DataBloom

I have a got a CNN, have run it on given training/testing data for a satisfactory amount of epochs, and then saved it as a .hd5 file. Now, I have got fresh images, in jpeg format, and need to run them through my model and obtain predictions. I have just the jpegs and they are not labelled. How do I proceed?I am using this snippet, to run the predictions on the test/validation dataset.

y_pred = mod(test_examples[:50], training = False) y_pred_argmax = np.argmax(y_pred, axis=3) 

Test and Train images are of 512x512x1 shape. Number of classes(features) in the model is 8.

Hi!

I come from python and wanted to test some client side ML with TensorFlow js. During the fit process I noticed some unexpected behaviour with some callbacks getting called and others not and some callbacks not receiving data.

Here my short example:

“`javascript model.fit(X, y, { callbacks: {

// called but empty logs object? (okay because no data available yet) onTrainBegin: (logs) => { console.log('TrainBegin'); console.log(logs); }, // called but empty logs object? (why?? Training is over and it should have data) onTrainEnd: (logs) => { console.log('TrainEnd'); console.log(logs); }, // never called??? onEpochStart: (epoch, logs) => { console.log('EpochStart'); console.log(`Epoch ${epoch}: loss = ${logs.loss}; accuracy = ${logs.acc}`); }, // works! onEpochEnd: (epoch, logs) => { console.log('EpochEnd'); console.log(`Epoch ${epoch}: loss = ${logs.loss}; accuracy = ${logs.acc}`); }, // never called??? onBatchStart: (batch, logs) => { console.log('BatchStart'); console.log(`Batch ${batch}: loss = ${logs.loss}; accuracy = ${logs.acc}`); }, // works! onBatchEnd: (batch, logs) => { console.log('BatchEnd'); console.log(`Batch ${batch}: loss = ${logs.loss}; accuracy = ${logs.acc}`); }, 

}, }); “`

I looked through the documentation but could not find an explanation for this behaviour. Does anyone have any ideas and can enlighten me? 😀

def find_class(self, mod_name, name): 

if type(name) is str and ‘Storage’ in name: try: return StorageType(name) except KeyError: pass mod_name = load_module_mapping.get(mod_name, mod_name) print(mod_name) return super().find_class(mod_name, name)

when trying to find the class ldm.models.autoencoder is throwing an error in my code:

`ModuleNotFoundError: No module named 'ldm.models'; 'ldm' is not a package` I have tried the ldm (0.1.1 and 0.1.3) package installed but I have no idea where .models is from. Ideas? 

I’m running a frigate docker container on unRAID and it’s detecting cars with the default model successfully. I would like to perform number plate localisation and optical character recognition, and would like to know how to best go about this.

I was going to use deepstack and found some guides on its use via double take, however, this seems exclusively for facial recognition.

Is it possible to perform car recognition and plate recognition within the same model, followed by separate OCR via snapshots, or will I need a separate model for each stage? Frigate is using my M.2 coral EdgeTPU for real time detection.

I have a bit of experience training models and think I could make an effective one for plate detection, but am unsure if I could share the TPU with frigate for real time detection if my model was deployed in a VM.

I made a simple web page to test FLOPS with tensorflow.js

https://www.videogames.ai/tensorflow-js-benchmark

Improvements compared to the previous flops test page I made if you already seen it:

• TF.JS now runs in a web worker, so the test reduces page freezes
• Can now choose which backend to use. I disabled WASM backend since there is seems to be a bug with web workers. Github Issue
• More info about the host are displayed

​

I get 417.467 flops on the integrated GPU of AMD Ryzen 5700u

What do you get?