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This model - MobileNetV2 is generated from `tf.keras.applications` using tf_model_generator.py. The dataset for int8 quantization is done using random data. The `kaggle.tflite` model is the original quantized model published by Google. This model uses v1 tflite quantization.

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This is a copy of Yolo as distributed at https://ultralytics.com under the license described in LICENSE.

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YOLO26 object detection models optimized for 320x320 resolution — Ultralytics' end-to-end NMS-free architecture with the decode head compiled onto the NPU. This repo carries both the **nano (yolo26n)** and **small (yolo26s)** variants. Both models are developed and optimized for the Synaptics Astra™ **SL2610-Series processors** Torq NPU. - **Model Type:** Object Detection (NMS-free, one-to-one head) - **Input:** 1x320x320x3 int8 NHWC (scale 1/255, zero-point −128) - **Output:** 1x84x2100 int8 — rows 0-3: xywh normalized to imgsz; rows 4-83: class probabilities (already decoded on-NPU; int8 output quantization saturates confidences near 0.5, threshold accordingly) - yolo26n output dequantization: scale 0.00423651235178113, zero-point −128 - yolo26s output dequantization: scale 0.004613031819462776, zero-point −128

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