commit 0cfc6dc542b8aefc1d1bfd81d55702e52075c761 Author: chengke-codes Date: Sat Apr 1 01:44:33 2023 +0100 first commit diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..31ba469 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,3 @@ +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.weights filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..bc0ecbf --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +.DS_Store +Thumbs.db + diff --git a/README.md b/README.md new file mode 100644 index 0000000..6bcb15d --- /dev/null +++ b/README.md @@ -0,0 +1,34 @@ +# YCB objects demo weights for Yolov4 & Yolov5 + +## Yolov4 + +1. Compile darknet. + +2. Copy `yolov4/ycb` and `test1.mp4`to the folder of darknet. + + ![image-20210227202736516](https://i.loli.net/2021/02/28/eWtJM3p9sjAf27C.png) + +3. Go to the folder of darknet, run `./darknet detector demo ycb/obj.data ycb/yolo-test.cfg ycb/weights/yolo-obj_7000.weights -thresh 0.5 -ext_output test1.mp4` . + +4. For a headless server, you will meet an error (something looks like Gtk-WARNING cannot open display) if you execute line 3. Just try `./darknet detector demo ycb/obj.data ycb/yolo-test.cfg ycb/weights/yolo-obj_7000.weights -thresh 0.5 test1.mp4 -dont_show -ext_output -out_filename res.avi` + + + +## Yolov5 + +1. Install yolov5 (assume you yolov5 project is located at ``) + +2. Execute `mkdir -p /runs/train ` + +3. Copy `yolov5/exp2` to `/runs/train` + + ![image-20210227202532270](https://i.loli.net/2021/02/28/ioXfnA5jVdR2F8h.png) + +4. Copy `test1.mp4` to `` + +4. Run `python detect.py --source test1.mp4 --weights runs/train/exp2/weights/best.pt --conf-thres 0.25 --view-img` + +## Test Videos + +The videos `test1.mp4` and `test2.mp4` are collected from internet, **I don’t own the rights to them**. + diff --git a/test1.mp4 b/test1.mp4 new file mode 100644 index 0000000..4e08c23 --- /dev/null +++ b/test1.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1b64ccfa2e945f35313d765436bc0574d97dd06e00f68ce286a67103c37086f +size 154215439 diff --git a/test2.mp4 b/test2.mp4 new file mode 100755 index 0000000..c067852 --- /dev/null +++ b/test2.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:019c0310e8d06f2c6e35c5980851509f02a8f8c6f7eb3ac0478d3d27f022cb45 +size 60806528 diff --git a/yolov4/ycb/obj.data b/yolov4/ycb/obj.data new file mode 100644 index 0000000..3505307 --- /dev/null +++ b/yolov4/ycb/obj.data @@ -0,0 +1,5 @@ +classes = 21 +train = ycb/train.txt +valid = ycb/test.txt +names = ycb/obj.names +backup = ycb/backup/ diff --git a/yolov4/ycb/obj.names b/yolov4/ycb/obj.names new file mode 100644 index 0000000..749fd14 --- /dev/null +++ b/yolov4/ycb/obj.names @@ -0,0 +1,21 @@ +002_master_chef_can +003_cracker_box +004_sugar_box +005_tomato_soup_can +006_mustard_bottle +007_tuna_fish_can +008_pudding_box +009_gelatin_box +010_potted_meat_can +011_banana +019_pitcher_base +021_bleach_cleanser +024_bowl +025_mug +035_power_drill +036_wood_block +037_scissors +040_large_marker +051_large_clamp +052_extra_large_clamp +061_foam_brick \ No newline at end of file diff --git a/yolov4/ycb/weights/yolo-obj_7000.weights b/yolov4/ycb/weights/yolo-obj_7000.weights new file mode 100644 index 0000000..294beac --- /dev/null +++ b/yolov4/ycb/weights/yolo-obj_7000.weights @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4113af7115863389af538dcf264a31990b1de9815d05fc6ec6d634924ec9e4aa +size 256446780 diff --git a/yolov4/ycb/yolo-test.cfg b/yolov4/ycb/yolo-test.cfg new file mode 100644 index 0000000..eab72d3 --- /dev/null +++ b/yolov4/ycb/yolo-test.cfg @@ -0,0 +1,1160 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +#batch=64 +#subdivisions=16 +width=832 +height=832 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.0013 +burn_in=1000 +max_batches =140000 +policy=steps +steps=112000,126000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish +stopbackward=800 + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=78 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=21 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +max_delta=5 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=78 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=21 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +max_delta=5 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=78 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=21 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +max_delta=5 + diff --git a/yolov4/ycb/yolo-train.cfg b/yolov4/ycb/yolo-train.cfg new file mode 100644 index 0000000..317762c --- /dev/null +++ b/yolov4/ycb/yolo-train.cfg @@ -0,0 +1,1160 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=64 +width=416 +height=416 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.0013 +burn_in=1000 +max_batches =140000 +policy=steps +steps=112000,126000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish +stopbackward=800 + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=78 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=21 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +max_delta=5 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=78 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=21 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +max_delta=5 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=78 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=21 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +max_delta=5 + diff --git a/yolov5/exp2/hyp.yaml b/yolov5/exp2/hyp.yaml new file mode 100644 index 0000000..239e124 --- /dev/null +++ b/yolov5/exp2/hyp.yaml @@ -0,0 +1,27 @@ +lr0: 0.01 +lrf: 0.2 +momentum: 0.937 +weight_decay: 0.0005 +warmup_epochs: 3.0 +warmup_momentum: 0.8 +warmup_bias_lr: 0.1 +box: 0.05 +cls: 0.5 +cls_pw: 1.0 +obj: 1.0 +obj_pw: 1.0 +iou_t: 0.2 +anchor_t: 4.0 +fl_gamma: 0.0 +hsv_h: 0.015 +hsv_s: 0.7 +hsv_v: 0.4 +degrees: 0.0 +translate: 0.1 +scale: 0.5 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.0 diff --git a/yolov5/exp2/labels.jpg b/yolov5/exp2/labels.jpg new file mode 100644 index 0000000..2138ed0 Binary files /dev/null and b/yolov5/exp2/labels.jpg differ diff --git a/yolov5/exp2/labels_correlogram.jpg b/yolov5/exp2/labels_correlogram.jpg new file mode 100644 index 0000000..e784b22 Binary files /dev/null and b/yolov5/exp2/labels_correlogram.jpg differ diff --git a/yolov5/exp2/opt.yaml b/yolov5/exp2/opt.yaml new file mode 100644 index 0000000..7500ca6 --- /dev/null +++ b/yolov5/exp2/opt.yaml @@ -0,0 +1,33 @@ +weights: yolov5m.pt +cfg: '' +data: ycb/dataset.yaml +hyp: data/hyp.scratch.yaml +epochs: 300 +batch_size: 32 +img_size: +- 640 +- 640 +rect: false +resume: false +nosave: false +notest: false +noautoanchor: false +evolve: false +bucket: '' +cache_images: false +image_weights: false +device: '' +multi_scale: false +single_cls: false +adam: false +sync_bn: false +local_rank: -1 +log_imgs: 16 +workers: 8 +project: runs/train +name: exp +exist_ok: false +total_batch_size: 32 +world_size: 1 +global_rank: -1 +save_dir: runs/train/exp2 diff --git a/yolov5/exp2/results.txt b/yolov5/exp2/results.txt new file mode 100644 index 0000000..5d97354 --- /dev/null +++ b/yolov5/exp2/results.txt @@ -0,0 +1,62 @@ + 0/299 13.9G 0.03528 0.03787 0.02883 0.102 134 640 0.3061 0.3211 0.2722 0.1961 0.0473 0.1027 0.05056 + 1/299 13.9G 0.02398 0.02717 0.006741 0.05789 148 640 0.3071 0.2974 0.2652 0.1965 0.04861 0.1071 0.05276 + 2/299 13.9G 0.02157 0.02633 0.005818 0.05371 147 640 0.2936 0.2364 0.2119 0.1551 0.06259 0.1203 0.05542 + 3/299 13.9G 0.01971 0.02526 0.005099 0.05006 137 640 0.2828 0.1926 0.181 0.13 0.07275 0.1188 0.06165 + 4/299 13.9G 0.01808 0.02358 0.004397 0.04606 129 640 0.2773 0.1769 0.1771 0.1288 0.07533 0.1218 0.06344 + 5/299 13.9G 0.01732 0.02273 0.004097 0.04415 125 640 0.2996 0.1743 0.1792 0.1315 0.07628 0.1225 0.06415 + 6/299 13.9G 0.01688 0.0222 0.003899 0.04298 153 640 0.3147 0.1623 0.171 0.1239 0.08036 0.1215 0.06577 + 7/299 13.9G 0.0166 0.0219 0.003824 0.04233 170 640 0.3103 0.1606 0.1734 0.1258 0.08092 0.1208 0.06552 + 8/299 13.9G 0.01639 0.0216 0.003753 0.04174 114 640 0.315 0.1576 0.1709 0.1243 0.0819 0.1199 0.06597 + 9/299 13.9G 0.01622 0.02144 0.003713 0.04138 146 640 0.3081 0.1586 0.1709 0.1244 0.08257 0.1187 0.06605 + 10/299 13.9G 0.01602 0.02126 0.003615 0.0409 139 640 0.3144 0.1588 0.1732 0.1259 0.08334 0.1172 0.06554 + 11/299 13.9G 0.01595 0.02113 0.003612 0.04069 138 640 0.3223 0.1544 0.1718 0.1258 0.08413 0.117 0.06595 + 12/299 13.9G 0.01587 0.02107 0.003584 0.04053 122 640 0.3316 0.1505 0.1716 0.1263 0.0844 0.1164 0.06596 + 13/299 13.9G 0.01577 0.02095 0.003575 0.0403 131 640 0.3394 0.1488 0.1701 0.126 0.08529 0.1163 0.06615 + 14/299 13.9G 0.01571 0.02086 0.003539 0.04012 124 640 0.3449 0.1476 0.1696 0.1268 0.08585 0.1152 0.06656 + 15/299 13.9G 0.0156 0.02076 0.003511 0.03987 123 640 0.3559 0.147 0.1713 0.1285 0.08565 0.1151 0.06663 + 16/299 13.9G 0.01553 0.02066 0.003482 0.03967 135 640 0.3641 0.1452 0.1722 0.1289 0.08608 0.1148 0.06637 + 17/299 13.9G 0.01547 0.0206 0.00346 0.03953 123 640 0.3675 0.1448 0.173 0.1299 0.08637 0.1136 0.0662 + 18/299 13.9G 0.01543 0.02059 0.003468 0.03948 160 640 0.3729 0.1444 0.172 0.1297 0.08639 0.1136 0.06575 + 19/299 13.9G 0.01537 0.02049 0.003432 0.03929 145 640 0.3739 0.1453 0.1717 0.1292 0.08642 0.1141 0.0656 + 20/299 13.9G 0.01527 0.02036 0.003424 0.03905 134 640 0.3699 0.1438 0.1718 0.129 0.08643 0.1138 0.06587 + 21/299 13.9G 0.01521 0.02033 0.00337 0.03891 150 640 0.3789 0.1405 0.171 0.1284 0.08678 0.1137 0.06596 + 22/299 13.9G 0.01518 0.02023 0.00337 0.03878 116 640 0.3966 0.1395 0.1732 0.1305 0.08688 0.1141 0.06601 + 23/299 13.9G 0.01522 0.02036 0.003413 0.03899 145 640 0.3909 0.1365 0.1709 0.1286 0.08745 0.1139 0.06625 + 24/299 13.9G 0.0151 0.0202 0.003362 0.03866 161 640 0.3898 0.135 0.1689 0.1269 0.088 0.1139 0.06606 + 25/299 13.9G 0.0151 0.02012 0.003366 0.03858 123 640 0.3857 0.1358 0.1694 0.1269 0.08783 0.113 0.06583 + 26/299 13.9G 0.01505 0.02013 0.00335 0.03853 204 640 0.3834 0.1367 0.1683 0.1261 0.08823 0.1124 0.06616 + 27/299 13.9G 0.01501 0.02003 0.003333 0.03837 138 640 0.387 0.137 0.1686 0.1256 0.08843 0.112 0.06586 + 28/299 13.9G 0.01496 0.02005 0.003319 0.03833 127 640 0.3848 0.1377 0.1668 0.1248 0.08862 0.1113 0.06593 + 29/299 13.9G 0.01493 0.01999 0.003302 0.03823 142 640 0.3859 0.1373 0.168 0.1252 0.08859 0.1107 0.06564 + 30/299 13.9G 0.01492 0.01999 0.003321 0.03823 132 640 0.3925 0.1388 0.1689 0.1259 0.08869 0.1104 0.06582 + 31/299 13.9G 0.01492 0.02002 0.003315 0.03825 146 640 0.3862 0.1418 0.1691 0.1266 0.08843 0.1102 0.0659 + 32/299 13.9G 0.0149 0.01992 0.00329 0.03811 140 640 0.3839 0.1429 0.1693 0.128 0.08801 0.1099 0.06569 + 33/299 13.9G 0.01482 0.01986 0.003263 0.03795 114 640 0.3857 0.1411 0.1672 0.1267 0.08838 0.1102 0.06628 + 34/299 13.9G 0.01478 0.01985 0.003276 0.03791 153 640 0.3791 0.143 0.1674 0.127 0.08801 0.1099 0.06613 + 35/299 13.9G 0.0148 0.01981 0.003253 0.03786 145 640 0.3754 0.142 0.1673 0.1262 0.08772 0.1101 0.06629 + 36/299 13.9G 0.01473 0.01979 0.003246 0.03776 133 640 0.3631 0.139 0.1647 0.123 0.08833 0.1093 0.06597 + 37/299 13.9G 0.0147 0.01974 0.003244 0.03769 130 640 0.3684 0.1399 0.164 0.1225 0.08863 0.1086 0.06679 + 38/299 13.9G 0.0147 0.01975 0.003232 0.03768 149 640 0.3702 0.1384 0.1631 0.121 0.08922 0.1084 0.06718 + 39/299 13.9G 0.0147 0.0197 0.003235 0.03764 138 640 0.3755 0.1376 0.1632 0.1212 0.08915 0.1083 0.06715 + 40/299 13.9G 0.01466 0.01973 0.003253 0.03764 159 640 0.3747 0.1391 0.1644 0.122 0.08868 0.108 0.06733 + 41/299 13.9G 0.01461 0.01964 0.003204 0.03745 145 640 0.3761 0.1392 0.1642 0.1223 0.08882 0.1081 0.06759 + 42/299 13.9G 0.01462 0.01961 0.003171 0.0374 152 640 0.3764 0.1397 0.1657 0.1228 0.08843 0.1078 0.06782 + 43/299 13.9G 0.01456 0.01952 0.003174 0.03725 95 640 0.3598 0.1408 0.1666 0.1229 0.08804 0.1075 0.06832 + 44/299 13.9G 0.01461 0.01959 0.003222 0.03742 148 640 0.3588 0.1444 0.1657 0.1228 0.08793 0.1071 0.06883 + 45/299 13.9G 0.01458 0.01961 0.003188 0.03737 166 640 0.3695 0.1452 0.1655 0.1226 0.08795 0.1072 0.06929 + 46/299 13.9G 0.0145 0.01944 0.003154 0.03709 125 640 0.3704 0.1447 0.1648 0.1228 0.08828 0.107 0.06907 + 47/299 13.9G 0.01454 0.01953 0.003209 0.03728 145 640 0.3613 0.1438 0.1623 0.1208 0.08848 0.1064 0.06944 + 48/299 13.9G 0.01449 0.0195 0.003178 0.03717 145 640 0.358 0.1432 0.1627 0.121 0.08888 0.1055 0.07016 + 49/299 13.9G 0.01448 0.01953 0.003173 0.03719 108 640 0.3494 0.1413 0.1612 0.12 0.08921 0.1048 0.07058 + 50/299 13.9G 0.01442 0.01937 0.003146 0.03693 126 640 0.3508 0.1396 0.16 0.119 0.08958 0.1045 0.07038 + 51/299 13.9G 0.01444 0.01942 0.003159 0.03703 141 640 0.3622 0.1424 0.1611 0.1192 0.08954 0.1038 0.07006 + 52/299 13.9G 0.01442 0.01938 0.003129 0.03693 141 640 0.3583 0.1417 0.1609 0.1199 0.08972 0.104 0.07044 + 53/299 13.9G 0.01439 0.01939 0.003147 0.03693 144 640 0.3518 0.1432 0.1616 0.1212 0.08914 0.1044 0.07012 + 54/299 13.9G 0.01437 0.01932 0.003126 0.03682 135 640 0.3386 0.1448 0.1618 0.1217 0.0889 0.1046 0.07034 + 55/299 13.9G 0.01436 0.01934 0.003114 0.03681 149 640 0.3406 0.1451 0.1622 0.123 0.08903 0.1047 0.07041 + 56/299 13.9G 0.01433 0.01927 0.003116 0.03671 131 640 0.3398 0.1429 0.1617 0.1226 0.08919 0.1045 0.07113 + 57/299 13.9G 0.0143 0.01929 0.003098 0.03668 128 640 0.3397 0.141 0.1607 0.1221 0.08932 0.1054 0.0706 + 58/299 13.9G 0.01432 0.01934 0.003107 0.03676 140 640 0.3434 0.1392 0.1598 0.1208 0.08957 0.1056 0.07025 + 59/299 13.9G 0.01435 0.01935 0.003133 0.03683 159 640 0.3499 0.1394 0.1611 0.1207 0.08964 0.1054 0.07002 + 60/299 13.9G 0.01424 0.01921 0.003072 0.03652 126 640 0.3538 0.138 0.1601 0.1202 0.08991 0.1057 0.07037 + 61/299 13.9G 0.01424 0.0192 0.003081 0.03652 149 640 0.3491 0.1394 0.1593 0.1198 0.08968 0.105 0.07087 diff --git a/yolov5/exp2/train_batch0.jpg b/yolov5/exp2/train_batch0.jpg new file mode 100644 index 0000000..320666c Binary files /dev/null and b/yolov5/exp2/train_batch0.jpg differ diff --git a/yolov5/exp2/train_batch1.jpg b/yolov5/exp2/train_batch1.jpg new file mode 100644 index 0000000..496b742 Binary files /dev/null and b/yolov5/exp2/train_batch1.jpg differ diff --git a/yolov5/exp2/train_batch2.jpg b/yolov5/exp2/train_batch2.jpg new file mode 100644 index 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