Remove `/weights` directory (#3659)

.github/workflows/ci-testing.yml

@@ -64,15 +64,15 @@ jobs:
           di=cpu # inference devices  # define device
 
           # train
-          python train.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di
+          python train.py --img 128 --batch 16 --weights ${{ matrix.model }}.pt --cfg ${{ matrix.model }}.yaml --epochs 1 --device $di
           # detect
-          python detect.py --weights weights/${{ matrix.model }}.pt --device $di
+          python detect.py --weights ${{ matrix.model }}.pt --device $di
           python detect.py --weights runs/train/exp/weights/last.pt --device $di
           # test
-          python test.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --device $di
+          python test.py --img 128 --batch 16 --weights ${{ matrix.model }}.pt --device $di
           python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
 
           python hubconf.py  # hub
-          python models/yolo.py --cfg models/${{ matrix.model }}.yaml  # inspect
-          python models/export.py --img 128 --batch 1 --weights weights/${{ matrix.model }}.pt  # export
+          python models/yolo.py --cfg ${{ matrix.model }}.yaml  # inspect
+          python models/export.py --img 128 --batch 1 --weights ${{ matrix.model }}.pt  # export
         shell: bash

Dockerfile

@@ -46,8 +46,5 @@ ENV HOME=/usr/src/app
 # Bash into stopped container
 # id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
 
-# Send weights to GCP
-# python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt
-
 # Clean up
 # docker system prune -a --volumes

detect.py

@@ -63,8 +63,8 @@ def detect(weights='yolov5s.pt',  # model.pt path(s)
     # Second-stage classifier
     classify = False
     if classify:
-        modelc = load_classifier(name='resnet101', n=2)  # initialize
-        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
+        modelc = load_classifier(name='resnet50', n=2)  # initialize
+        modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
 
     # Set Dataloader
     vid_path, vid_writer = None, None