--- tags: - generated_from_trainer model-index: - name: pixel-tiny-cont results: [] --- # pixel-tiny-cont This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8016 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0006 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 1024 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - training_steps: 250000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.7411 | 0.06 | 1000 | 0.9070 | | 0.7395 | 0.12 | 2000 | 0.9064 | | 0.7387 | 0.18 | 3000 | 0.9047 | | 0.7382 | 0.25 | 4000 | 0.9015 | | 0.7381 | 0.31 | 5000 | 0.9044 | | 0.7379 | 0.37 | 6000 | 0.9042 | | 0.7379 | 0.43 | 7000 | 0.9054 | | 0.7378 | 0.49 | 8000 | 0.9035 | | 0.7378 | 0.55 | 9000 | 0.9026 | | 0.7371 | 0.61 | 10000 | 0.9038 | | 0.7369 | 0.67 | 11000 | 0.9027 | | 0.7368 | 0.74 | 12000 | 0.9022 | | 0.7368 | 0.8 | 13000 | 0.8987 | | 0.7374 | 0.86 | 14000 | 0.9014 | | 0.7369 | 0.92 | 15000 | 0.9002 | | 0.7369 | 0.98 | 16000 | 0.9002 | | 0.7372 | 1.04 | 17000 | 0.9019 | | 0.737 | 1.1 | 18000 | 0.9001 | | 0.737 | 1.16 | 19000 | 0.9006 | | 0.7369 | 1.23 | 20000 | 0.9007 | | 0.7365 | 1.29 | 21000 | 0.8698 | | 0.7363 | 1.35 | 22000 | 0.8700 | | 0.7366 | 1.41 | 23000 | 0.9021 | | 0.7362 | 1.47 | 24000 | 0.8763 | | 0.7082 | 1.53 | 25000 | 0.8719 | | 0.6774 | 1.59 | 26000 | 0.8876 | | 0.6525 | 1.65 | 27000 | 0.8905 | | 0.6022 | 1.72 | 28000 | 0.8856 | | 0.5874 | 1.78 | 29000 | 0.8794 | | 0.5765 | 1.84 | 30000 | 0.8806 | | 0.5685 | 1.9 | 31000 | 0.8747 | | 0.564 | 1.96 | 32000 | 0.8779 | | 0.5606 | 2.02 | 33000 | 0.8762 | | 0.5574 | 2.08 | 34000 | 0.8703 | | 0.5528 | 2.14 | 35000 | 0.8664 | | 0.5494 | 2.21 | 36000 | 0.8717 | | 0.5448 | 2.27 | 37000 | 0.8673 | | 0.5419 | 2.33 | 38000 | 0.8637 | | 0.5385 | 2.39 | 39000 | 0.8634 | | 0.536 | 2.45 | 40000 | 0.8661 | | 0.5336 | 2.51 | 41000 | 0.8631 | | 0.5316 | 2.57 | 42000 | 0.8606 | | 0.5297 | 2.63 | 43000 | 0.8589 | | 0.5305 | 2.7 | 44000 | 0.8570 | | 0.5262 | 2.76 | 45000 | 0.8559 | | 0.5247 | 2.82 | 46000 | 0.8634 | | 0.5235 | 2.88 | 47000 | 0.8606 | | 0.5227 | 2.94 | 48000 | 0.8610 | | 0.5206 | 3.0 | 49000 | 0.8610 | | 0.5194 | 3.06 | 50000 | 0.8611 | | 0.5183 | 3.12 | 51000 | 0.8579 | | 0.5175 | 3.19 | 52000 | 0.8598 | | 0.5163 | 3.25 | 53000 | 0.8521 | | 0.5156 | 3.31 | 54000 | 0.8550 | | 0.5148 | 3.37 | 55000 | 0.8504 | | 0.5139 | 3.43 | 56000 | 0.8530 | | 0.5133 | 3.49 | 57000 | 0.8589 | | 0.5126 | 3.55 | 58000 | 0.8561 | | 0.5119 | 3.62 | 59000 | 0.8574 | | 0.5127 | 3.68 | 60000 | 0.8624 | | 0.5105 | 3.74 | 61000 | 0.8522 | | 0.5099 | 3.8 | 62000 | 0.8550 | | 0.5094 | 3.86 | 63000 | 0.8537 | | 0.509 | 3.92 | 64000 | 0.8535 | | 0.5091 | 3.98 | 65000 | 0.8592 | | 0.5079 | 4.04 | 66000 | 0.8554 | | 0.5074 | 4.11 | 67000 | 0.8516 | | 0.5069 | 4.17 | 68000 | 0.8491 | | 0.5066 | 4.23 | 69000 | 0.8571 | | 0.5068 | 4.29 | 70000 | 0.8536 | | 0.5066 | 4.35 | 71000 | 0.9288 | | 0.5051 | 4.41 | 72000 | 0.8597 | | 0.5045 | 4.47 | 73000 | 0.8555 | | 0.5043 | 4.53 | 74000 | 0.8547 | | 0.5039 | 4.6 | 75000 | 0.8561 | | 0.504 | 4.66 | 76000 | 0.8541 | | 0.5026 | 4.72 | 77000 | 0.8490 | | 0.5024 | 4.78 | 78000 | 0.8499 | | 0.5019 | 4.84 | 79000 | 0.8522 | | 0.5014 | 4.9 | 80000 | 0.8508 | | 0.5008 | 4.96 | 81000 | 0.8512 | | 0.5002 | 5.02 | 82000 | 0.8470 | | 0.4995 | 5.09 | 83000 | 0.8462 | | 0.4991 | 5.15 | 84000 | 0.8455 | | 0.4982 | 5.21 | 85000 | 0.8465 | | 0.4978 | 5.27 | 86000 | 0.8434 | | 0.4969 | 5.33 | 87000 | 0.8432 | | 0.4964 | 5.39 | 88000 | 0.8417 | | 0.4957 | 5.45 | 89000 | 0.8363 | | 0.495 | 5.51 | 90000 | 0.8392 | | 0.4946 | 5.58 | 91000 | 0.8401 | | 0.4935 | 5.64 | 92000 | 0.8373 | | 0.4929 | 5.7 | 93000 | 0.8401 | | 0.492 | 5.76 | 94000 | 0.8356 | | 0.4912 | 5.82 | 95000 | 0.8334 | | 0.4904 | 5.88 | 96000 | 0.8281 | | 0.4898 | 5.94 | 97000 | 0.8338 | | 0.4891 | 6.0 | 98000 | 0.8300 | | 0.4882 | 6.07 | 99000 | 0.8262 | | 0.4876 | 6.13 | 100000 | 0.8172 | | 0.4868 | 6.19 | 101000 | 0.8240 | | 0.4861 | 6.25 | 102000 | 0.8212 | | 0.4854 | 6.31 | 103000 | 0.8243 | | 0.4847 | 6.37 | 104000 | 0.8228 | | 0.4841 | 6.43 | 105000 | 0.8185 | | 0.4837 | 6.5 | 106000 | 0.8177 | | 0.4827 | 6.56 | 107000 | 0.8140 | | 0.4819 | 6.62 | 108000 | 0.8147 | | 0.4813 | 6.68 | 109000 | 0.8172 | | 0.4807 | 6.74 | 110000 | 0.8149 | | 0.4801 | 6.8 | 111000 | 0.8152 | | 0.4792 | 6.86 | 112000 | 0.8089 | | 0.4785 | 6.92 | 113000 | 0.8084 | | 0.4777 | 6.99 | 114000 | 0.8103 | | 0.477 | 7.05 | 115000 | 0.8104 | | 0.4772 | 7.11 | 116000 | 0.8142 | | 0.4754 | 7.17 | 117000 | 0.8159 | | 0.4748 | 7.23 | 118000 | 0.8092 | | 0.4738 | 7.29 | 119000 | 0.8036 | | 0.473 | 7.35 | 120000 | 0.8085 | | 0.4724 | 7.41 | 121000 | 0.8084 | | 0.4714 | 7.48 | 122000 | 0.8066 | | 0.4705 | 7.54 | 123000 | 0.8094 | | 0.4699 | 7.6 | 124000 | 0.8095 | | 0.4693 | 7.66 | 125000 | 0.8101 | | 0.4685 | 7.72 | 126000 | 0.8092 | | 0.4679 | 7.78 | 127000 | 0.8025 | | 0.4672 | 7.84 | 128000 | 0.8000 | | 0.4665 | 7.9 | 129000 | 0.8020 | | 0.4659 | 7.97 | 130000 | 0.8022 | | 0.4653 | 8.03 | 131000 | 0.8071 | | 0.4647 | 8.09 | 132000 | 0.7994 | | 0.4639 | 8.15 | 133000 | 0.8034 | | 0.4634 | 8.21 | 134000 | 0.8022 | | 0.4656 | 8.27 | 135000 | 0.8052 | | 0.4623 | 8.33 | 136000 | 0.7989 | | 0.4617 | 8.39 | 137000 | 0.7993 | | 0.4612 | 8.46 | 138000 | 0.8003 | | 0.4608 | 8.52 | 139000 | 0.7990 | | 0.4603 | 8.58 | 140000 | 0.8074 | | 0.4597 | 8.64 | 141000 | 0.8089 | | 0.4591 | 8.7 | 142000 | 0.8040 | | 0.4586 | 8.76 | 143000 | 0.7993 | | 0.4584 | 8.82 | 144000 | 0.8004 | | 0.4594 | 8.88 | 145000 | 0.7991 | | 0.4574 | 8.95 | 146000 | 0.7956 | | 0.4571 | 9.01 | 147000 | 0.7948 | | 0.4565 | 9.07 | 148000 | 0.7982 | | 0.4563 | 9.13 | 149000 | 0.7960 | | 0.4555 | 9.19 | 150000 | 0.8043 | | 0.4551 | 9.25 | 151000 | 0.8021 | | 0.4549 | 9.31 | 152000 | 0.7972 | | 0.4545 | 9.38 | 153000 | 0.8003 | | 0.4542 | 9.44 | 154000 | 0.8000 | | 0.4539 | 9.5 | 155000 | 0.7960 | | 0.4533 | 9.56 | 156000 | 0.8035 | | 0.453 | 9.62 | 157000 | 0.7953 | | 0.4527 | 9.68 | 158000 | 0.7937 | | 0.4524 | 9.74 | 159000 | 0.8021 | | 0.4519 | 9.8 | 160000 | 0.8028 | | 0.4517 | 9.87 | 161000 | 0.8006 | | 0.4514 | 9.93 | 162000 | 0.8067 | | 0.4512 | 9.99 | 163000 | 0.7990 | | 0.4508 | 10.05 | 164000 | 0.8041 | | 0.4504 | 10.11 | 165000 | 0.7995 | | 0.4501 | 10.17 | 166000 | 0.7979 | | 0.4499 | 10.23 | 167000 | 0.7969 | | 0.4497 | 10.29 | 168000 | 0.8041 | | 0.4495 | 10.36 | 169000 | 0.8050 | | 0.4492 | 10.42 | 170000 | 0.7999 | | 0.4494 | 10.48 | 171000 | 0.7992 | | 0.4486 | 10.54 | 172000 | 0.8019 | | 0.4485 | 10.6 | 173000 | 0.8026 | | 0.4483 | 10.66 | 174000 | 0.8009 | | 0.448 | 10.72 | 175000 | 0.8022 | | 0.4479 | 10.78 | 176000 | 0.8016 | | 0.4476 | 10.85 | 177000 | 0.7988 | | 0.4474 | 10.91 | 178000 | 0.8025 | | 0.4471 | 10.97 | 179000 | 0.8035 | | 0.4471 | 11.03 | 180000 | 0.7983 | | 0.4467 | 11.09 | 181000 | 0.8010 | | 0.4463 | 11.15 | 182000 | 0.8035 | | 0.4463 | 11.21 | 183000 | 0.8049 | | 0.4462 | 11.27 | 184000 | 0.7998 | | 0.4459 | 11.34 | 185000 | 0.7988 | | 0.4457 | 11.4 | 186000 | 0.8064 | | 0.4456 | 11.46 | 187000 | 0.8042 | | 0.4454 | 11.52 | 188000 | 0.7998 | | 0.4453 | 11.58 | 189000 | 0.8026 | | 0.4449 | 11.64 | 190000 | 0.7993 | | 0.4448 | 11.7 | 191000 | 0.8037 | | 0.4448 | 11.76 | 192000 | 0.8038 | | 0.4445 | 11.83 | 193000 | 0.8010 | | 0.4442 | 11.89 | 194000 | 0.7977 | | 0.4443 | 11.95 | 195000 | 0.8008 | | 0.4441 | 12.01 | 196000 | 0.8048 | | 0.4439 | 12.07 | 197000 | 0.8034 | | 0.4438 | 12.13 | 198000 | 0.8052 | | 0.4437 | 12.19 | 199000 | 0.8041 | | 0.4434 | 12.25 | 200000 | 0.8001 | | 0.4434 | 12.32 | 201000 | 0.8013 | | 0.4432 | 12.38 | 202000 | 0.7987 | | 0.443 | 12.44 | 203000 | 0.7962 | | 0.443 | 12.5 | 204000 | 0.8017 | | 0.4429 | 12.56 | 205000 | 0.7996 | | 0.4428 | 12.62 | 206000 | 0.7997 | | 0.4425 | 12.68 | 207000 | 0.8017 | | 0.4424 | 12.75 | 208000 | 0.8008 | | 0.4424 | 12.81 | 209000 | 0.8052 | | 0.4422 | 12.87 | 210000 | 0.8004 | | 0.4421 | 12.93 | 211000 | 0.8023 | | 0.4421 | 12.99 | 212000 | 0.8014 | | 0.442 | 13.05 | 213000 | 0.7999 | | 0.4418 | 13.11 | 214000 | 0.8019 | | 0.4417 | 13.17 | 215000 | 0.7996 | | 0.4416 | 13.24 | 216000 | 0.8007 | | 0.4414 | 13.3 | 217000 | 0.8029 | | 0.4415 | 13.36 | 218000 | 0.7990 | | 0.4413 | 13.42 | 219000 | 0.7997 | | 0.4413 | 13.48 | 220000 | 0.7997 | | 0.4412 | 13.54 | 221000 | 0.7996 | | 0.4411 | 13.6 | 222000 | 0.8003 | | 0.4411 | 13.66 | 223000 | 0.7993 | | 0.4411 | 13.73 | 224000 | 0.8005 | | 0.4409 | 13.79 | 225000 | 0.8013 | | 0.4409 | 13.85 | 226000 | 0.8016 | | 0.4409 | 13.91 | 227000 | 0.7994 | | 0.4408 | 13.97 | 228000 | 0.8023 | | 0.4407 | 14.03 | 229000 | 0.8013 | | 0.4406 | 14.09 | 230000 | 0.8038 | | 0.4408 | 14.15 | 231000 | 0.7994 | | 0.4406 | 14.22 | 232000 | 0.8007 | | 0.4404 | 14.28 | 233000 | 0.8006 | | 0.4403 | 14.34 | 234000 | 0.7987 | | 0.4405 | 14.4 | 235000 | 0.8010 | | 0.4404 | 14.46 | 236000 | 0.7982 | | 0.4404 | 14.52 | 237000 | 0.7985 | | 0.4403 | 14.58 | 238000 | 0.8016 | | 0.4402 | 14.64 | 239000 | 0.8025 | | 0.4402 | 14.71 | 240000 | 0.8020 | | 0.4401 | 14.77 | 241000 | 0.8009 | | 0.4401 | 14.83 | 242000 | 0.8015 | | 0.4401 | 14.89 | 243000 | 0.8010 | | 0.44 | 14.95 | 244000 | 0.7996 | | 0.4402 | 15.01 | 245000 | 0.8014 | | 0.44 | 15.07 | 246000 | 0.8007 | | 0.44 | 15.13 | 247000 | 0.7984 | | 0.44 | 15.2 | 248000 | 0.8009 | | 0.4399 | 15.26 | 249000 | 0.8006 | | 0.4399 | 15.32 | 250000 | 0.8016 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.1.1.dev0 - Tokenizers 0.12.1