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

.gitignore

@@ -2,3 +2,4 @@ node_modules
 .rpt2_cache
 .env*
 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) {