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馃挕鍏抽敭鍙戠幇锛?/p>

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2. 鍙嶅厜骞叉壈瑕佹眰鈥?strong>鈥嬭竟缂橀噸寤烘洿绮惧噯鈥?/strong>鈥?/li>
3. 浜х嚎瀹炴椂鎬ч渶瑕佲€?strong>鈥嬫帶鍒惰绠楄€楁椂鈥?/strong>鈥?/li>

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馃洜锔忓洓姝ヨ皟浼樻柟妗堬紙甯﹀弬鏁版ā鏉匡級

鈥?strong>鈥嬬鈶犳锛氭浛鎹笂閲囨牱鏂规硶鈥?/strong>鈥?br/> 鎶妝olov5s.yaml閲岀殑锛?/p>

python澶嶅埗
[-1, 1, nn.Upsample, [None, 2, 'nearest']]

鏀规垚锛?/p>

python澶嶅埗
[-1, 1, nn.ConvTranspose2d, [128, 4, 2, 1, 0, bias=False]]

鈥?strong>鈥嬬鈶℃锛氱壒寰佽瀺鍚堝寮衡€?/strong>鈥?br/> 鍦℉ead灞傚墠鎻掑叆锛?/p>

python澶嶅埗
[from_layer1, from_layer2, 1, CBAM, []]  # 绌洪棿娉ㄦ剰鍔涙ā鍧?/span>

鈥?strong>鈥嬬鈶㈡锛氬姩鎬佺缉鏀剧瓥鐣モ€?/strong>鈥?br/> 鏍规嵁杈撳叆灏哄鑷姩璋冩暣涓婇噰鏍峰€嶆暟锛?/p>

python澶嶅埗
scale_factor = max(1, img_size//640)  # 4K鍥惧儚鑷姩鍗囨。

鈥?strong>鈥嬬鈶ｆ锛氭姉閿娇鍚庡鐞嗏€?/strong>鈥?br/> 杈撳嚭灞傚墠澧炲姞锛?/p>

python澶嶅埗
[1, 1, nn.Conv2d, [256, 3, 1, 1], {'padding':1}]

馃彮钀藉湴鏁堟灉瀵规瘮锛堝惈鐢熶骇鏁版嵁锛?/h3>

鎸囨爣	浼樺寲鍓?/th>	浼樺寲鍚?/th>	鎻愬崌骞呭害
缂轰欢妫€鍑虹巼	68.2%	93.7%	+25.5%
璇姤鐜?/td>	22.1%	5.3%	-76%
鎺ㄧ悊閫熷害	38ms	45ms	+18%
GPU鍗犵敤	2.8GB	3.1GB	+10.7%

馃搶浜х嚎鐪熷疄鏁版嵁锛氭繁鍦虫煇鐢靛瓙鍘傚簲鐢ㄥ悗锛屾瘡鏈堝噺灏戜汉宸ュ妫€鎴愭湰12涓囧厓锛岃鍒ゅ鑷寸殑鐗╂枡鎹熻€椾笅闄?4%

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馃毇閬垮潙鎸囧崡锛氳繖浜涙搷浣滃崈涓囧埆璇曪紒

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2. 鍫嗗彔澶氬眰涓婇噰鏍凤細鏌愭柟妗堝晢鎼?灞備笂閲囨牱锛屾帹鐞嗗欢杩熼鍒?00ms+
3. 蹇界暐杈撳叆鍒嗚鲸鐜囷細4K鍥惧儚鐢?40璁粌鐩存帴涓婇噰鏍凤紝鏁堟灉鍫瘮鐏甸瓊鐢绘墜

馃挕鐙鍔ㄦ€佷笂閲囨牱绛栫暐

鎴戜滑鑷爺鐨勨€?strong>鈥嬪脊鎬х缉鏀炬ā鍧椻€?/strong>鈥嬶紙浠ｇ爜鏆備笉寮€婧愶級鑳芥牴鎹洰鏍囧瘑搴﹁嚜鍔ㄨ皟鑺傦細

• 褰撴娴嬪埌>10涓?cm虏鐨勫瘑闆嗙洰鏍囨椂锛屽惎鐢ㄥ弻璺笂閲囨牱骞惰仈
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馃鍐欑粰宸ョ▼甯堢殑鏆磋

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