only keep quantized models in this repo + renamed face_detection_model to ssd_mobilenetv1_model

.gitignore

 node_modules
 .rpt2_cache
 .env*
-tmp
\ No newline at end of file
+tmp
+weights_uncompressed
\ No newline at end of file

karma.conf.js

@@ -3,7 +3,8 @@ const dataFiles = [
   'test/images/*.png',
   'test/data/*.json',
   'test/media/*.mp4',
-  'weights/**/*'
+  'weights/**/*',
+  'weights_uncompressed/**/*'
 ].map(pattern => ({
   pattern,
   watched: false,

src/faceDetectionNet/loadQuantizedParams.ts

@@ -7,7 +7,7 @@ import { loadWeightMap } from '../commons/loadWeightMap';
 import { ConvParams, ParamMapping } from '../commons/types';
 import { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
 
-const DEFAULT_MODEL_NAME = 'face_detection_model'
+const DEFAULT_MODEL_NAME = 'ssd_mobilenetv1_model'
 
 function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
 

test/tests/e2e/expectedResults.ts

 import * as faceapi from '../../../src';
-import { FaceLandmarks5 } from '../../../src/mtcnn/FaceLandmarks5';
 import { Point } from '../../../src/Point';
 import { expectMaxDelta, expectPointClose, expectRectClose } from '../../utils';
 

test/tests/e2e/faceDetectionNet.test.ts

@@ -79,7 +79,7 @@ describe('faceDetectionNet', () => {
 
       it('disposes all param tensors', async () => {
         await expectAllTensorsReleased(async () => {
-          const res = await fetch('base/weights/uncompressed/face_detection_model.weights')
+          const res = await fetch('base/weights_uncompressed/ssd_mobilenetv1_model.weights')
           const weights = new Float32Array(await res.arrayBuffer())
           const net = faceapi.createFaceDetectionNet(weights)
           net.dispose()

test/tests/e2e/faceLandmarkNet.test.ts

@@ -211,7 +211,7 @@ describe('faceLandmarkNet', () => {
 
       it('disposes all param tensors', async () => {
         await expectAllTensorsReleased(async () => {
-          const res = await fetch('base/weights/uncompressed/face_landmark_68_model.weights')
+          const res = await fetch('base/weights_uncompressed/face_landmark_68_model.weights')
           const weights = new Float32Array(await res.arrayBuffer())
           const net = faceapi.createFaceLandmarkNet(weights)
           net.dispose()

test/tests/e2e/faceRecognitionNet.test.ts

@@ -139,7 +139,7 @@ describe('faceRecognitionNet', () => {
 
       it('disposes all param tensors', async () => {
         await expectAllTensorsReleased(async () => {
-          const res = await fetch('base/weights/uncompressed/face_recognition_model.weights')
+          const res = await fetch('base/weights_uncompressed/face_recognition_model.weights')
           const weights = new Float32Array(await res.arrayBuffer())
           const net = faceapi.createFaceRecognitionNet(weights)
           net.dispose()

test/tests/e2e/mtcnn.test.ts

@@ -67,7 +67,7 @@ describe('mtcnn', () => {
 
       it('disposes all param tensors', async () => {
         await expectAllTensorsReleased(async () => {
-          const res = await fetch('base/weights/uncompressed/mtcnn_model.weights')
+          const res = await fetch('base/weights_uncompressed/mtcnn_model.weights')
           const weights = new Float32Array(await res.arrayBuffer())
           const net = faceapi.createMtcnn(weights)
           net.dispose()

test/utils.ts

@@ -83,7 +83,7 @@ async function initNet<TNet extends NeuralNetwork<any>>(
 ) {
   await net.load(
     uncompressedFilename
-      ? await loadNetWeights(`base/weights/uncompressed/${uncompressedFilename}`)
+      ? await loadNetWeights(`base/weights_uncompressed/${uncompressedFilename}`)
       : 'base/weights'
   )
 }
@@ -115,7 +115,7 @@ export function describeWithNets(
       if (withFaceDetectionNet || withAllFaces) {
         await initNet<faceapi.FaceDetectionNet>(
           faceDetectionNet,
-          !!withFaceDetectionNet && !withFaceDetectionNet.quantized && 'face_detection_model.weights'
+          !!withFaceDetectionNet && !withFaceDetectionNet.quantized && 'ssd_mobilenetv1_model.weights'
         )
       }
       if (withFaceLandmarkNet || withAllFaces) {

weights/face_detection_model-weights_manifest.json

-[{"paths":["face_detection_model-shard1","face_detection_model-shard2"],"weights":[{"dtype":"float32","shape":[1,1,512,9],"quantization":{"scale":0.0026856216729856004,"min":-0.34107395246917127,"dtype":"uint8"},"name":"Prediction/BoxPredictor_0/ClassPredictor/weights"},{"dtype":"float32","shape":[9],"quantization":{"scale":0.00198518248165355,"min":-0.32159956202787515,"dtype":"uint8"},"name":"Prediction/BoxPredictor_0/ClassPredictor/biases"},{"dtype":"float32","shape":[1,1,1024,18],"quantization":{"scale":0.003060340296988394,"min":-0.489654447518143,"dtype":"uint8"},"name":"Prediction/BoxPredictor_1/ClassPredictor/weights"},{"dtype":"float32","shape":[18],"quantization":{"scale":0.0008040678851744708,"min":-0.12221831854651957,"dtype":"uint8"},"name":"Prediction/BoxPredictor_1/ClassPredictor/biases"},{"dtype":"float32","shape":[1,1,512,18],"quantization":{"scale":0.0012513800578958848,"min":-0.16017664741067325,"dtype":"uint8"},"name":"Prediction/BoxPredictor_2/ClassPredictor/weights"},{"dtype":"float32","shape":[18],"quantization":{"scale":0.000338070518245884,"min":-0.05510549447407909,"dtype":"uint8"},"name":"Prediction/BoxPredictor_2/ClassPredictor/biases"},{"dtype":"float32","shape":[1,1,256,18],"quantization":{"scale":0.0011819932975021064,"min":-0.1453851755927591,"dtype":"uint8"},"name":"Prediction/BoxPredictor_3/ClassPredictor/weights"},{"dtype":"float32","shape":[18],"quantization":{"scale":0.00015985782386041154,"min":-0.026536398760828316,"dtype":"uint8"},"name":"Prediction/BoxPredictor_3/ClassPredictor/biases"},{"dtype":"float32","shape":[1,1,256,18],"quantization":{"scale":0.0007035591438704846,"min":-0.08513065640832863,"dtype":"uint8"},"name":"Prediction/BoxPredictor_4/ClassPredictor/weights"},{"dtype":"float32","shape":[18],"quantization":{"scale":0.00008793946574716008,"min":-0.013190919862074012,"dtype":"uint8"},"name":"Prediction/BoxPredictor_4/ClassPredictor/biases"},{"dtype":"float32","shape":[1,1,128,18],"quantization":{"scale":0.00081320781918133,"min":-0.11059626340866088,"dtype":"uint8"},"name":"Prediction/BoxPredictor_5/ClassPredictor/weights"},{"dtype":"float32","shape":[18],"quantization":{"scale":0.0000980533805547976,"min":-0.014609953702664841,"dtype":"uint8"},"name":"Prediction/BoxPredictor_5/ClassPredictor/biases"},{"dtype":"int32","shape":[],"quantization":{"scale":1,"min":3,"dtype":"uint8"},"name":"Prediction/BoxPredictor_0/stack_1/2"},{"dtype":"int32","shape":[3],"quantization":{"scale":0.00392156862745098,"min":0,"dtype":"uint8"},"name":"Postprocessor/Slice/begin"},{"dtype":"int32","shape":[3],"quantization":{"scale":1,"min":-1,"dtype":"uint8"},"name":"Postprocessor/Slice/size"},{"dtype":"float32","shape":[1,1,512,12],"quantization":{"scale":0.003730384859384275,"min":-0.4327246436885759,"dtype":"uint8"},"name":"Prediction/BoxPredictor_0/BoxEncodingPredictor/weights"},{"dtype":"float32","shape":[12],"quantization":{"scale":0.0018744708568442102,"min":-0.3917644090804399,"dtype":"uint8"},"name":"Prediction/BoxPredictor_0/BoxEncodingPredictor/biases"},{"dtype":"int32","shape":[],"quantization":{"scale":1,"min":3072,"dtype":"uint8"},"name":"Prediction/BoxPredictor_0/stack_1/1"},{"dtype":"float32","shape":[1,1,1024,24],"quantization":{"scale":0.00157488017689948,"min":-0.20000978246623397,"dtype":"uint8"},"name":"Prediction/BoxPredictor_1/BoxEncodingPredictor/weights"},{"dtype":"float32","shape":[24],"quantization":{"scale":0.0002823906713256649,"min":-0.043488163384152394,"dtype":"uint8"},"name":"Prediction/BoxPredictor_1/BoxEncodingPredictor/biases"},{"dtype":"int32","shape":[],"quantization":{"scale":1,"min":1536,"dtype":"uint8"},"name":"Prediction/BoxPredictor_1/stack_1/1"},{"dtype":"float32","shape":[1,1,512,24],"quantization":{"scale":0.0007974451663447361,"min":-0.11004743295557358,"dtype":"uint8"},"name":"Prediction/BoxPredictor_2/BoxEncodingPredictor/weights"},{"dtype":"float32","shape":[24],"quantization":{"scale":0.0001350417988849621,"min":-0.02039131163162928,"dtype":"uint8"},"name":"Prediction/BoxPredictor_2/BoxEncodingPredictor/biases"},{"dtype":"int32","shape":[],"quantization":{"scale":1,"min":384,"dtype":"uint8"},"name":"Prediction/BoxPredictor_2/stack_1/1"},{"dtype":"float32","shape":[1,1,256,24],"quantization":{"scale":0.0007113990246080885,"min":-0.0860792819775787,"dtype":"uint8"},"name":"Prediction/BoxPredictor_3/BoxEncodingPredictor/weights"},{"dtype":"float32","shape":[24],"quantization":{"scale":0.000050115815418608046,"min":-0.007617603943628423,"dtype":"uint8"},"name":"Prediction/BoxPredictor_3/BoxEncodingPredictor/biases"},{"dtype":"int32","shape":[],"quantization":{"scale":1,"min":96,"dtype":"uint8"},"name":"Prediction/BoxPredictor_3/stack_1/1"},{"dtype":"float32","shape":[1,1,256,24],"quantization":{"scale":0.000590049314732645,"min":-0.06903576982371946,"dtype":"uint8"},"name":"Prediction/BoxPredictor_4/BoxEncodingPredictor/weights"},{"dtype":"float32","shape":[24],"quantization":{"scale":0.00003513663861097074,"min":-0.006359731588585704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