 'cgF1_eval_bbox_F1@0.5': 0.6405038516738359, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.7784650302224031, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.6828791993174386, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.6361811415113126, 'cgF1_eval_bbox_cgF1@0.75': 0.48416171621299414, 'cgF1_eval_bbox_precision@0.75': 0.5317034775058727, 'cgF1_eval_bbox_recall@0.75': 0.6919713397341954, 'cgF1_eval_bbox_F1@0.75': 0.6012929216844536, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.743302410171559, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6410742608973653, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.5972378304381096}\n", + "Processing subset: ego4d\n", + "loading annotations into memory...\n", + "Done (t=0.36s)\n", + "creating index...\n", + "index created!\n", + "Loaded 54328 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12428/12428 [00:04<00:00, 2599.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.386\n", + " Average precision @[ IoU=0.50:0.95] = 0.521\n", + " Average recall @[ IoU=0.50:0.95] = 0.689\n", + " Average F1 @[ IoU=0.50:0.95] = 0.594\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.765\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.626\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.573\n", + " Average IL_precision = 0.901\n", + " Average IL_recall = 0.912\n", + " Average IL_F1 = 0.907\n", + " Average IL_FPR = 0.303\n", + " Average IL_MCC = 0.618\n", + " Average cgF1 @[ IoU=0.50 ] = 0.438\n", + " Average precision @[ IoU=0.50 ] = 0.591\n", + " Average recall @[ IoU=0.50 ] = 0.782\n", + " Average F1 @[ IoU=0.50 ] = 0.673\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.842\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.709\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.649\n", + " Average cgF1 @[ IoU=0.75 ] = 0.404\n", + " Average precision @[ IoU=0.75 ] = 0.545\n", + " Average recall @[ IoU=0.75 ] = 0.721\n", + " Average F1 @[ IoU=0.75 ] = 0.621\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.795\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.655\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.599\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.38643115983316056, 'cgF1_eval_segm_precision': 0.5213111360174665, 'cgF1_eval_segm_recall': 0.6893477933583816, 'cgF1_eval_segm_F1': 0.5936188825546556, 'cgF1_eval_segm_positive_macro_F1': 0.7647892994501866, 'cgF1_eval_segm_positive_micro_F1': 0.6256311694898511, 'cgF1_eval_segm_positive_micro_precision': 0.5727797240533252, 'cgF1_eval_segm_IL_precision': 0.9011314369354836, 'cgF1_eval_segm_IL_recall': 0.9124197001164432, 'cgF1_eval_segm_IL_F1': 0.9067399372269952, 'cgF1_eval_segm_IL_FPR': 0.30278497399521215, 'cgF1_eval_segm_IL_MCC': 0.6176660925450091, 'cgF1_eval_segm_cgF1@0.5': 0.4381751660817137, 'cgF1_eval_segm_precision@0.5': 0.5911103463493724, 'cgF1_eval_segm_recall@0.5': 0.7816457097006957, 'cgF1_eval_segm_F1@0.5': 0.6731060611161825, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8419527726712451, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7094045980025753, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.6494701487744837, 'cgF1_eval_segm_cgF1@0.75': 0.4042830454832554, 'cgF1_eval_segm_precision@0.75': 0.545392138165807, 'cgF1_eval_segm_recall@0.75': 0.7211909375882043, 'cgF1_eval_segm_F1@0.75': 0.6210422717504098, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7947833780726534, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6545333317836213, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.5992382222753772}\n", + "loading annotations into memory...\n", + "Done (t=0.31s)\n", + "creating index...\n", + "index created!\n", + "Loaded 54328 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12428/12428 [00:03<00:00, 4068.74it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.388\n", + " Average precision @[ IoU=0.50:0.95] = 0.523\n", + " Average recall @[ IoU=0.50:0.95] = 0.692\n", + " Average F1 @[ IoU=0.50:0.95] = 0.596\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.778\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.628\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.575\n", + " Average IL_precision = 0.901\n", + " Average IL_recall = 0.912\n", + " Average IL_F1 = 0.907\n", + " Average IL_FPR = 0.303\n", + " Average IL_MCC = 0.618\n", + " Average cgF1 @[ IoU=0.50 ] = 0.437\n", + " Average precision @[ IoU=0.50 ] = 0.589\n", + " Average recall @[ IoU=0.50 ] = 0.779\n", + " Average F1 @[ IoU=0.50 ] = 0.671\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.840\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.707\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.647\n", + " Average cgF1 @[ IoU=0.75 ] = 0.404\n", + " Average precision @[ IoU=0.75 ] = 0.545\n", + " Average recall @[ IoU=0.75 ] = 0.721\n", + " Average F1 @[ IoU=0.75 ] = 0.621\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.796\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.654\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.599\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.38789930948498, 'cgF1_eval_bbox_precision': 0.5232915719125053, 'cgF1_eval_bbox_recall': 0.6919665924206118, 'cgF1_eval_bbox_F1': 0.5958741983272995, 'cgF1_eval_bbox_positive_macro_F1': 0.7777412549940348, 'cgF1_eval_bbox_positive_micro_F1': 0.6280081004393389, 'cgF1_eval_bbox_positive_micro_precision': 0.5749556866351561, 'cgF1_eval_bbox_IL_precision': 0.9011314369354836, 'cgF1_eval_bbox_IL_recall': 0.9124197001164432, 'cgF1_eval_bbox_IL_F1': 0.9067399372269952, 'cgF1_eval_bbox_IL_FPR': 0.30278497399521215, 'cgF1_eval_bbox_IL_MCC': 0.6176660925450091, 'cgF1_eval_bbox_cgF1@0.5': 0.43664849398208544, 'cgF1_eval_bbox_precision@0.5': 0.589050967602365, 'cgF1_eval_bbox_recall@0.5': 0.7789225217677006, 'cgF1_eval_bbox_F1@0.5': 0.6707608453779862, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8401608683011329, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7069329193430948, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.6472074493826321, 'cgF1_eval_bbox_cgF1@0.75': 0.4039522665286139, 'cgF1_eval_bbox_precision@0.75': 0.5449459394372886, 'cgF1_eval_bbox_recall@0.75': 0.7206009135360554, 'cgF1_eval_bbox_F1@0.75': 0.6205341416742646, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7957011848300353, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6539978014078505, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.598747970740476}\n", + "Processing subset: food_rec\n", + "loading annotations into memory...\n", + "Done (t=0.31s)\n", + "creating index...\n", + "index created!\n", + "Loaded 54984 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20888/20888 [00:04<00:00, 4826.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.530\n", + " Average precision @[ IoU=0.50:0.95] = 0.598\n", + " Average recall @[ IoU=0.50:0.95] = 0.674\n", + " Average F1 @[ IoU=0.50:0.95] = 0.634\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.839\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.672\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.670\n", + " Average IL_precision = 0.863\n", + " Average IL_recall = 0.903\n", + " Average IL_F1 = 0.883\n", + " Average IL_FPR = 0.112\n", + " Average IL_MCC = 0.788\n", + " Average cgF1 @[ IoU=0.50 ] = 0.576\n", + " Average precision @[ IoU=0.50 ] = 0.650\n", + " Average recall @[ IoU=0.50 ] = 0.733\n", + " Average F1 @[ IoU=0.50 ] = 0.689\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.883\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.731\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.729\n", + " Average cgF1 @[ IoU=0.75 ] = 0.549\n", + " Average precision @[ IoU=0.75 ] = 0.620\n", + " Average recall @[ IoU=0.75 ] = 0.699\n", + " Average F1 @[ IoU=0.75 ] = 0.657\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.855\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.697\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.695\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.5296031530873976, 'cgF1_eval_segm_precision': 0.5979719064128862, 'cgF1_eval_segm_recall': 0.67394967253306, 'cgF1_eval_segm_F1': 0.6336417199495971, 'cgF1_eval_segm_positive_macro_F1': 0.8386411473003422, 'cgF1_eval_segm_positive_micro_F1': 0.6721473109964069, 'cgF1_eval_segm_positive_micro_precision': 0.6704540304049686, 'cgF1_eval_segm_IL_precision': 0.8631095331666377, 'cgF1_eval_segm_IL_recall': 0.9029932269059434, 'cgF1_eval_segm_IL_F1': 0.882600535877229, 'cgF1_eval_segm_IL_FPR': 0.11172660643329413, 'cgF1_eval_segm_IL_MCC': 0.7879272064665432, 'cgF1_eval_segm_cgF1@0.5': 0.5756816178619664, 'cgF1_eval_segm_precision@0.5': 0.6499949694948377, 'cgF1_eval_segm_recall@0.5': 0.7325827386558004, 'cgF1_eval_segm_F1@0.5': 0.6887723611338412, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8833570881072849, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7306279223985785, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7287829785432497, 'cgF1_eval_segm_cgF1@0.75': 0.5489294451647743, 'cgF1_eval_segm_precision@0.75': 0.6197914899943298, 'cgF1_eval_segm_recall@0.75': 0.6985416325429136, 'cgF1_eval_segm_F1@0.75': 0.6567646881074378, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.854577862666958, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6966753282025206, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6949184368378619}\n", + "loading annotations into memory...\n", + "Done (t=0.27s)\n", + "creating index...\n", + "index created!\n", + "Loaded 54984 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████��███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20888/20888 [00:03<00:00, 6545.85it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.534\n", + " Average precision @[ IoU=0.50:0.95] = 0.602\n", + " Average recall @[ IoU=0.50:0.95] = 0.679\n", + " Average F1 @[ IoU=0.50:0.95] = 0.638\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.869\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.677\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.675\n", + " Average IL_precision = 0.863\n", + " Average IL_recall = 0.903\n", + " Average IL_F1 = 0.883\n", + " Average IL_FPR = 0.112\n", + " Average IL_MCC = 0.788\n", + " Average cgF1 @[ IoU=0.50 ] = 0.577\n", + " Average precision @[ IoU=0.50 ] = 0.652\n", + " Average recall @[ IoU=0.50 ] = 0.735\n", + " Average F1 @[ IoU=0.50 ] = 0.691\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.897\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.733\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.731\n", + " Average cgF1 @[ IoU=0.75 ] = 0.554\n", + " Average precision @[ IoU=0.75 ] = 0.626\n", + " Average recall @[ IoU=0.75 ] = 0.705\n", + " Average F1 @[ IoU=0.75 ] = 0.663\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.881\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.703\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.701\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.5335343269652235, 'cgF1_eval_bbox_precision': 0.602410242357245, 'cgF1_eval_bbox_recall': 0.6789519394024788, 'cgF1_eval_bbox_F1': 0.6383451782260778, 'cgF1_eval_bbox_positive_macro_F1': 0.8687478456564411, 'cgF1_eval_bbox_positive_micro_F1': 0.6771365712295384, 'cgF1_eval_bbox_positive_micro_precision': 0.675430351516837, 'cgF1_eval_bbox_IL_precision': 0.8631095331666377, 'cgF1_eval_bbox_IL_recall': 0.9029932269059434, 'cgF1_eval_bbox_IL_F1': 0.882600535877229, 'cgF1_eval_bbox_IL_FPR': 0.11172660643329413, 'cgF1_eval_bbox_IL_MCC': 0.7879272064665432, 'cgF1_eval_bbox_cgF1@0.5': 0.5771958917885865, 'cgF1_eval_bbox_precision@0.5': 0.6517046004099607, 'cgF1_eval_bbox_recall@0.5': 0.734509593718794, 'cgF1_eval_bbox_F1@0.5': 0.6905841162112941, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8970825996312395, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7325497673535344, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.730699839394498, 'cgF1_eval_bbox_cgF1@0.75': 0.5540364082092076, 'cgF1_eval_bbox_precision@0.75': 0.6255573040610194, 'cgF1_eval_bbox_recall@0.75': 0.705040045696539, 'cgF1_eval_bbox_F1@0.75': 0.6628749209147204, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8806948276411976, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.703156844518394, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7013831440224643}\n", + "Processing subset: geode\n", + "loading annotations into memory...\n", + "Done (t=0.16s)\n", + "creating index...\n", + "index created!\n", + "Loaded 14206 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14797/14797 [00:02<00:00, 5611.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.701\n", + " Average precision @[ IoU=0.50:0.95] = 0.672\n", + " Average recall @[ IoU=0.50:0.95] = 0.840\n", + " Average F1 @[ IoU=0.50:0.95] = 0.747\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.857\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.787\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.741\n", + " Average IL_precision = 0.881\n", + " Average IL_recall = 0.975\n", + " Average IL_F1 = 0.925\n", + " Average IL_FPR = 0.062\n", + " Average IL_MCC = 0.890\n", + " Average cgF1 @[ IoU=0.50 ] = 0.745\n", + " Average precision @[ IoU=0.50 ] = 0.714\n", + " Average recall @[ IoU=0.50 ] = 0.893\n", + " Average F1 @[ IoU=0.50 ] = 0.793\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.899\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.837\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.787\n", + " Average cgF1 @[ IoU=0.75 ] = 0.716\n", + " Average precision @[ IoU=0.75 ] = 0.687\n", + " Average recall @[ IoU=0.75 ] = 0.859\n", + " Average F1 @[ IoU=0.75 ] = 0.763\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.869\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.805\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.758\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.7006886839682105, 'cgF1_eval_segm_precision': 0.6718811161345404, 'cgF1_eval_segm_recall': 0.8400571547562657, 'cgF1_eval_segm_F1': 0.7465664720739594, 'cgF1_eval_segm_positive_macro_F1': 0.8565758633142841, 'cgF1_eval_segm_positive_micro_F1': 0.7872827580159756, 'cgF1_eval_segm_positive_micro_precision': 0.740835425028983, 'cgF1_eval_segm_IL_precision': 0.8808211364986084, 'cgF1_eval_segm_IL_recall': 0.9747580982383764, 'cgF1_eval_segm_IL_F1': 0.9254113832735964, 'cgF1_eval_segm_IL_FPR': 0.06243154435303878, 'cgF1_eval_segm_IL_MCC': 0.8900089286014722, 'cgF1_eval_segm_cgF1@0.5': 0.7445249438888863, 'cgF1_eval_segm_precision@0.5': 0.7139124677344605, 'cgF1_eval_segm_recall@0.5': 0.8926092161071292, 'cgF1_eval_segm_F1@0.5': 0.7932730581226168, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8987084032239174, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8365364885258015, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7871804010661337, 'cgF1_eval_segm_cgF1@0.75': 0.7164815452728956, 'cgF1_eval_segm_precision@0.75': 0.6870237242867002, 'cgF1_eval_segm_recall@0.75': 0.8589900522799775, 'cgF1_eval_segm_F1@0.75': 0.7633934239343049, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8685617156358091, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.8050273679824188, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.7575320998975862}\n", + "loading annotations into memory...\n", + "Done (t=0.11s)\n", + "creating index...\n", + "index created!\n", + "Loaded 14206 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14797/14797 [00:01<00:00, 7792.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.708\n", + " Average precision @[ IoU=0.50:0.95] = 0.679\n", + " Average recall @[ IoU=0.50:0.95] = 0.848\n", + " Average F1 @[ IoU=0.50:0.95] = 0.754\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.872\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.795\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.748\n", + " Average IL_precision = 0.881\n", + " Average IL_recall = 0.975\n", + " Average IL_F1 = 0.925\n", + " Average IL_FPR = 0.062\n", + " Average IL_MCC = 0.890\n", + " Average cgF1 @[ IoU=0.50 ] = 0.744\n", + " Average precision @[ IoU=0.50 ] = 0.714\n", + " Average recall @[ IoU=0.50 ] = 0.892\n", + " Average F1 @[ IoU=0.50 ] = 0.793\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.901\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.836\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.787\n", + " Average cgF1 @[ IoU=0.75 ] = 0.723\n", + " Average precision @[ IoU=0.75 ] = 0.693\n", + " Average recall @[ IoU=0.75 ] = 0.867\n", + " Average F1 @[ IoU=0.75 ] = 0.770\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.882\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.812\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.764\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.7076143815954025, 'cgF1_eval_bbox_precision': 0.6785216560143518, 'cgF1_eval_bbox_recall': 0.8483598632317971, 'cgF1_eval_bbox_F1': 0.7539456529790385, 'cgF1_eval_bbox_positive_macro_F1': 0.8719976344244793, 'cgF1_eval_bbox_positive_micro_F1': 0.7950643626770375, 'cgF1_eval_bbox_positive_micro_precision': 0.7481574751151425, 'cgF1_eval_bbox_IL_precision': 0.8808211364986084, 'cgF1_eval_bbox_IL_recall': 0.9747580982383764, 'cgF1_eval_bbox_IL_F1': 0.9254113832735964, 'cgF1_eval_bbox_IL_FPR': 0.06243154435303878, 'cgF1_eval_bbox_IL_MCC': 0.8900089286014722, 'cgF1_eval_bbox_cgF1@0.5': 0.7442978718352845, 'cgF1_eval_bbox_precision@0.5': 0.7136947451154503, 'cgF1_eval_bbox_recall@0.5': 0.8923369961571118, 'cgF1_eval_bbox_F1@0.5': 0.7930311177647804, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9007468930624848, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.836281353946468, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7869403338501941, 'cgF1_eval_bbox_cgF1@0.75': 0.7230666348257286, 'cgF1_eval_bbox_precision@0.75': 0.6933376802379961, 'cgF1_eval_bbox_recall@0.75': 0.8668844308304827, 'cgF1_eval_bbox_F1@0.75': 0.7704096943096695, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.8818653539703468, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.8124262707812712, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.7644940491598362}\n", + "Processing subset: inaturalist\n", + "loading annotations into memory...\n", + "Done (t=3.87s)\n", + "creating index...\n", + "index created!\n", + "Loaded 53887 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1439027/1439027 [01:22<00:00, 17398.82it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.658\n", + " Average precision @[ IoU=0.50:0.95] = 0.776\n", + " Average recall @[ IoU=0.50:0.95] = 0.722\n", + " Average F1 @[ IoU=0.50:0.95] = 0.748\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.935\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.807\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.915\n", + " Average IL_precision = 0.848\n", + " Average IL_recall = 0.796\n", + " Average IL_F1 = 0.821\n", + " Average IL_FPR = 0.005\n", + " Average IL_MCC = 0.816\n", + " Average cgF1 @[ IoU=0.50 ] = 0.692\n", + " Average precision @[ IoU=0.50 ] = 0.816\n", + " Average recall @[ IoU=0.50 ] = 0.759\n", + " Average F1 @[ IoU=0.50 ] = 0.786\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.981\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.848\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.962\n", + " Average cgF1 @[ IoU=0.75 ] = 0.680\n", + " Average precision @[ IoU=0.75 ] = 0.801\n", + " Average recall @[ IoU=0.75 ] = 0.745\n", + " Average F1 @[ IoU=0.75 ] = 0.772\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.965\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.833\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.945\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.6580192923425129, 'cgF1_eval_segm_precision': 0.7756659354606497, 'cgF1_eval_segm_recall': 0.721546369069409, 'cgF1_eval_segm_F1': 0.7475780939907699, 'cgF1_eval_segm_positive_macro_F1': 0.9345464754990648, 'cgF1_eval_segm_positive_micro_F1': 0.8066917804453706, 'cgF1_eval_segm_positive_micro_precision': 0.914747514495301, 'cgF1_eval_segm_IL_precision': 0.8477058323766221, 'cgF1_eval_segm_IL_recall': 0.7959997413454318, 'cgF1_eval_segm_IL_F1': 0.8210390273987034, 'cgF1_eval_segm_IL_FPR': 0.004764366701848471, 'cgF1_eval_segm_IL_MCC': 0.815701000423264, 'cgF1_eval_segm_cgF1@0.5': 0.6920242595075669, 'cgF1_eval_segm_precision@0.5': 0.81574816894191, 'cgF1_eval_segm_recall@0.5': 0.7588319951494304, 'cgF1_eval_segm_F1@0.5': 0.7862114792496716, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.9812705268789929, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.8483798096955603, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.9620167341118989, 'cgF1_eval_segm_cgF1@0.75': 0.6797028038245808, 'cgF1_eval_segm_precision@0.75': 0.8012246594574692, 'cgF1_eval_segm_recall@0.75': 0.7453218162752996, 'cgF1_eval_segm_F1@0.75': 0.7722129495022094, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.9650080958461289, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.8332744516334855, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.9448890718087305}\n", + "loading annotations into memory...\n", + "Done (t=3.67s)\n", + "creating index...\n", + "index created!\n", + "Loaded 53887 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1439027/1439027 [01:18<00:00, 18312.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.652\n", + " Average precision @[ IoU=0.50:0.95] = 0.769\n", + " Average recall @[ IoU=0.50:0.95] = 0.715\n", + " Average F1 @[ IoU=0.50:0.95] = 0.741\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.926\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.800\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.907\n", + " Average IL_precision = 0.848\n", + " Average IL_recall = 0.796\n", + " Average IL_F1 = 0.821\n", + " Average IL_FPR = 0.005\n", + " Average IL_MCC = 0.816\n", + " Average cgF1 @[ IoU=0.50 ] = 0.691\n", + " Average precision @[ IoU=0.50 ] = 0.815\n", + " Average recall @[ IoU=0.50 ] = 0.758\n", + " Average F1 @[ IoU=0.50 ] = 0.786\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.981\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.848\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.961\n", + " Average cgF1 @[ IoU=0.75 ] = 0.665\n", + " Average precision @[ IoU=0.75 ] = 0.784\n", + " Average recall @[ IoU=0.75 ] = 0.729\n", + " Average F1 @[ IoU=0.75 ] = 0.756\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.944\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.816\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.925\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.6524418175713689, 'cgF1_eval_bbox_precision': 0.7690916710456228, 'cgF1_eval_bbox_recall': 0.7154308025592622, 'cgF1_eval_bbox_F1': 0.741241468795898, 'cgF1_eval_bbox_positive_macro_F1': 0.9264232147142696, 'cgF1_eval_bbox_positive_micro_F1': 0.7998541343369929, 'cgF1_eval_bbox_positive_micro_precision': 0.906994444831736, 'cgF1_eval_bbox_IL_precision': 0.8477058323766221, 'cgF1_eval_bbox_IL_recall': 0.7959997413454318, 'cgF1_eval_bbox_IL_F1': 0.8210390273987034, 'cgF1_eval_bbox_IL_FPR': 0.004764366701848471, 'cgF1_eval_bbox_IL_MCC': 0.815701000423264, 'cgF1_eval_bbox_cgF1@0.5': 0.6914428818421255, 'cgF1_eval_bbox_precision@0.5': 0.8150628891945468, 'cgF1_eval_bbox_recall@0.5': 0.7581945285663284, 'cgF1_eval_bbox_F1@0.5': 0.7855509702356662, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.9805502412911423, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.8476670759056795, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.9612085795740021, 'cgF1_eval_bbox_cgF1@0.75': 0.6652433786643561, 'cgF1_eval_bbox_precision@0.75': 0.7841810889665958, 'cgF1_eval_bbox_recall@0.75': 0.7294674054504063, 'cgF1_eval_bbox_F1@0.75': 0.7557854511254823, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.944243447752961, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.8155480725402617, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.9247894863661997}\n", + "Processing subset: nga_art\n", + "loading annotations into memory...\n", + "Done (t=0.45s)\n", + "creating index...\n", + "index created!\n", + "Loaded 34558 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22221/22221 [00:04<00:00, 5095.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.381\n", + " Average precision @[ IoU=0.50:0.95] = 0.523\n", + " Average recall @[ IoU=0.50:0.95] = 0.512\n", + " Average F1 @[ IoU=0.50:0.95] = 0.517\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.754\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.576\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.659\n", + " Average IL_precision = 0.700\n", + " Average IL_recall = 0.809\n", + " Average IL_F1 = 0.750\n", + " Average IL_FPR = 0.118\n", + " Average IL_MCC = 0.661\n", + " Average cgF1 @[ IoU=0.50 ] = 0.435\n", + " Average precision @[ IoU=0.50 ] = 0.597\n", + " Average recall @[ IoU=0.50 ] = 0.585\n", + " Average F1 @[ IoU=0.50 ] = 0.591\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.838\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.658\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.753\n", + " Average cgF1 @[ IoU=0.75 ] = 0.399\n", + " Average precision @[ IoU=0.75 ] = 0.547\n", + " Average recall @[ IoU=0.75 ] = 0.536\n", + " Average F1 @[ IoU=0.75 ] = 0.542\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.781\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.603\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.690\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.38061980191685274, 'cgF1_eval_segm_precision': 0.5226448021036465, 'cgF1_eval_segm_recall': 0.5118585116882325, 'cgF1_eval_segm_F1': 0.5171454354933199, 'cgF1_eval_segm_positive_macro_F1': 0.7539468395539315, 'cgF1_eval_segm_positive_micro_F1': 0.576201025016698, 'cgF1_eval_segm_positive_micro_precision': 0.6591742420468004, 'cgF1_eval_segm_IL_precision': 0.6997999691183566, 'cgF1_eval_segm_IL_recall': 0.808677098006992, 'cgF1_eval_segm_IL_F1': 0.7503088318552136, 'cgF1_eval_segm_IL_FPR': 0.11755136469738198, 'cgF1_eval_segm_IL_MCC': 0.6605677279137476, 'cgF1_eval_segm_cgF1@0.5': 0.434669236088304, 'cgF1_eval_segm_precision@0.5': 0.5968559804098716, 'cgF1_eval_segm_recall@0.5': 0.5845381272236059, 'cgF1_eval_segm_F1@0.5': 0.5905828471879718, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8377112359789534, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.6580237237158217, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7527714557079914, 'cgF1_eval_segm_cgF1@0.75': 0.3986390156764444, 'cgF1_eval_segm_precision@0.75': 0.5473856178308225, 'cgF1_eval_segm_recall@0.75': 0.5360887289698226, 'cgF1_eval_segm_F1@0.75': 0.5416282897167458, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.78142131171764, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6034793993576627, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6903780508074329}\n", + "loading annotations into memory...\n", + "Done (t=0.32s)\n", + "creating index...\n", + "index created!\n", + "Loaded 34558 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22221/22221 [00:02<00:00, 7605.50it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.385\n", + " Average precision @[ IoU=0.50:0.95] = 0.528\n", + " Average recall @[ IoU=0.50:0.95] = 0.517\n", + " Average F1 @[ IoU=0.50:0.95] = 0.523\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.775\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.582\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.666\n", + " Average IL_precision = 0.700\n", + " Average IL_recall = 0.809\n", + " Average IL_F1 = 0.750\n", + " Average IL_FPR = 0.118\n", + " Average IL_MCC = 0.661\n", + " Average cgF1 @[ IoU=0.50 ] = 0.432\n", + " Average precision @[ IoU=0.50 ] = 0.594\n", + " Average recall @[ IoU=0.50 ] = 0.582\n", + " Average F1 @[ IoU=0.50 ] = 0.588\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.838\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.655\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.749\n", + " Average cgF1 @[ IoU=0.75 ] = 0.404\n", + " Average precision @[ IoU=0.75 ] = 0.555\n", + " Average recall @[ IoU=0.75 ] = 0.543\n", + " Average F1 @[ IoU=0.75 ] = 0.549\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.799\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.611\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.699\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.3846578129287702, 'cgF1_eval_bbox_precision': 0.528189088752597, 'cgF1_eval_bbox_recall': 0.5172883759116633, 'cgF1_eval_bbox_F1': 0.5226319143318613, 'cgF1_eval_bbox_positive_macro_F1': 0.7754605892309756, 'cgF1_eval_bbox_positive_micro_F1': 0.5823139652062992, 'cgF1_eval_bbox_positive_micro_precision': 0.6661668514342891, 'cgF1_eval_bbox_IL_precision': 0.6997999691183566, 'cgF1_eval_bbox_IL_recall': 0.808677098006992, 'cgF1_eval_bbox_IL_F1': 0.7503088318552136, 'cgF1_eval_bbox_IL_FPR': 0.11755136469738198, 'cgF1_eval_bbox_IL_MCC': 0.6605677279137476, 'cgF1_eval_bbox_cgF1@0.5': 0.4324122177242999, 'cgF1_eval_bbox_precision@0.5': 0.5937570397016169, 'cgF1_eval_bbox_recall@0.5': 0.5815031421393713, 'cgF1_eval_bbox_F1@0.5': 0.5875162177317533, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8378319170832961, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.6546069380197775, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.748862985013765, 'cgF1_eval_bbox_cgF1@0.75': 0.40385067625751253, 'cgF1_eval_bbox_precision@0.75': 0.5545413536480653, 'cgF1_eval_bbox_recall@0.75': 0.5430967854370555, 'cgF1_eval_bbox_F1@0.75': 0.5487094159071108, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.798977400359963, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6113690681392848, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6994030649559191}\n", + "Processing subset: sav\n", + "loading annotations into memory...\n", + "Done (t=0.46s)\n", + "creating index...\n", + "index created!\n", + "Loaded 74229 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 18079/18079 [00:05<00:00, 3149.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.444\n", + " Average precision @[ IoU=0.50:0.95] = 0.587\n", + " Average recall @[ IoU=0.50:0.95] = 0.684\n", + " Average F1 @[ IoU=0.50:0.95] = 0.632\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.768\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.660\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.639\n", + " Average IL_precision = 0.900\n", + " Average IL_recall = 0.908\n", + " Average IL_F1 = 0.904\n", + " Average IL_FPR = 0.241\n", + " Average IL_MCC = 0.672\n", + " Average cgF1 @[ IoU=0.50 ] = 0.503\n", + " Average precision @[ IoU=0.50 ] = 0.666\n", + " Average recall @[ IoU=0.50 ] = 0.776\n", + " Average F1 @[ IoU=0.50 ] = 0.717\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.846\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.750\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.725\n", + " Average cgF1 @[ IoU=0.75 ] = 0.470\n", + " Average precision @[ IoU=0.75 ] = 0.622\n", + " Average recall @[ IoU=0.75 ] = 0.725\n", + " Average F1 @[ IoU=0.75 ] = 0.669\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.806\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.700\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.677\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.44362320439319136, 'cgF1_eval_segm_precision': 0.5869346360044498, 'cgF1_eval_segm_recall': 0.6838706968595512, 'cgF1_eval_segm_F1': 0.6316558570754611, 'cgF1_eval_segm_positive_macro_F1': 0.7677480052550809, 'cgF1_eval_segm_positive_micro_F1': 0.66049478026234, 'cgF1_eval_segm_positive_micro_precision': 0.6387574946422351, 'cgF1_eval_segm_IL_precision': 0.8998911013612405, 'cgF1_eval_segm_IL_recall': 0.9082273511612319, 'cgF1_eval_segm_IL_F1': 0.9040395093174578, 'cgF1_eval_segm_IL_FPR': 0.24096611117001196, 'cgF1_eval_segm_IL_MCC': 0.671652854269325, 'cgF1_eval_segm_cgF1@0.5': 0.5034829560314773, 'cgF1_eval_segm_precision@0.5': 0.6661259526094324, 'cgF1_eval_segm_recall@0.5': 0.776140972883037, 'cgF1_eval_segm_F1@0.5': 0.7168878262296565, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8464809541363024, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7496178313411686, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7249409363562374, 'cgF1_eval_segm_cgF1@0.75': 0.47003419875527414, 'cgF1_eval_segm_precision@0.75': 0.6218749985165196, 'cgF1_eval_segm_recall@0.75': 0.7245816867958706, 'cgF1_eval_segm_F1@0.75': 0.669261443378069, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.8063026840071241, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6998171685976278, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6767828846107561}\n", + "loading annotations into memory...\n", + "Done (t=0.40s)\n", + "creating index...\n", + "index created!\n", + "Loaded 74229 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 18079/18079 [00:03<00:00, 4783.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.444\n", + " Average precision @[ IoU=0.50:0.95] = 0.588\n", + " Average recall @[ IoU=0.50:0.95] = 0.685\n", + " Average F1 @[ IoU=0.50:0.95] = 0.632\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.776\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.661\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.639\n", + " Average IL_precision = 0.900\n", + " Average IL_recall = 0.908\n", + " Average IL_F1 = 0.904\n", + " Average IL_FPR = 0.241\n", + " Average IL_MCC = 0.672\n", + " Average cgF1 @[ IoU=0.50 ] = 0.503\n", + " Average precision @[ IoU=0.50 ] = 0.665\n", + " Average recall @[ IoU=0.50 ] = 0.775\n", + " Average F1 @[ IoU=0.50 ] = 0.716\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.846\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.748\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.724\n", + " Average cgF1 @[ IoU=0.75 ] = 0.470\n", + " Average precision @[ IoU=0.75 ] = 0.622\n", + " Average recall @[ IoU=0.75 ] = 0.724\n", + " Average F1 @[ IoU=0.75 ] = 0.669\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.806\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.700\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.677\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.44413891029397917, 'cgF1_eval_bbox_precision': 0.5876168879112194, 'cgF1_eval_bbox_recall': 0.6846656270924854, 'cgF1_eval_bbox_F1': 0.6323901506230807, 'cgF1_eval_bbox_positive_macro_F1': 0.7758898702585046, 'cgF1_eval_bbox_positive_micro_F1': 0.6612625964005575, 'cgF1_eval_bbox_positive_micro_precision': 0.6394999853591734, 'cgF1_eval_bbox_IL_precision': 0.8998911013612405, 'cgF1_eval_bbox_IL_recall': 0.9082273511612319, 'cgF1_eval_bbox_IL_F1': 0.9040395093174578, 'cgF1_eval_bbox_IL_FPR': 0.24096611117001196, 'cgF1_eval_bbox_IL_MCC': 0.671652854269325, 'cgF1_eval_bbox_cgF1@0.5': 0.502671530652559, 'cgF1_eval_bbox_precision@0.5': 0.6650524793295505, 'cgF1_eval_bbox_recall@0.5': 0.7748902085305182, 'cgF1_eval_bbox_F1@0.5': 0.7157324692328706, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8464436229967108, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.74840972901009, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.723772681731684, 'cgF1_eval_bbox_cgF1@0.75': 0.4699260087048218, 'cgF1_eval_bbox_precision@0.75': 0.6217318687458687, 'cgF1_eval_bbox_recall@0.75': 0.7244149182155347, 'cgF1_eval_bbox_F1@0.75': 0.6691073957786058, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.806477309183606, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6996560882869217, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.6766271173274823}\n", + "Processing subset: yt1b\n", + "loading annotations into memory...\n", + "Done (t=0.15s)\n", + "creating index...\n", + "index created!\n", + "Loaded 23120 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7778/7778 [00:01<00:00, 4510.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.421\n", + " Average precision @[ IoU=0.50:0.95] = 0.545\n", + " Average recall @[ IoU=0.50:0.95] = 0.567\n", + " Average F1 @[ IoU=0.50:0.95] = 0.556\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.720\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.584\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.601\n", + " Average IL_precision = 0.853\n", + " Average IL_recall = 0.841\n", + " Average IL_F1 = 0.847\n", + " Average IL_FPR = 0.121\n", + " Average IL_MCC = 0.721\n", + " Average cgF1 @[ IoU=0.50 ] = 0.505\n", + " Average precision @[ IoU=0.50 ] = 0.655\n", + " Average recall @[ IoU=0.50 ] = 0.681\n", + " Average F1 @[ IoU=0.50 ] = 0.668\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.820\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.701\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.722\n", + " Average cgF1 @[ IoU=0.75 ] = 0.454\n", + " Average precision @[ IoU=0.75 ] = 0.588\n", + " Average recall @[ IoU=0.75 ] = 0.611\n", + " Average F1 @[ IoU=0.75 ] = 0.599\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.759\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.629\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.648\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.42073151404696596, 'cgF1_eval_segm_precision': 0.5453179724143051, 'cgF1_eval_segm_recall': 0.5669906233339933, 'cgF1_eval_segm_F1': 0.5558931826749454, 'cgF1_eval_segm_positive_macro_F1': 0.719514022577698, 'cgF1_eval_segm_positive_micro_F1': 0.5836034594081336, 'cgF1_eval_segm_positive_micro_precision': 0.6013252291429667, 'cgF1_eval_segm_IL_precision': 0.8525862066515557, 'cgF1_eval_segm_IL_recall': 0.8407480870386093, 'cgF1_eval_segm_IL_F1': 0.8466252666543157, 'cgF1_eval_segm_IL_FPR': 0.12073429039286084, 'cgF1_eval_segm_IL_MCC': 0.7209201852121546, 'cgF1_eval_segm_cgF1@0.5': 0.5052842773564472, 'cgF1_eval_segm_precision@0.5': 0.6548990097237606, 'cgF1_eval_segm_recall@0.5': 0.6809267556323607, 'cgF1_eval_segm_F1@0.5': 0.6676093380670313, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8203674808429026, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7008879591958582, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.7221608621188951, 'cgF1_eval_segm_cgF1@0.75': 0.4535093938858592, 'cgF1_eval_segm_precision@0.75': 0.587798359307308, 'cgF1_eval_segm_recall@0.75': 0.6111593143773055, 'cgF1_eval_segm_F1@0.75': 0.5992012730504395, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7591976776888013, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.629070184451, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.6481685933354631}\n", + "loading annotations into memory...\n", + "Done (t=0.11s)\n", + "creating index...\n", + "index created!\n", + "Loaded 23120 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7778/7778 [00:01<00:00, 6379.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.424\n", + " Average precision @[ IoU=0.50:0.95] = 0.549\n", + " Average recall @[ IoU=0.50:0.95] = 0.571\n", + " Average F1 @[ IoU=0.50:0.95] = 0.560\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.732\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.588\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.605\n", + " Average IL_precision = 0.853\n", + " Average IL_recall = 0.841\n", + " Average IL_F1 = 0.847\n", + " Average IL_FPR = 0.121\n", + " Average IL_MCC = 0.721\n", + " Average cgF1 @[ IoU=0.50 ] = 0.502\n", + " Average precision @[ IoU=0.50 ] = 0.651\n", + " Average recall @[ IoU=0.50 ] = 0.677\n", + " Average F1 @[ IoU=0.50 ] = 0.664\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.818\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.697\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.718\n", + " Average cgF1 @[ IoU=0.75 ] = 0.454\n", + " Average precision @[ IoU=0.75 ] = 0.588\n", + " Average recall @[ IoU=0.75 ] = 0.611\n", + " Average F1 @[ IoU=0.75 ] = 0.599\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.761\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.629\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.648\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.4236486808813284, 'cgF1_eval_bbox_precision': 0.549098643389262, 'cgF1_eval_bbox_recall': 0.5709215500614235, 'cgF1_eval_bbox_F1': 0.5597475175248535, 'cgF1_eval_bbox_positive_macro_F1': 0.7315482621439834, 'cgF1_eval_bbox_positive_micro_F1': 0.5876499085077717, 'cgF1_eval_bbox_positive_micro_precision': 0.605494196525914, 'cgF1_eval_bbox_IL_precision': 0.8525862066515557, 'cgF1_eval_bbox_IL_recall': 0.8407480870386093, 'cgF1_eval_bbox_IL_F1': 0.8466252666543157, 'cgF1_eval_bbox_IL_FPR': 0.12073429039286084, 'cgF1_eval_bbox_IL_MCC': 0.7209201852121546, 'cgF1_eval_bbox_cgF1@0.5': 0.5021932395356122, 'cgF1_eval_bbox_precision@0.5': 0.6508930007436738, 'cgF1_eval_bbox_recall@0.5': 0.6767615352589246, 'cgF1_eval_bbox_F1@0.5': 0.6635252744814799, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8180919678307201, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.69660033085053, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.7177434132363022, 'cgF1_eval_bbox_cgF1@0.75': 0.4535737905070497, 'cgF1_eval_bbox_precision@0.75': 0.5878818178277265, 'cgF1_eval_bbox_recall@0.75': 0.6112460898017521, 'cgF1_eval_bbox_F1@0.75': 0.5992863577083599, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.7609919097954506, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.629159510041421, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.648260623520517}\n", + "Processing subset: fathomnet\n", + "loading annotations into memory...\n", + "Done (t=0.82s)\n", + "creating index...\n", + "index created!\n", + "Loaded 25749 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 281205/281205 [00:14<00:00, 19028.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=segm\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.515\n", + " Average precision @[ IoU=0.50:0.95] = 0.472\n", + " Average recall @[ IoU=0.50:0.95] = 0.711\n", + " Average F1 @[ IoU=0.50:0.95] = 0.567\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.690\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.600\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.519\n", + " Average IL_precision = 0.839\n", + " Average IL_recall = 0.885\n", + " Average IL_F1 = 0.861\n", + " Average IL_FPR = 0.003\n", + " Average IL_MCC = 0.859\n", + " Average cgF1 @[ IoU=0.50 ] = 0.615\n", + " Average precision @[ IoU=0.50 ] = 0.564\n", + " Average recall @[ IoU=0.50 ] = 0.848\n", + " Average F1 @[ IoU=0.50 ] = 0.677\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.828\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.716\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.619\n", + " Average cgF1 @[ IoU=0.75 ] = 0.560\n", + " Average precision @[ IoU=0.75 ] = 0.513\n", + " Average recall @[ IoU=0.75 ] = 0.772\n", + " Average F1 @[ IoU=0.75 ] = 0.617\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.750\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.652\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.564\n", + "\n", + "{'cgF1_eval_segm_cgF1': 0.5152972059816302, 'cgF1_eval_segm_precision': 0.47227706231315125, 'cgF1_eval_segm_recall': 0.7107059458476863, 'cgF1_eval_segm_F1': 0.5674160042153165, 'cgF1_eval_segm_positive_macro_F1': 0.6904954435936812, 'cgF1_eval_segm_positive_micro_F1': 0.5997854205638585, 'cgF1_eval_segm_positive_micro_precision': 0.5188866930899365, 'cgF1_eval_segm_IL_precision': 0.8390254059060754, 'cgF1_eval_segm_IL_recall': 0.8845225025500498, 'cgF1_eval_segm_IL_F1': 0.8611729531944466, 'cgF1_eval_segm_IL_FPR': 0.0027941442255456565, 'cgF1_eval_segm_IL_MCC': 0.8591359314756251, 'cgF1_eval_segm_cgF1@0.5': 0.6149429060698316, 'cgF1_eval_segm_precision@0.5': 0.5635963077131737, 'cgF1_eval_segm_recall@0.5': 0.8481276752838837, 'cgF1_eval_segm_F1@0.5': 0.6771405387867684, 'cgF1_eval_segm_positive_macro_F1@0.5': 0.8277623237104728, 'cgF1_eval_segm_positive_micro_F1@0.5': 0.7157690460153667, 'cgF1_eval_segm_positive_micro_precision@0.5': 0.6192183522838083, 'cgF1_eval_segm_cgF1@0.75': 0.5599254460996396, 'cgF1_eval_segm_precision@0.75': 0.5131761400573049, 'cgF1_eval_segm_recall@0.75': 0.7722529064180121, 'cgF1_eval_segm_F1@0.75': 0.6165582438611041, 'cgF1_eval_segm_positive_macro_F1@0.75': 0.7496616653080356, 'cgF1_eval_segm_positive_micro_F1@0.75': 0.6517309142663014, 'cgF1_eval_segm_positive_micro_precision@0.75': 0.5638221534257606}\n", + "loading annotations into memory...\n", + "Done (t=0.79s)\n", + "creating index...\n", + "index created!\n", + "Loaded 25749 predictions\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 281205/281205 [00:14<00:00, 19965.61it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accumulating results\n", + "cgF1 metric, IoU type=bbox\n", + " Average cgF1 @[ IoU=0.50:0.95] = 0.540\n", + " Average precision @[ IoU=0.50:0.95] = 0.495\n", + " Average recall @[ IoU=0.50:0.95] = 0.745\n", + " Average F1 @[ IoU=0.50:0.95] = 0.595\n", + " Average positive_macro_F1 @[ IoU=0.50:0.95] = 0.747\n", + " Average positive_micro_F1 @[ IoU=0.50:0.95] = 0.629\n", + " Average positive_micro_precision @[ IoU=0.50:0.95] = 0.544\n", + " Average IL_precision = 0.839\n", + " Average IL_recall = 0.885\n", + " Average IL_F1 = 0.861\n", + " Average IL_FPR = 0.003\n", + " Average IL_MCC = 0.859\n", + " Average cgF1 @[ IoU=0.50 ] = 0.620\n", + " Average precision @[ IoU=0.50 ] = 0.568\n", + " Average recall @[ IoU=0.50 ] = 0.855\n", + " Average F1 @[ IoU=0.50 ] = 0.683\n", + " Average positive_macro_F1 @[ IoU=0.50 ] = 0.842\n", + " Average positive_micro_F1 @[ IoU=0.50 ] = 0.722\n", + " Average positive_micro_precision @[ IoU=0.50 ] = 0.625\n", + " Average cgF1 @[ IoU=0.75 ] = 0.577\n", + " Average precision @[ IoU=0.75 ] = 0.528\n", + " Average recall @[ IoU=0.75 ] = 0.795\n", + " Average F1 @[ IoU=0.75 ] = 0.635\n", + " Average positive_macro_F1 @[ IoU=0.75 ] = 0.786\n", + " Average positive_micro_F1 @[ IoU=0.75 ] = 0.671\n", + " Average positive_micro_precision @[ IoU=0.75 ] = 0.581\n", + "\n", + "{'cgF1_eval_bbox_cgF1': 0.5404467727790648, 'cgF1_eval_bbox_precision': 0.4953251162982368, 'cgF1_eval_bbox_recall': 0.7453898005477004, 'cgF1_eval_bbox_F1': 0.5951093668775386, 'cgF1_eval_bbox_positive_macro_F1': 0.7472057384312792, 'cgF1_eval_bbox_positive_micro_F1': 0.6290585144667509, 'cgF1_eval_bbox_positive_micro_precision': 0.5442093891698694, 'cgF1_eval_bbox_IL_precision': 0.8390254059060754, 'cgF1_eval_bbox_IL_recall': 0.8845225025500498, 'cgF1_eval_bbox_IL_F1': 0.8611729531944466, 'cgF1_eval_bbox_IL_FPR': 0.0027941442255456565, 'cgF1_eval_bbox_IL_MCC': 0.8591359314756251, 'cgF1_eval_bbox_cgF1@0.5': 0.6201953820242813, 'cgF1_eval_bbox_precision@0.5': 0.5684098835897048, 'cgF1_eval_bbox_recall@0.5': 0.8553713829911432, 'cgF1_eval_bbox_F1@0.5': 0.6829242853928004, 'cgF1_eval_bbox_positive_macro_F1@0.5': 0.8423905574972759, 'cgF1_eval_bbox_positive_micro_F1@0.5': 0.7218827187905563, 'cgF1_eval_bbox_positive_micro_precision@0.5': 0.6245069861553695, 'cgF1_eval_bbox_cgF1@0.75': 0.5765731241108606, 'cgF1_eval_bbox_precision@0.75': 0.5284327280049546, 'cgF1_eval_bbox_recall@0.75': 0.7952117766088179, 'cgF1_eval_bbox_F1@0.75': 0.6348897796975862, 'cgF1_eval_bbox_positive_macro_F1@0.75': 0.786060071093738, 'cgF1_eval_bbox_positive_micro_F1@0.75': 0.6711081483003005, 'cgF1_eval_bbox_positive_micro_precision@0.75': 0.5805844336627427}\n" + ] + } + ], + "source": [ + "results_silver = {}\n", + "results_silver_bbox = {}\n", + "\n", + "for subset_name, gt in saco_silver_gts.items():\n", + " print(\"Processing subset: \", subset_name)\n", + " gt_path = os.path.join(GT_DIR, gt)\n", + " pred_path = os.path.join(PRED_DIR, f\"silver_{subset_name}/dumps/silver_{subset_name}/coco_predictions_segm.json\")\n", + " \n", + " evaluator = CGF1Evaluator(gt_path=gt_path, verbose=True, iou_type=\"segm\") \n", + " summary = evaluator.evaluate(pred_path)\n", + " print(summary)\n", + "\n", + " cur_results = {}\n", + " cur_results[\"cgf1\"] = summary[\"cgF1_eval_segm_cgF1\"] * 100\n", + " cur_results[\"il_mcc\"] = summary[\"cgF1_eval_segm_IL_MCC\"]\n", + " cur_results[\"pmf1\"] = summary[\"cgF1_eval_segm_positive_micro_F1\"] * 100\n", + " results_silver[subset_name] = cur_results\n", + "\n", + " # Also eval bbox \n", + " evaluator = CGF1Evaluator(gt_path=gt_path, verbose=True, iou_type=\"bbox\") \n", + " summary = evaluator.evaluate(pred_path)\n", + " print(summary)\n", + "\n", + " cur_results = {}\n", + " cur_results[\"cgf1\"] = summary[\"cgF1_eval_bbox_cgF1\"] * 100\n", + " cur_results[\"il_mcc\"] = summary[\"cgF1_eval_bbox_IL_MCC\"]\n", + " cur_results[\"pmf1\"] = summary[\"cgF1_eval_bbox_positive_micro_F1\"] * 100\n", + " results_silver_bbox[subset_name] = cur_results\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f90e949a-05ad-46e4-9b97-78b36a5f8c65", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute averages\n", + "METRICS = [\"cgf1\", \"il_mcc\", \"pmf1\"]\n", + "avg_stats, avg_stats_bbox = {}, {}\n", + "for key in METRICS:\n", + " avg_stats[key] = sum(res[key] for res in results_silver.values()) / len(results_silver)\n", + " avg_stats_bbox[key] = sum(res[key] for res in results_silver_bbox.values()) / len(results_silver_bbox)\n", + "results_silver[\"Average\"] = avg_stats\n", + "results_silver_bbox[\"Average\"] = avg_stats_bbox" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "791eefb4-5e36-4dc0-9f26-2870355a7997", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pretty print segmentation results\n", + "from IPython.display import HTML, display\n", + "\n", + "row1, row2, row3 = \"\", \"\", \"\"\n", + "for subset in results_silver:\n", + " row1 += f'{subset}'\n", + " row2 += \"\" + \"\".join(METRICS) + \"\"\n", + " row3 += \"\" + \"\".join([str(round(results_silver[subset][k], 2)) for k in METRICS]) + \"\"\n", + "\n", + "display(HTML(\n", + " f\"{row1}{row2}{row3}
\"\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9250c2da-fe9a-4c13-be32-edb54748440e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Pretty print bbox detection results\n", + "from IPython.display import HTML, display\n", + "\n", + "row1, row2, row3 = \"\", \"\", \"\"\n", + "for subset in results_silver_bbox:\n", + " row1 += f'{subset}'\n", + " row2 += \"\" + \"\".join(METRICS) + \"\"\n", + " row3 += \"\" + \"\".join([str(round(results_silver_bbox[subset][k], 2)) for k in METRICS]) + \"\"\n", + "\n", + "display(HTML(\n", + " f\"{row1}{row2}{row3}
\"\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9059d9e2-ce61-42f9-9b12-3c61b3bf75bb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}