diff --git a/detect_int8.py b/detect_int8.py
deleted file mode 100644
index 7511cf7..0000000
--- a/detect_int8.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import time
-from pathlib import Path
-import glob
-import json
-
-import numpy as np
-from tqdm import tqdm
-import cv2
-import yaml
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-from edgetpumodel import EdgeTPUModel
-from utils import resize_and_pad, get_image_tensor, save_one_json, coco80_to_coco91_class
-
-if __name__ == "__main__":
- 
-    parser = argparse.ArgumentParser("EdgeTPU test runner")
-    parser.add_argument("--model", "-m", help="weights file", required=True)
-    parser.add_argument("--bench_speed", action='store_true', help="run speed test on dummy data")
-    parser.add_argument("--bench_image", action='store_true', help="run detection test")
-    parser.add_argument("--conf_thresh", type=float, default=0.25, help="model confidence threshold")
-    parser.add_argument("--iou_thresh", type=float, default=0.45, help="NMS IOU threshold")
-    parser.add_argument("--names", type=str, default='data/coco.yaml', help="Names file")
-    parser.add_argument("--image", "-i", type=str, help="Image file to run detection on")
-    parser.add_argument("--device", type=int, default=0, help="Image capture device to run live detection")
-    parser.add_argument("--stream", action='store_true', help="Process a stream")
-    parser.add_argument("--bench_coco", action='store_true', help="Process a stream")
-    parser.add_argument("--coco_path", type=str, help="Path to COCO 2017 Val folder")
-    parser.add_argument("--quiet","-q", action='store_true', help="Disable logging (except errors)")
-        
-    args = parser.parse_args()
-    
-    if args.quiet:
-        logging.disable(logging.CRITICAL)
-        logger.disabled = True
-    
-    if args.stream and args.image:
-        logger.error("Please select either an input image or a stream")
-        exit(1)
-    
-    model = EdgeTPUModel(args.model, args.names, conf_thresh=args.conf_thresh, iou_thresh=args.iou_thresh)
-    input_size = model.get_image_size()
-
-    x = (255*np.random.random((3,*input_size))).astype(np.int8)
-    model.forward(x)
-
-    conf_thresh = 0.25
-    iou_thresh = 0.45
-    classes = None
-    agnostic_nms = False
-    max_det = 1000
-
-    if args.bench_speed:
-        logger.info("Performing test run")
-        n_runs = 100
-        
-        
-        inference_times = []
-        nms_times = []
-        total_times = []
-        
-        for i in tqdm(range(n_runs)):
-            x = (255*np.random.random((3,*input_size))).astype(np.float32)
-            
-            pred = model.forward(x)
-            tinference, tnms = model.get_last_inference_time()
-            
-            inference_times.append(tinference)
-            nms_times.append(tnms)
-            total_times.append(tinference + tnms)
-            
-        inference_times = np.array(inference_times)
-        nms_times = np.array(nms_times)
-        total_times = np.array(total_times)
-            
-        logger.info("Inference time (EdgeTPU): {:1.2f} +- {:1.2f} ms".format(inference_times.mean()/1e-3, inference_times.std()/1e-3))
-        logger.info("NMS time (CPU): {:1.2f} +- {:1.2f} ms".format(nms_times.mean()/1e-3, nms_times.std()/1e-3))
-        fps = 1.0/total_times.mean()
-        logger.info("Mean FPS: {:1.2f}".format(fps))
-
-    elif args.bench_image:
-        logger.info("Testing on Zidane image")
-        model.predict("./data/images/zidane.jpg")
-
-    elif args.bench_coco:
-        logger.info("Testing on COCO dataset")
-        
-        model.conf_thresh = 0.001
-        model.iou_thresh = 0.65
-        
-        coco_glob = os.path.join(args.coco_path, "*.jpg")
-        images = glob.glob(coco_glob)
-        
-        logger.info("Looking for: {}".format(coco_glob))
-        ids = [int(os.path.basename(i).split('.')[0]) for i in images]
-        
-        out_path = "./coco_eval"
-        os.makedirs("./coco_eval", exist_ok=True)
-        
-        logger.info("Found {} images".format(len(images)))
-        
-        class_map = coco80_to_coco91_class()
-        
-        predictions = []
-        
-        for image in tqdm(images):
-            res = model.predict(image, save_img=False, save_txt=False)
-            save_one_json(res, predictions, Path(image), class_map)
-            
-        pred_json = os.path.join(out_path,
-                    "{}_predictions.json".format(os.path.basename(args.model)))
-        
-        with open(pred_json, 'w') as f:
-            json.dump(predictions, f,indent=1)
-        
-    elif args.image is not None:
-        logger.info("Testing on user image: {}".format(args.image))
-        model.predict(args.image)
-        
-    elif args.stream:
-        logger.info("Opening stream on device: {}".format(args.device))
-        
-        cam = cv2.VideoCapture(args.device)
-        
-        while True:
-          try:
-            res, image = cam.read()
-            
-            if res is False:
-                logger.error("Empty image received")
-                break
-            else:
-                full_image, net_image, pad = get_image_tensor(image, input_size[0])
-                pred = model.forward(net_image)
-                
-                model.process_predictions(pred[0], full_image, pad)
-                
-                tinference, tnms = model.get_last_inference_time()
-                logger.info("Frame done in {}".format(tinference+tnms))
-          except KeyboardInterrupt:
-            break
-          
-        cam.release()
-            
-        
-
-    
-