EasyCV/configs/detection/yolox/yolox_l_8xb8_300e_coco.py

_base_ = './yolox_s_8xb16_300e_coco.py'

# model settings
model = dict(model_type='l', head=dict(model_type='l', ))

data = dict(imgs_per_gpu=8, workers_per_gpu=4)

optimizer = dict(
    type='SGD', lr=0.01, momentum=0.9, weight_decay=5e-4, nesterov=True)
initial commit 2022-04-02 20:01:06 +08:00			`_base_ = './yolox_s_8xb16_300e_coco.py'`

			`# model settings`
Yolox improve with REPConv/ASFF/TOOD (#154) * add attention layer and more loss function * add attention layer and various loss functions * add siou loss * add tah,various attention layers, and different loss functions * add asff sim, gsconv * blade utils fit faster * blade optimize for yolox static & fp16 * decode output for yolox control by cfg * add reparameterize_models for export * e2e trt_nms plugin export support and numeric test * split preprocess from end2end+blade, speedup from 17ms->7.2ms Co-authored-by: zouxinyi0625 <zouxinyi.zxy@alibaba-inc.com> 2022-08-24 18:11:15 +08:00			`model = dict(model_type='l', head=dict(model_type='l', ))`
initial commit 2022-04-02 20:01:06 +08:00
			`data = dict(imgs_per_gpu=8, workers_per_gpu=4)`

			`optimizer = dict(`
			`type='SGD', lr=0.01, momentum=0.9, weight_decay=5e-4, nesterov=True)`