diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4918e24b..cb445bf4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,7 +9,7 @@
   
 ## Changelog
   
-### **HEAD -> main** 2022/10/28 mandic00@live.com
+### **HEAD -> main** 2022/11/04 mandic00@live.com
 
 - add named exports
 - add draw label templates
diff --git a/TODO.md b/TODO.md
index ca9bc1dd..f9914cb4 100644
--- a/TODO.md
+++ b/TODO.md
@@ -51,10 +51,29 @@ No support for running in **web workers** as Safari still does not support `Offs
 
 ## Pending Release Changes
 
+Optimizations:
+- Enabled high-resolution optimizations  
+  Internal limits are increased from **2k** to **4k**
+- Enhanced device capabilities detection  
+  See `human.env.[agent, wasm, webgl, webgpu]` for details  
+- If `config.backend` is not set, Human will auto-select best backend  
+  based on device capabilities  
+- Enhanced support for `webgpu`  
+
+Features:
+- Add [draw label templates](https://github.com/vladmandic/human/wiki/Draw)  
+- Add `config.filter.autoBrightness` (*enabled by default*)  
+  Per-frame video on-the-fly brightness adjustments  
+  Which significantly increases performance and precision in poorly lit scenes  
+- Improved `config.filter.equalization` (*disabled by default*)  
+  Image and video on-demand histogram equalization  
+
+Architecture:
 - Upgrade to TFJS 4.0 with **strong typing**  
   see [notes](https://github.com/vladmandic/human#typedefs) on how to use  
 - `TypeDef` refactoring  
-- Add [draw label templates](https://github.com/vladmandic/human/wiki/Draw)  
 - Reduce build dependencies  
   `Human` is now 30% smaller :)  
+  As usual, `Human` has **zero** runtime dependencies,  
+  all *devDependencies* are only to rebuild `Human` itself
 - Add named export for improved bundler support when using non-default imports  
diff --git a/demo/typescript/index.js b/demo/typescript/index.js
index 8a067242..d3a60185 100644
--- a/demo/typescript/index.js
+++ b/demo/typescript/index.js
@@ -4,6 +4,6 @@
   author: <https://github.com/vladmandic>'
 */
 
-import*as m from"../../dist/human.esm.js";var w={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!0},hand:{enabled:!0},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(w);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+`
-`,console.log(...t)},d=t=>a.fps.innerText=t,v=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function u(){var i,l,c;if(!a.video.paused){let r=e.next(e.result);e.config.filter.flip?e.draw.canvas(r.canvas,a.canvas):e.draw.canvas(a.video,a.canvas);let p={bodyLabels:`person confidence [score] and ${(c=(l=(i=e.result)==null?void 0:i.body)==null?void 0:l[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,r,p),v(r.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,d(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(u,30)}async function g(){await e.webcam.start({element:a.video,crop:!0}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function b(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),d("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.getModelStats()),o("models loaded:",Object.values(e.models).filter(t=>t!==null).length),o("environment",e.env),d("initializing..."),await e.warmup(),await g(),await f(),await u()}window.onload=b;
+import*as m from"../../dist/human.esm.js";var f=1920,g={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1,width:f},face:{enabled:!0,detector:{rotation:!0},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!0},hand:{enabled:!0},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(g);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},o={detectFPS:0,drawFPS:0,frames:0,averageMs:0},s=(...t)=>{a.log.innerText+=t.join(" ")+`
+`,console.log(...t)},r=t=>a.fps.innerText=t,b=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function u(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&s("allocated tensors:",t-n.tensors),n.tensors=t,o.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,o.frames++,o.averageMs=Math.round(1e3*(e.now()-n.start)/o.frames)/1e3,o.frames%100===0&&!a.video.paused&&s("performance",{...o,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(u)}async function p(){var d,i,l;if(!a.video.paused){let c=e.next(e.result),w=await e.image(a.video);e.draw.canvas(w.canvas,a.canvas);let v={bodyLabels:`person confidence [score] and ${(l=(i=(d=e.result)==null?void 0:d.body)==null?void 0:i[0])==null?void 0:l.keypoints.length} keypoints`};await e.draw.all(a.canvas,c,v),b(c.performance)}let t=e.now();o.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,r(a.video.paused?"paused":`fps: ${o.detectFPS.toFixed(1).padStart(5," ")} detect | ${o.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(p,30)}async function h(){await e.webcam.start({element:a.video,crop:!0,width:f}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function y(){s("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),s("platform:",e.env.platform,"| agent:",e.env.agent),r("loading..."),await e.load(),s("backend:",e.tf.getBackend(),"| available:",e.env.backends),s("models stats:",e.getModelStats()),s("models loaded:",Object.values(e.models).filter(t=>t!==null).length),s("environment",e.env),r("initializing..."),await e.warmup(),await h(),await u(),await p()}window.onload=y;
 //# sourceMappingURL=index.js.map
diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map
index f0d25ada..9dc875a2 100644
--- a/demo/typescript/index.js.map
+++ b/demo/typescript/index.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["index.ts"],
-  "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior\n  // backend: 'wasm',\n  modelBasePath: '../../models',\n  filter: { enabled: true, equalization: false, flip: false },\n  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n  body: { enabled: true },\n  hand: { enabled: true },\n  object: { enabled: false },\n  segmentation: { enabled: false },\n  gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n  video: document.getElementById('video') as HTMLVideoElement,\n  canvas: document.getElementById('canvas') as HTMLCanvasElement,\n  log: document.getElementById('log') as HTMLPreElement,\n  fps: document.getElementById('status') as HTMLPreElement,\n  perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n  dom.log.innerText += msg.join(' ') + '\\n';\n  console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    if (timestamp.start === 0) timestamp.start = human.now();\n    // log('profiling data:', await human.profile(dom.video));\n    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n    timestamp.tensors = tensors;\n    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n    fps.frames++;\n    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n    if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n  }\n  timestamp.detect = human.now();\n  requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n  if (!dom.video.paused) {\n    const interpolated = human.next(human.result); // smoothen result using last-known results\n    if (human.config.filter.flip) human.draw.canvas(interpolated.canvas as HTMLCanvasElement, dom.canvas); // draw processed image to screen canvas\n    else human.draw.canvas(dom.video, dom.canvas); // draw original video to screen canvas // better than using procesed image as this loop happens faster than processing loop\n\n    const opt: Partial<H.DrawOptions> = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n    await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n    perf(interpolated.performance); // write performance data\n  }\n  const now = human.now();\n  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n  timestamp.draw = now;\n  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n  await human.webcam.start({ element: dom.video, crop: true }); // use human webcam helper methods and associate webcam stream with a dom element\n  dom.canvas.width = human.webcam.width;\n  dom.canvas.height = human.webcam.height;\n  dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n    if (human.webcam.paused) await human.webcam.play();\n    else human.webcam.pause();\n  };\n}\n\nasync function main() { // main entry point\n  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n  log('platform:', human.env.platform, '| agent:', human.env.agent);\n  status('loading...');\n  await human.load(); // preload all models\n  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n  log('models stats:', human.getModelStats());\n  log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);\n  log('environment', human.env);\n  status('initializing...');\n  await human.warmup(); // warmup function to initialize backend for future faster detection\n  await webCam(); // start webcam\n  await detectionLoop(); // start detection loop\n  await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"],
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-  "names": ["H", "humanConfig", "human", "dom", "timestamp", "fps", "log", "msg", "status", "perf", "detectionLoop", "tensors", "drawLoop", "_a", "_b", "_c", "interpolated", "opt", "now", "webCam", "main", "model"]
+  "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior\n  // backend: 'webgpu',\n  modelBasePath: '../../models',\n  filter: { enabled: true, equalization: false, flip: false, width },\n  face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n  body: { enabled: true },\n  hand: { enabled: true },\n  object: { enabled: false },\n  segmentation: { enabled: false },\n  gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n  video: document.getElementById('video') as HTMLVideoElement,\n  canvas: document.getElementById('canvas') as HTMLCanvasElement,\n  log: document.getElementById('log') as HTMLPreElement,\n  fps: document.getElementById('status') as HTMLPreElement,\n  perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n  dom.log.innerText += msg.join(' ') + '\\n';\n  console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    if (timestamp.start === 0) timestamp.start = human.now();\n    // log('profiling data:', await human.profile(dom.video));\n    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n    timestamp.tensors = tensors;\n    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n    fps.frames++;\n    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n    if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n  }\n  timestamp.detect = human.now();\n  requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n  if (!dom.video.paused) {\n    const interpolated = human.next(human.result); // smoothen result using last-known results\n    const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n    human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n    const opt: Partial<H.DrawOptions> = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n    await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n    perf(interpolated.performance); // write performance data\n  }\n  const now = human.now();\n  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n  timestamp.draw = now;\n  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n  await human.webcam.start({ element: dom.video, crop: true, width }); // use human webcam helper methods and associate webcam stream with a dom element\n  dom.canvas.width = human.webcam.width;\n  dom.canvas.height = human.webcam.height;\n  dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n    if (human.webcam.paused) await human.webcam.play();\n    else human.webcam.pause();\n  };\n}\n\nasync function main() { // main entry point\n  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n  log('platform:', human.env.platform, '| agent:', human.env.agent);\n  status('loading...');\n  await human.load(); // preload all models\n  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n  log('models stats:', human.getModelStats());\n  log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);\n  log('environment', human.env);\n  status('initializing...');\n  await human.warmup(); // warmup function to initialize backend for future faster detection\n  await webCam(); // start webcam\n  await detectionLoop(); // start detection loop\n  await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"],
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+  "names": ["H", "width", "humanConfig", "human", "dom", "timestamp", "fps", "log", "msg", "status", "perf", "detectionLoop", "tensors", "drawLoop", "_a", "_b", "_c", "interpolated", "processed", "opt", "now", "webCam", "main", "model"]
 }
diff --git a/demo/typescript/index.ts b/demo/typescript/index.ts
index 0699b9cf..a127491d 100644
--- a/demo/typescript/index.ts
+++ b/demo/typescript/index.ts
@@ -9,11 +9,13 @@
 
 import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
 
+const width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations
+
 const humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior
-  // backend: 'wasm',
+  // backend: 'webgpu',
   modelBasePath: '../../models',
-  filter: { enabled: true, equalization: false, flip: false },
-  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },
+  filter: { enabled: true, equalization: false, flip: false, width },
+  face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },
   body: { enabled: true },
   hand: { enabled: true },
   object: { enabled: false },
@@ -65,8 +67,8 @@ async function detectionLoop() { // main detection loop
 async function drawLoop() { // main screen refresh loop
   if (!dom.video.paused) {
     const interpolated = human.next(human.result); // smoothen result using last-known results
-    if (human.config.filter.flip) human.draw.canvas(interpolated.canvas as HTMLCanvasElement, dom.canvas); // draw processed image to screen canvas
-    else human.draw.canvas(dom.video, dom.canvas); // draw original video to screen canvas // better than using procesed image as this loop happens faster than processing loop
+    const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters
+    human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);
 
     const opt: Partial<H.DrawOptions> = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };
     await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.
@@ -80,7 +82,7 @@ async function drawLoop() { // main screen refresh loop
 }
 
 async function webCam() {
-  await human.webcam.start({ element: dom.video, crop: true }); // use human webcam helper methods and associate webcam stream with a dom element
+  await human.webcam.start({ element: dom.video, crop: true, width }); // use human webcam helper methods and associate webcam stream with a dom element
   dom.canvas.width = human.webcam.width;
   dom.canvas.height = human.webcam.height;
   dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click
diff --git a/package.json b/package.json
index b3633a18..41087844 100644
--- a/package.json
+++ b/package.json
@@ -74,7 +74,7 @@
   "devDependencies": {
     "@html-eslint/eslint-plugin": "^0.15.0",
     "@html-eslint/parser": "^0.15.0",
-    "@microsoft/api-extractor": "^7.33.5",
+    "@microsoft/api-extractor": "^7.33.6",
     "@tensorflow/tfjs-backend-cpu": "^4.0.0",
     "@tensorflow/tfjs-backend-wasm": "^4.0.0",
     "@tensorflow/tfjs-backend-webgl": "^4.0.0",
@@ -85,14 +85,14 @@
     "@tensorflow/tfjs-node-gpu": "^4.0.0",
     "@types/node": "^18.11.9",
     "@types/offscreencanvas": "^2019.7.0",
-    "@typescript-eslint/eslint-plugin": "^5.42.0",
-    "@typescript-eslint/parser": "^5.42.0",
+    "@typescript-eslint/eslint-plugin": "^5.42.1",
+    "@typescript-eslint/parser": "^5.42.1",
     "@vladmandic/build": "^0.7.14",
     "@vladmandic/pilogger": "^0.4.6",
     "@vladmandic/tfjs": "github:vladmandic/tfjs",
     "canvas": "^2.10.2",
     "esbuild": "^0.15.13",
-    "eslint": "8.26.0",
+    "eslint": "8.27.0",
     "eslint-config-airbnb-base": "^15.0.0",
     "eslint-plugin-html": "^7.1.0",
     "eslint-plugin-import": "^2.26.0",
diff --git a/src/config.ts b/src/config.ts
index 08ff32c8..3dd4909d 100644
--- a/src/config.ts
+++ b/src/config.ts
@@ -186,6 +186,8 @@ export interface FilterConfig {
   return: boolean,
   /** flip input as mirror image */
   flip: boolean,
+  /** apply auto-brighness */
+  autoBrightness: boolean,
   /** range: -1 (darken) to 1 (lighten) */
   brightness: number,
   /** range: -1 (reduce contrast) to 1 (increase contrast) */
@@ -350,6 +352,7 @@ const config: Config = {
     height: 0,
     flip: false,
     return: true,
+    autoBrightness: true,
     brightness: 0,
     contrast: 0,
     sharpness: 0,
diff --git a/src/human.ts b/src/human.ts
index 46e03e9f..b036db38 100644
--- a/src/human.ts
+++ b/src/human.ts
@@ -139,7 +139,6 @@ export class Human {
     const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');
     defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
     defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';
-    defaults.backend = env.browser ? 'webgl' : 'tensorflow';
     this.version = app.version; // expose version property on instance of class
     Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself
     this.config = JSON.parse(JSON.stringify(defaults));
@@ -252,7 +251,7 @@ export class Human {
    * @param getTensor - should image processing also return tensor or just canvas
    * Returns object with `tensor` and `canvas`
    */
-  image(input: Input, getTensor: boolean = true) {
+  image(input: Input, getTensor: boolean = false) {
     return image.process(input, this.config, getTensor);
   }
 
@@ -455,6 +454,7 @@ export class Human {
 
       timeStamp = now();
       this.config.skipAllowed = await image.skip(this.config, img.tensor);
+      this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change
       if (!this.performance.totalFrames) this.performance.totalFrames = 0;
       if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;
       (this.performance.totalFrames)++;
diff --git a/src/image/enhance.ts b/src/image/enhance.ts
index 97846907..e807633a 100644
--- a/src/image/enhance.ts
+++ b/src/image/enhance.ts
@@ -6,19 +6,23 @@ import * as tf from 'dist/tfjs.esm.js';
 import type { Tensor } from '../exports';
 
 export async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {
-  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors
   const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
-  const channels = tf.split(squeeze, 3, 2);
-  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
-  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
-  const absMax = await Promise.all(max.map((channel) => channel.data()));
-  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
-  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];
-  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];
-  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
-  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];
-  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);
-  const reshape = tf.reshape(rgb, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);
-  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);
-  return reshape; // output shape is [1, height, width, 3]
+  const rgb = tf.split(squeeze, 3, 2);
+  const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]
+  const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]
+  // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]
+  // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);
+  const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]
+  const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
+  const maxRange = maxValue > 1 ? 255 : 1;
+  const factor = maxRange / maxValue;
+  const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values
+  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges
+  // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between
+  const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];
+  const stack = tf.stack([enh[0], enh[1], enh[2]], 2);
+  const reshape = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);
+  const final = tf.squeeze(reshape);
+  tf.dispose([...rgb, ...min, ...max, ...sub, ...range, ...enh, rgb, squeeze, reshape]);
+  return final; // output shape is [height, width, 3]
 }
diff --git a/src/image/image.ts b/src/image/image.ts
index 2236adae..c5a4f85e 100644
--- a/src/image/image.ts
+++ b/src/image/image.ts
@@ -188,7 +188,7 @@ export async function process(input: Input, config: Config, getTensor: boolean =
       if (config.filter.technicolor) fx.add('technicolor');
       if (config.filter.polaroid) fx.add('polaroid');
       if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);
-      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);
+      if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);
       else outCanvas = fx.draw(inCanvas);
     }
   } else {
@@ -238,6 +238,14 @@ export async function process(input: Input, config: Config, getTensor: boolean =
   if (!pixels) throw new Error('input error: cannot create tensor');
   const casted: Tensor = tf.cast(pixels, 'float32');
   const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);
+
+  if (config.filter.autoBrightness) {
+    const max = tf.max(tensor);
+    const maxVal = await max.data();
+    config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);
+    tf.dispose(max);
+  }
+
   tf.dispose([pixels, casted]);
   return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };
 }
@@ -274,7 +282,7 @@ const checksum = async (input: Tensor): Promise<number> => { // use tf sum or js
 
 export async function skip(config: Partial<Config>, input: Tensor) {
   let skipFrame = false;
-  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis
+  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis
 
   /*
   const checkSum = await checksum(input);
diff --git a/src/object/centernet.ts b/src/object/centernet.ts
index de98b798..8c99eb39 100644
--- a/src/object/centernet.ts
+++ b/src/object/centernet.ts
@@ -1,7 +1,7 @@
 /**
  * CenterNet object detection model implementation
  *
- * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)
+ * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)
  */
 
 import * as tf from 'dist/tfjs.esm.js';
diff --git a/src/object/nanodet.ts b/src/object/nanodet.ts
index d8863edf..f1006c9c 100644
--- a/src/object/nanodet.ts
+++ b/src/object/nanodet.ts
@@ -1,7 +1,7 @@
 /**
  * NanoDet object detection model implementation
  *
- * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)
+ * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)
  */
 
 import * as tf from 'dist/tfjs.esm.js';
diff --git a/src/tfjs/backend.ts b/src/tfjs/backend.ts
index cdbc4356..603e44b9 100644
--- a/src/tfjs/backend.ts
+++ b/src/tfjs/backend.ts
@@ -8,6 +8,15 @@ import * as humangl from './humangl';
 import * as constants from './constants';
 import type { TensorInfo } from './types';
 
+export async function getBestBackend(): Promise<BackendEnum> {
+  await env.updateBackend(); // update env on backend init
+  if (!env.browser) return 'tensorflow';
+  if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';
+  if (env.webgl.supported && env.webgl.backend) return 'webgl';
+  if (env.wasm.supported && env.wasm.backend) return 'wasm';
+  return 'cpu';
+}
+
 function registerCustomOps(config: Config) {
   const newKernels: string[] = [];
   if (!env.kernels.includes('mod')) {
@@ -73,6 +82,7 @@ let defaultFlags: Record<string, unknown> = {};
 
 export async function check(instance: Human, force = false) {
   instance.state = 'backend';
+  if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();
   if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {
     const timeStamp = now();
 
diff --git a/src/util/env.ts b/src/util/env.ts
index e8ac6291..996b401f 100644
--- a/src/util/env.ts
+++ b/src/util/env.ts
@@ -53,17 +53,21 @@ export class Env {
     backend: undefined | boolean,
     version: undefined | string,
     renderer: undefined | string,
+    shader: undefined | string,
+    vendor: undefined | string,
   } = {
       supported: undefined,
       backend: undefined,
       version: undefined,
       renderer: undefined,
+      shader: undefined,
+      vendor: undefined,
     };
   /** WebGPU detected capabilities */
   webgpu: {
     supported: undefined | boolean,
     backend: undefined | boolean,
-    adapter: undefined | string,
+    adapter: undefined | GPUAdapterInfo,
   } = {
       supported: undefined,
       backend: undefined,
@@ -123,35 +127,36 @@ export class Env {
   async updateBackend() {
     // analyze backends
     this.backends = Object.keys(tf.engine().registryFactory);
-    this.tensorflow = {
-      version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),
-      gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),
-    };
+    try {
+      this.tensorflow = {
+        version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),
+        gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),
+      };
+    } catch { /**/ }
     this.wasm.supported = typeof WebAssembly !== 'undefined';
     this.wasm.backend = this.backends.includes('wasm');
-    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {
-      this.wasm.simd = tf.env().get('WASM_HAS_SIMD_SUPPORT') as boolean;
-      this.wasm.multithread = tf.env().get('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;
+    if (this.wasm.supported && this.wasm.backend) {
+      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;
+      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;
     }
     const c = image.canvas(100, 100);
     const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts
     // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;
     this.webgl.supported = typeof ctx !== 'undefined';
     this.webgl.backend = this.backends.includes('webgl');
-    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {
-      const backend = tf.backend();
-      const gl = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;
-      if (gl) {
-        this.webgl.version = gl.getParameter(gl.VERSION);
-        this.webgl.renderer = gl.getParameter(gl.RENDERER);
-      }
+    if (this.webgl.supported && this.webgl.backend) {
+      const gl = ctx as WebGL2RenderingContext;
+      this.webgl.version = gl.getParameter(gl.VERSION);
+      this.webgl.vendor = gl.getParameter(gl.VENDOR);
+      this.webgl.renderer = gl.getParameter(gl.RENDERER);
+      this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);
     }
     this.webgpu.supported = this.browser && typeof navigator.gpu !== 'undefined';
     this.webgpu.backend = this.backends.includes('webgpu');
     try {
       if (this.webgpu.supported) {
         const adapter = await navigator.gpu.requestAdapter();
-        this.webgpu.adapter = adapter ? adapter.name : undefined;
+        this.webgpu.adapter = await adapter?.requestAdapterInfo();
       }
     } catch {
       this.webgpu.supported = false;
diff --git a/test/build.log b/test/build.log
index 1bcf00e3..e512420a 100644
--- a/test/build.log
+++ b/test/build.log
@@ -1,40 +1,40 @@
-2022-11-04 13:19:02 DATA:  Build {"name":"@vladmandic/human","version":"3.0.0"} 
-2022-11-04 13:19:02 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.0"} 
-2022-11-04 13:19:02 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2022-11-04 13:19:02 INFO:  Toolchain: {"build":"0.7.14","esbuild":"0.15.13","typescript":"4.8.4","typedoc":"0.23.20","eslint":"8.26.0"} 
-2022-11-04 13:19:02 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2022-11-04 13:19:02 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":79,"inputBytes":672790,"outputBytes":316303} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":79,"inputBytes":672794,"outputBytes":316307} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":79,"inputBytes":673742,"outputBytes":316418} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":79,"inputBytes":672536,"outputBytes":314908} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1144900} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":79,"inputBytes":1816766,"outputBytes":1456466} 
-2022-11-04 13:19:02 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":79,"inputBytes":1816766,"outputBytes":1913830} 
-2022-11-04 13:19:06 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} 
-2022-11-04 13:19:08 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":76,"generated":true} 
-2022-11-04 13:19:08 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5936,"outputBytes":2867} 
-2022-11-04 13:19:08 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17134,"outputBytes":9181} 
-2022-11-04 13:19:16 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":116,"errors":0,"warnings":0} 
-2022-11-04 13:19:16 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2022-11-04 13:19:16 STATE: Copy: {"input":"src/tfjs","output":"dist/tfjs.esm.d.ts"} 
-2022-11-04 13:19:16 INFO:  Done... 
-2022-11-04 13:19:17 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":195} 
-2022-11-04 13:19:17 STATE: Filter: {"input":"types/human.d.ts"} 
-2022-11-04 13:19:17 STATE: Link: {"input":"types/human.d.ts"} 
-2022-11-04 13:19:17 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} 
-2022-11-04 13:19:17 STATE: Models {"folder":"./models","models":12} 
-2022-11-04 13:19:17 STATE: Models {"folder":"../human-models/models","models":43} 
-2022-11-04 13:19:17 STATE: Models {"folder":"../blazepose/model/","models":4} 
-2022-11-04 13:19:17 STATE: Models {"folder":"../anti-spoofing/model","models":1} 
-2022-11-04 13:19:17 STATE: Models {"folder":"../efficientpose/models","models":3} 
-2022-11-04 13:19:17 STATE: Models {"folder":"../insightface/models","models":5} 
-2022-11-04 13:19:17 STATE: Models {"folder":"../movenet/models","models":3} 
-2022-11-04 13:19:17 STATE: Models {"folder":"../nanodet/models","models":4} 
-2022-11-04 13:19:18 STATE: Models: {"count":58,"totalSize":386543911} 
-2022-11-04 13:19:18 INFO:  Human Build complete... {"logFile":"test/build.log"} 
+2022-11-10 20:16:03 DATA:  Build {"name":"@vladmandic/human","version":"3.0.0"} 
+2022-11-10 20:16:03 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.0"} 
+2022-11-10 20:16:03 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2022-11-10 20:16:03 INFO:  Toolchain: {"build":"0.7.14","esbuild":"0.15.13","typescript":"4.8.4","typedoc":"0.23.20","eslint":"8.27.0"} 
+2022-11-10 20:16:03 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2022-11-10 20:16:03 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":79,"inputBytes":674094,"outputBytes":316829} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":79,"inputBytes":674098,"outputBytes":316833} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":79,"inputBytes":675046,"outputBytes":316944} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":79,"inputBytes":673840,"outputBytes":315438} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1144900} 
+2022-11-10 20:16:03 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":79,"inputBytes":1818070,"outputBytes":1457034} 
+2022-11-10 20:16:04 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":79,"inputBytes":1818070,"outputBytes":1914674} 
+2022-11-10 20:16:08 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} 
+2022-11-10 20:16:10 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":76,"generated":true} 
+2022-11-10 20:16:10 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5981,"outputBytes":2862} 
+2022-11-10 20:16:10 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17134,"outputBytes":9181} 
+2022-11-10 20:16:19 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":116,"errors":0,"warnings":0} 
+2022-11-10 20:16:19 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2022-11-10 20:16:19 STATE: Copy: {"input":"src/tfjs","output":"dist/tfjs.esm.d.ts"} 
+2022-11-10 20:16:19 INFO:  Done... 
+2022-11-10 20:16:20 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":195} 
+2022-11-10 20:16:20 STATE: Filter: {"input":"types/human.d.ts"} 
+2022-11-10 20:16:20 STATE: Link: {"input":"types/human.d.ts"} 
+2022-11-10 20:16:20 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} 
+2022-11-10 20:16:20 STATE: Models {"folder":"./models","models":12} 
+2022-11-10 20:16:20 STATE: Models {"folder":"../human-models/models","models":43} 
+2022-11-10 20:16:20 STATE: Models {"folder":"../blazepose/model/","models":4} 
+2022-11-10 20:16:20 STATE: Models {"folder":"../anti-spoofing/model","models":1} 
+2022-11-10 20:16:20 STATE: Models {"folder":"../efficientpose/models","models":3} 
+2022-11-10 20:16:20 STATE: Models {"folder":"../insightface/models","models":5} 
+2022-11-10 20:16:20 STATE: Models {"folder":"../movenet/models","models":3} 
+2022-11-10 20:16:20 STATE: Models {"folder":"../nanodet/models","models":4} 
+2022-11-10 20:16:21 STATE: Models: {"count":58,"totalSize":386543911} 
+2022-11-10 20:16:21 INFO:  Human Build complete... {"logFile":"test/build.log"}