learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 300
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Apple	Mar 100 Apple
No log	1.0	169	14.5718	0.1228	0.2421	0.1084	-1.0	0.0157	0.1399	0.1375	0.3146	0.6275	-1.0	0.5333	0.6277	0.1228	0.6275
No log	2.0	338	9.2170	0.5022	0.692	0.5452	-1.0	0.3272	0.5035	0.225	0.5618	0.8138	-1.0	0.9333	0.8136	0.5022	0.8138
30.1672	3.0	507	8.8104	0.5503	0.6829	0.6163	-1.0	0.4455	0.5516	0.2309	0.6075	0.8643	-1.0	0.9	0.8642	0.5503	0.8643
30.1672	4.0	676	8.0371	0.6275	0.7697	0.7078	-1.0	0.4374	0.6279	0.2427	0.6525	0.8602	-1.0	0.9333	0.86	0.6275	0.8602
30.1672	5.0	845	7.0432	0.6851	0.807	0.778	-1.0	0.521	0.6858	0.2448	0.6939	0.8754	-1.0	0.7667	0.8757	0.6851	0.8754
12.3777	6.0	1014	7.4024	0.7262	0.8607	0.8231	-1.0	0.4729	0.729	0.2437	0.6998	0.8842	-1.0	0.9	0.8842	0.7262	0.8842
12.3777	7.0	1183	7.6969	0.7006	0.8097	0.7746	-1.0	0.417	0.7031	0.2443	0.7104	0.8897	-1.0	0.7667	0.89	0.7006	0.8897
12.3777	8.0	1352	7.0771	0.7536	0.8664	0.8397	-1.0	0.7023	0.7541	0.249	0.7151	0.891	-1.0	0.9333	0.8909	0.7536	0.891
11.4378	9.0	1521	7.6821	0.7493	0.8728	0.8397	-1.0	0.5096	0.7509	0.2484	0.7257	0.9009	-1.0	0.9	0.9009	0.7493	0.9009
11.4378	10.0	1690	7.0789	0.7901	0.9168	0.8872	-1.0	0.5995	0.7907	0.2459	0.7272	0.8985	-1.0	0.8667	0.8985	0.7901	0.8985
11.4378	11.0	1859	7.1990	0.7908	0.894	0.8677	-1.0	0.6183	0.7918	0.2503	0.7353	0.9113	-1.0	0.9	0.9114	0.7908	0.9113
10.5157	12.0	2028	6.6454	0.7664	0.8872	0.8547	-1.0	0.5614	0.7677	0.248	0.7315	0.8994	-1.0	0.8667	0.8995	0.7664	0.8994
10.5157	13.0	2197	7.4329	0.7401	0.8425	0.812	-1.0	0.7072	0.7409	0.2485	0.7313	0.902	-1.0	0.9	0.902	0.7401	0.902
10.5157	14.0	2366	6.7330	0.8094	0.9061	0.8781	-1.0	0.7158	0.8098	0.2523	0.7525	0.9168	-1.0	0.9	0.9169	0.8094	0.9168
9.8125	15.0	2535	6.5761	0.8023	0.9071	0.8815	-1.0	0.7502	0.8029	0.2528	0.7456	0.9108	-1.0	0.8667	0.9109	0.8023	0.9108
9.8125	16.0	2704	6.5281	0.818	0.919	0.8961	-1.0	0.6455	0.8185	0.252	0.7501	0.9141	-1.0	0.9	0.9141	0.818	0.9141
9.8125	17.0	2873	6.6390	0.8178	0.9158	0.8959	-1.0	0.7281	0.8189	0.251	0.7423	0.9179	-1.0	0.9	0.918	0.8178	0.9179
9.4182	18.0	3042	6.4843	0.8341	0.9298	0.9035	-1.0	0.6102	0.8347	0.2507	0.7526	0.9203	-1.0	0.8667	0.9205	0.8341	0.9203
9.4182	19.0	3211	7.2777	0.7999	0.9018	0.8787	-1.0	0.6338	0.8015	0.249	0.7413	0.9173	-1.0	0.9	0.9173	0.7999	0.9173
9.4182	20.0	3380	6.6993	0.8291	0.9234	0.8972	-1.0	0.7493	0.8301	0.2516	0.7537	0.9245	-1.0	0.9333	0.9245	0.8291	0.9245
9.0821	21.0	3549	6.8164	0.8154	0.9222	0.9018	-1.0	0.7889	0.8158	0.2533	0.744	0.9167	-1.0	0.9333	0.9166	0.8154	0.9167
9.0821	22.0	3718	6.7705	0.8158	0.9272	0.9034	-1.0	0.6072	0.8169	0.2473	0.7421	0.9116	-1.0	0.9	0.9117	0.8158	0.9116
9.0821	23.0	3887	5.8021	0.838	0.9356	0.9121	-1.0	0.6007	0.8391	0.2505	0.7538	0.9247	-1.0	0.9	0.9248	0.838	0.9247
8.7442	24.0	4056	6.3070	0.8475	0.9312	0.9098	-1.0	0.7521	0.8481	0.2531	0.7582	0.9282	-1.0	0.9333	0.9282	0.8475	0.9282
8.7442	25.0	4225	5.7625	0.8491	0.9336	0.9136	-1.0	0.7109	0.8498	0.2511	0.7639	0.9285	-1.0	0.9333	0.9285	0.8491	0.9285
8.7442	26.0	4394	5.9863	0.8284	0.9285	0.9072	-1.0	0.5891	0.8295	0.2525	0.7493	0.9185	-1.0	0.7667	0.9188	0.8284	0.9185
8.3969	27.0	4563	6.0623	0.8271	0.9343	0.9078	-1.0	0.5669	0.8277	0.2521	0.7518	0.9097	-1.0	0.8	0.91	0.8271	0.9097
8.3969	28.0	4732	6.2344	0.8329	0.9302	0.9033	-1.0	0.5902	0.8336	0.2519	0.7548	0.9247	-1.0	0.9	0.9248	0.8329	0.9247
8.3969	29.0	4901	6.1610	0.8294	0.9177	0.8965	-1.0	0.6745	0.8304	0.2503	0.7544	0.9315	-1.0	0.9	0.9315	0.8294	0.9315
8.2127	30.0	5070	6.5000	0.8297	0.9219	0.9001	-1.0	0.6378	0.8305	0.2508	0.7541	0.9225	-1.0	0.8667	0.9227	0.8297	0.9225

Framework versions

Transformers 4.49.0.dev0
Pytorch 2.5.1+cu121
Tokenizers 0.21.0

firdhokk/apple-detection-with-rtdetr-rd50vd-coco-o365

apple-detection-with-rtdetr-rd50vd-coco-o365

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

firdhokk/
apple-detection-with-rtdetr-rd50vd-coco-o365