***************
** Arguments **
***************
backbone: 
config_file: configs/trainers/CoOp/vit_b16_ep50_bs4.yaml
dataset_config_file: configs/datasets/imagenet.yaml
eval_only: True
head: 
load_epoch: None
model_dir: 
no_train: False
opts: ['DATASET.SUBSAMPLE_CLASSES', 'new_ratio4']
output_dir: output/new_ratio4/ZeroshotCLIP/vit_b16_ep50_bs4/imagenet/3
resume: 
root: /mnt/hdd/DATA
seed: 3
source_domains: None
target_domains: None
trainer: ZeroshotCLIP
transforms: None
************
** Config **
************
DATALOADER:
  K_TRANSFORMS: 1
  NUM_WORKERS: 8
  RETURN_IMG0: False
  TEST:
    BATCH_SIZE: 100
    SAMPLER: SequentialSampler
  TRAIN_U:
    BATCH_SIZE: 32
    N_DOMAIN: 0
    N_INS: 16
    SAME_AS_X: True
    SAMPLER: RandomSampler
  TRAIN_X:
    BATCH_SIZE: 4
    N_DOMAIN: 0
    N_INS: 16
    SAMPLER: RandomSampler
DATASET:
  ALL_AS_UNLABELED: False
  CIFAR_C_LEVEL: 1
  CIFAR_C_TYPE: 
  NAME: ImageNet
  NUM_LABELED: -1
  NUM_SHOTS: -1
  ROOT: /mnt/hdd/DATA
  SOURCE_DOMAINS: ()
  STL10_FOLD: -1
  SUBSAMPLE_CLASSES: new_ratio4
  TARGET_DOMAINS: ()
  VAL_PERCENT: 0.1
INPUT:
  COLORJITTER_B: 0.4
  COLORJITTER_C: 0.4
  COLORJITTER_H: 0.1
  COLORJITTER_S: 0.4
  CROP_PADDING: 4
  CUTOUT_LEN: 16
  CUTOUT_N: 1
  GB_K: 21
  GB_P: 0.5
  GN_MEAN: 0.0
  GN_STD: 0.15
  INTERPOLATION: bicubic
  NO_TRANSFORM: False
  PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
  PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
  RANDAUGMENT_M: 10
  RANDAUGMENT_N: 2
  RGS_P: 0.2
  RRCROP_SCALE: (0.08, 1.0)
  SIZE: (224, 224)
  TRANSFORMS: ('random_resized_crop', 'random_flip', 'normalize')
MODEL:
  BACKBONE:
    NAME: ViT-B/16
    PRETRAINED: True
  HEAD:
    ACTIVATION: relu
    BN: True
    DROPOUT: 0.0
    HIDDEN_LAYERS: ()
    NAME: 
  INIT_WEIGHTS: 
OPTIM:
  ADAM_BETA1: 0.9
  ADAM_BETA2: 0.999
  BASE_LR_MULT: 0.1
  GAMMA: 0.1
  LR: 0.002
  LR_SCHEDULER: cosine
  MAX_EPOCH: 50
  MOMENTUM: 0.9
  NAME: sgd
  NEW_LAYERS: ()
  RMSPROP_ALPHA: 0.99
  SGD_DAMPNING: 0
  SGD_NESTEROV: False
  STAGED_LR: False
  STEPSIZE: (-1,)
  WARMUP_CONS_LR: 1e-05
  WARMUP_EPOCH: 1
  WARMUP_MIN_LR: 1e-05
  WARMUP_RECOUNT: True
  WARMUP_TYPE: constant
  WEIGHT_DECAY: 0.0005
OUTPUT_DIR: output/new_ratio4/ZeroshotCLIP/vit_b16_ep50_bs4/imagenet/3
RESUME: 
SEED: 3
TEST:
  COMPUTE_CMAT: False
  EVALUATOR: Classification
  FINAL_MODEL: last_step
  NO_TEST: False
  PER_CLASS_RESULT: False
  SPLIT: test
TRAIN:
  CHECKPOINT_FREQ: 0
  COUNT_ITER: train_x
  PRINT_FREQ: 20
TRAINER:
  CDAC:
    CLASS_LR_MULTI: 10
    P_THRESH: 0.95
    RAMPUP_COEF: 30
    RAMPUP_ITRS: 1000
    STRONG_TRANSFORMS: ()
    TOPK_MATCH: 5
  COCOOP:
    CTX_INIT: 
    N_CTX: 16
    PREC: fp16
  COOP:
    CLASS_TOKEN_POSITION: end
    CSC: False
    CTX_INIT: 
    N_CTX: 16
    PREC: fp16
  CROSSGRAD:
    ALPHA_D: 0.5
    ALPHA_F: 0.5
    EPS_D: 1.0
    EPS_F: 1.0
  DAEL:
    CONF_THRE: 0.95
    STRONG_TRANSFORMS: ()
    WEIGHT_U: 0.5
  DAELDG:
    CONF_THRE: 0.95
    STRONG_TRANSFORMS: ()
    WEIGHT_U: 0.5
  DDAIG:
    ALPHA: 0.5
    CLAMP: False
    CLAMP_MAX: 1.0
    CLAMP_MIN: -1.0
    G_ARCH: 
    LMDA: 0.3
    WARMUP: 0
  DOMAINMIX:
    ALPHA: 1.0
    BETA: 1.0
    TYPE: crossdomain
  ENTMIN:
    LMDA: 0.001
  FIXMATCH:
    CONF_THRE: 0.95
    STRONG_TRANSFORMS: ()
    WEIGHT_U: 1.0
  IVLP:
    CTX_INIT: a photo of a
    N_CTX_TEXT: 2
    N_CTX_VISION: 2
    PREC: fp16
    PROMPT_DEPTH_TEXT: 9
    PROMPT_DEPTH_VISION: 9
  M3SDA:
    LMDA: 0.5
    N_STEP_F: 4
  MAPLE:
    CTX_INIT: a photo of a
    N_CTX: 4
    PREC: fp16
    PROMPT_DEPTH: 9
  MCD:
    N_STEP_F: 4
  MEANTEACHER:
    EMA_ALPHA: 0.999
    RAMPUP: 5
    WEIGHT_U: 1.0
  MIXMATCH:
    MIXUP_BETA: 0.75
    RAMPUP: 20000
    TEMP: 2.0
    WEIGHT_U: 100.0
  MME:
    LMDA: 0.1
  NAME: ZeroshotCLIP
  ProDA:
    N_CTX: 16
    N_PROMPT: 32
    PREC: fp16
  SE:
    CONF_THRE: 0.95
    EMA_ALPHA: 0.999
    RAMPUP: 300
  VPT:
    CTX_INIT: a photo of a
    N_CTX_VISION: 2
    PREC: fp16
    PROMPT_DEPTH_VISION: 1
USE_CUDA: True
VERBOSE: True
VERSION: 1
Collecting env info ...
** System info **
PyTorch version: 2.2.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 12 (bookworm) (x86_64)
GCC version: (Debian 12.2.0-14) 12.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.36

Python version: 3.11.2 (main, Mar 13 2023, 12:18:29) [GCC 12.2.0] (64-bit runtime)
Python platform: Linux-6.5.13-3-pve-x86_64-with-glibc2.36
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A800 80GB PCIe
GPU 1: NVIDIA A800 80GB PCIe

Nvidia driver version: 525.147.05
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             64
On-line CPU(s) list:                18,20,22,23,25-27,29,31,32,34,37,46-49
Off-line CPU(s) list:               0-17,19,21,24,28,30,33,35,36,38-45,50-63
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz
CPU family:                         6
Model:                              106
Thread(s) per core:                 2
Core(s) per socket:                 16
Socket(s):                          2
Stepping:                           6
CPU(s) scaling MHz:                 98%
CPU max MHz:                        3500.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5800.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          1.5 MiB (32 instances)
L1i cache:                          1 MiB (32 instances)
L2 cache:                           40 MiB (32 instances)
L3 cache:                           48 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-15,32-47
NUMA node1 CPU(s):                  16-31,48-63
Vulnerability Gather data sampling: Vulnerable: No microcode
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] flake8==3.7.9
[pip3] flake8==3.7.9
[pip3] numpy==1.26.4
[pip3] torch==2.2.1
[pip3] torchaudio==2.2.1
[pip3] torchvision==0.17.1
[pip3] triton==2.2.0
[conda] Could not collect
        Pillow (10.2.0)

Loading trainer: ZeroshotCLIP
Loading dataset: ImageNet
Loading preprocessed few-shot data from /mnt/hdd/DATA/imagenet/split_fewshot/shot_-1_shuffled-seed_3.pkl
SUBSAMPLE NEW_RATIO4 CLASSES!
Building transform_train
+ random resized crop (size=(224, 224), scale=(0.08, 1.0))
+ random flip
+ to torch tensor of range [0, 1]
+ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
Building transform_test
+ resize the smaller edge to 224
+ 224x224 center crop
+ to torch tensor of range [0, 1]
+ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
---------  --------
Dataset    ImageNet
# classes  500
# train_x  641,873
# val      25,000
# test     25,000
---------  --------
Loading CLIP (backbone: ViT-B/16)
Prompts: ['a photo of a candy store.', 'a photo of a tow truck.', 'a photo of a photocopier.', 'a photo of a apiary.', 'a photo of a duck.', 'a photo of a graduation cap.', 'a photo of a holster.', 'a photo of a bolo tie.', 'a photo of a moving van.', 'a photo of a container ship.', 'a photo of a pufferfish.', 'a photo of a chimpanzee.', 'a photo of a hare.', 'a photo of a fiddler crab.', 'a photo of a dome.', 'a photo of a park bench.', 'a photo of a vulture.', 'a photo of a black-and-white colobus.', 'a photo of a academic gown.', 'a photo of a stone wall.', 'a photo of a space heater.', 'a photo of a stinkhorn mushroom.', 'a photo of a Great Dane.', 'a photo of a indigo bunting.', 'a photo of a pill bottle.', 'a photo of a sink.', 'a photo of a triumphal arch.', 'a photo of a barrel.', 'a photo of a parking meter.', 'a photo of a baseball player.', 'a photo of a strawberry.', 'a photo of a pot pie.', 'a photo of a jeans.', 'a photo of a spatula.', 'a photo of a beach.', 'a photo of a Gordon Setter.', 'a photo of a gas pump.', 'a photo of a artichoke.', 'a photo of a castle.', 'a photo of a Bluetick Coonhound.', 'a photo of a matchstick.', 'a photo of a snorkel.', 'a photo of a joystick.', 'a photo of a knot.', 'a photo of a diaper.', 'a photo of a hoop skirt.', 'a photo of a apron.', 'a photo of a carbonara.', 'a photo of a cheeseburger.', 'a photo of a spaghetti squash.', 'a photo of a Papillon.', 'a photo of a bolete.', 'a photo of a church.', 'a photo of a chain-link fence.', 'a photo of a cassette player.', 'a photo of a brass memorial plaque.', 'a photo of a leaf beetle.', 'a photo of a parallel bars.', 'a photo of a trench coat.', 'a photo of a shopping basket.', 'a photo of a warthog.', 'a photo of a Chihuahua.', 'a photo of a pizza.', 'a photo of a music speaker.', 'a photo of a clogs.', 'a photo of a gas mask or respirator.', 'a photo of a Dungeness crab.', 'a photo of a Basset Hound.', 'a photo of a Pembroke Welsh Corgi.', 'a photo of a meerkat.', 'a photo of a southern black widow.', 'a photo of a electric fan.', 'a photo of a flute.', 'a photo of a fig.', 'a photo of a notebook computer.', 'a photo of a jacamar.', 'a photo of a military hat (bearskin or shako).', 'a photo of a suit.', 'a photo of a tricycle.', 'a photo of a balaclava ski mask.', 'a photo of a Christmas stocking.', 'a photo of a mink.', 'a photo of a cauldron.', 'a photo of a gyromitra.', 'a photo of a promontory.', 'a photo of a loupe magnifying glass.', 'a photo of a Nile crocodile.', 'a photo of a prairie grouse.', 'a photo of a tarantula.', 'a photo of a bookstore.', 'a photo of a bra.', 'a photo of a Flat-Coated Retriever.', 'a photo of a prayer rug.', 'a photo of a dam.', 'a photo of a mousetrap.', 'a photo of a hartebeest.', 'a photo of a space shuttle.', 'a photo of a can opener.', 'a photo of a ox.', 'a photo of a polar bear.', 'a photo of a red panda.', 'a photo of a dock.', 'a photo of a water bottle.', 'a photo of a English Setter.', 'a photo of a ruffed grouse.', 'a photo of a typewriter keyboard.', 'a photo of a hair dryer.', 'a photo of a water tower.', 'a photo of a Vizsla.', 'a photo of a soup bowl.', 'a photo of a movie theater.', 'a photo of a Appenzeller Sennenhund.', 'a photo of a sewing machine.', 'a photo of a fire truck.', 'a photo of a quail.', 'a photo of a gown.', 'a photo of a European polecat.', 'a photo of a coral reef.', 'a photo of a proboscis monkey.', 'a photo of a milk can.', 'a photo of a aircraft carrier.', 'a photo of a bulletproof vest.', 'a photo of a Leonberger.', 'a photo of a desk.', 'a photo of a St. Bernard.', 'a photo of a centipede.', 'a photo of a Irish Terrier.', 'a photo of a Angora rabbit.', 'a photo of a tea cup.', 'a photo of a echidna.', 'a photo of a brain coral.', 'a photo of a monastery.', 'a photo of a crutch.', 'a photo of a box turtle.', 'a photo of a turnstile.', 'a photo of a rhinoceros beetle.', 'a photo of a vaulted or arched ceiling.', 'a photo of a washing machine.', 'a photo of a chambered nautilus.', 'a photo of a smooth newt.', 'a photo of a lakeshore.', 'a photo of a miniskirt.', 'a photo of a great grey owl.', 'a photo of a Kerry Blue Terrier.', 'a photo of a indri.', 'a photo of a tusker.', 'a photo of a hummingbird.', 'a photo of a lotion.', 'a photo of a toy store.', 'a photo of a sandal.', 'a photo of a website.', 'a photo of a albatross.', 'a photo of a limousine.', 'a photo of a electric ray.', 'a photo of a stethoscope.', 'a photo of a Dutch oven.', 'a photo of a gossamer-winged butterfly.', 'a photo of a nematode.', 'a photo of a snowmobile.', 'a photo of a drink pitcher.', 'a photo of a wok.', 'a photo of a pedestal.', 'a photo of a crayfish.', 'a photo of a Mexican hairless dog (xoloitzcuintli).', 'a photo of a ring-tailed lemur.', 'a photo of a hamster.', 'a photo of a spotted salamander.', 'a photo of a hen of the woods mushroom.', 'a photo of a station wagon.', 'a photo of a plant pot.', 'a photo of a bathtub.', 'a photo of a rotisserie.', 'a photo of a hyena.', 'a photo of a table lamp.', 'a photo of a spindle.', 'a photo of a metal nail.', 'a photo of a magpie.', 'a photo of a ping-pong ball.', 'a photo of a wheelbarrow.', 'a photo of a basketball.', 'a photo of a T-shirt.', 'a photo of a computer keyboard.', "a photo of a Geoffroy's spider monkey.", 'a photo of a mitten.', 'a photo of a traffic light.', 'a photo of a alligator lizard.', 'a photo of a horse chestnut seed.', 'a photo of a military aircraft.', 'a photo of a stretcher.', 'a photo of a common squirrel monkey.', 'a photo of a sunglasses.', 'a photo of a Border Terrier.', 'a photo of a greenhouse.', 'a photo of a toaster.', 'a photo of a snoek fish.', 'a photo of a tiger shark.', 'a photo of a fur coat.', 'a photo of a flamingo.', 'a photo of a isopod.', 'a photo of a beaker.', 'a photo of a marmot.', 'a photo of a pirate ship.', 'a photo of a red wolf or maned wolf.', 'a photo of a siamang.', 'a photo of a grasshopper.', 'a photo of a tile roof.', 'a photo of a abacus.', 'a photo of a strainer.', 'a photo of a shipwreck.', 'a photo of a barn spider.', 'a photo of a shoe store.', 'a photo of a Afghan Hound.', 'a photo of a monarch butterfly.', 'a photo of a seat belt.', 'a photo of a cowboy boot.', 'a photo of a desktop computer.', 'a photo of a macaw.', 'a photo of a overskirt.', 'a photo of a bell or wind chime.', 'a photo of a harvestman.', 'a photo of a steel drum.', 'a photo of a vase.', 'a photo of a chameleon.', 'a photo of a spoonbill.', 'a photo of a kingsnake.', 'a photo of a goldfish.', 'a photo of a cottontail rabbit.', 'a photo of a mushroom.', 'a photo of a worm snake.', 'a photo of a swimming cap.', 'a photo of a tram.', 'a photo of a doormat.', 'a photo of a fishing casting reel.', 'a photo of a pug.', 'a photo of a pencil case.', 'a photo of a triceratops.', 'a photo of a analog clock.', 'a photo of a hair clip.', 'a photo of a cleaver.', 'a photo of a teddy bear.', 'a photo of a little blue heron.', 'a photo of a grey fox.', 'a photo of a French Bulldog.', 'a photo of a mortar and pestle.', 'a photo of a whiskey jug.', 'a photo of a electric locomotive.', 'a photo of a hermit crab.', 'a photo of a electrical switch.', 'a photo of a Briard.', 'a photo of a otter.', 'a photo of a CRT monitor.', 'a photo of a shopping cart.', 'a photo of a sandbar.', 'a photo of a Samoyed.', 'a photo of a black stork.', 'a photo of a oboe.', 'a photo of a chiton.', 'a photo of a lighter.', 'a photo of a eastern hog-nosed snake.', 'a photo of a pan flute.', 'a photo of a envelope.', 'a photo of a dog sled.', 'a photo of a snowplow.', 'a photo of a race car.', 'a photo of a rifle.', 'a photo of a cowboy hat.', 'a photo of a Otterhound.', 'a photo of a planetarium.', 'a photo of a storage chest.', 'a photo of a corn.', 'a photo of a American alligator.', 'a photo of a mashed potatoes.', 'a photo of a beer bottle.', 'a photo of a West Highland White Terrier.', 'a photo of a cuirass.', 'a photo of a mud turtle.', 'a photo of a toilet seat.', 'a photo of a Siberian Husky.', 'a photo of a American lobster.', 'a photo of a cheetah.', 'a photo of a gymnastic horizontal bar.', 'a photo of a trilobite.', 'a photo of a Border Collie.', 'a photo of a bighorn sheep.', 'a photo of a fire screen.', 'a photo of a American black bear.', 'a photo of a digital watch.', 'a photo of a neck brace.', 'a photo of a cherimoya (custard apple).', 'a photo of a loggerhead sea turtle.', 'a photo of a radio.', 'a photo of a scarf.', 'a photo of a tandem bicycle.', 'a photo of a croquet ball.', 'a photo of a mailbox.', 'a photo of a chain.', 'a photo of a hammerhead shark.', 'a photo of a Lhasa Apso.', 'a photo of a suspension bridge.', 'a photo of a barber chair.', 'a photo of a tennis ball.', 'a photo of a Band-Aid.', 'a photo of a radiator grille.', 'a photo of a baseball.', 'a photo of a sloth bear.', 'a photo of a barometer.', 'a photo of a spotlight.', 'a photo of a arabian camel.', 'a photo of a car mirror.', 'a photo of a plate rack.', 'a photo of a digital clock.', 'a photo of a swim trunks / shorts.', 'a photo of a crossword.', 'a photo of a great egret.', 'a photo of a screw.', 'a photo of a gazelle.', 'a photo of a sawmill.', 'a photo of a african grey parrot.', 'a photo of a missile.', 'a photo of a ram (adult male sheep).', 'a photo of a tray.', 'a photo of a Yorkshire Terrier.', 'a photo of a hamper.', 'a photo of a soap dispenser.', 'a photo of a Boston Terrier.', 'a photo of a drumstick.', 'a photo of a leatherback sea turtle.', 'a photo of a spiny lobster.', 'a photo of a Shetland Sheepdog.', 'a photo of a cabbage.', 'a photo of a breastplate.', 'a photo of a refrigerator.', 'a photo of a Schipperke.', 'a photo of a hockey puck.', 'a photo of a wine bottle.', 'a photo of a ptarmigan.', 'a photo of a Ibizan Hound.', 'a photo of a birdhouse.', 'a photo of a Old English Sheepdog.', 'a photo of a oxygen mask.', 'a photo of a pig.', 'a photo of a sea cucumber.', 'a photo of a salt shaker.', 'a photo of a wallaby.', "a photo of a yellow lady's slipper.", 'a photo of a plectrum.', 'a photo of a moped.', 'a photo of a bikini.', 'a photo of a paddle wheel.', 'a photo of a Airedale Terrier.', 'a photo of a fox squirrel.', 'a photo of a Tibetan Mastiff.', 'a photo of a burrito.', 'a photo of a mosquito net.', 'a photo of a Australian Kelpie.', 'a photo of a corkscrew.', 'a photo of a vestment.', 'a photo of a crane bird.', 'a photo of a racket.', 'a photo of a paper towel.', 'a photo of a wallet.', 'a photo of a newt.', 'a photo of a jellyfish.', 'a photo of a stupa.', 'a photo of a weevil.', 'a photo of a Alaskan tundra wolf.', 'a photo of a stopwatch.', 'a photo of a torch.', 'a photo of a damselfly.', 'a photo of a Lakeland Terrier.', 'a photo of a baboon.', 'a photo of a Bedlington Terrier.', 'a photo of a starfish.', 'a photo of a knee pad.', 'a photo of a corn cob.', 'a photo of a dung beetle.', 'a photo of a butcher shop.', 'a photo of a mask.', 'a photo of a Great Pyrenees dog.', 'a photo of a earth star fungus.', 'a photo of a kite (bird of prey).', 'a photo of a product packet / packaging.', 'a photo of a African rock python.', 'a photo of a slot machine.', 'a photo of a bald eagle.', 'a photo of a cloak.', 'a photo of a radio telescope.', 'a photo of a thimble.', 'a photo of a pinwheel.', 'a photo of a letter opener.', 'a photo of a shovel.', 'a photo of a padlock.', 'a photo of a skunk.', 'a photo of a tool kit.', 'a photo of a zebra.', 'a photo of a marimba.', 'a photo of a red wine.', 'a photo of a boa constrictor.', 'a photo of a common gallinule.', 'a photo of a measuring cup.', 'a photo of a horse-drawn vehicle.', 'a photo of a slip-on shoe.', 'a photo of a couch.', 'a photo of a trimaran.', 'a photo of a ringlet butterfly.', 'a photo of a harmonica.', 'a photo of a semi-trailer truck.', 'a photo of a goldfinch.', 'a photo of a sunglasses.', 'a photo of a forklift.', 'a photo of a ant.', 'a photo of a dishcloth.', 'a photo of a consomme.', 'a photo of a binoculars.', 'a photo of a traffic or street sign.', 'a photo of a toilet paper.', 'a photo of a Polaroid camera.', 'a photo of a soda bottle.', 'a photo of a megalith.', 'a photo of a beer glass.', 'a photo of a red fox.', 'a photo of a comic book.', 'a photo of a magnetic compass.', 'a photo of a koala.', 'a photo of a goblet.', 'a photo of a pajamas.', 'a photo of a cardoon.', 'a photo of a mixing bowl.', 'a photo of a yurt.', 'a photo of a dining table.', 'a photo of a cannon.', 'a photo of a Asian elephant.', 'a photo of a tobacco shop.', 'a photo of a common redshank.', 'a photo of a gibbon.', 'a photo of a red admiral butterfly.', 'a photo of a bison.', 'a photo of a banana.', 'a photo of a patio.', 'a photo of a Rhodesian Ridgeback.', 'a photo of a grey wolf.', 'a photo of a lifeboat.', 'a photo of a mongoose.', 'a photo of a wooden spoon.', 'a photo of a Soft-coated Wheaten Terrier.', 'a photo of a tailed frog.', 'a photo of a entertainment center.', 'a photo of a kimono.', 'a photo of a flatworm.', 'a photo of a tick.', 'a photo of a rooster.', 'a photo of a leafhopper.', 'a photo of a popsicle.', 'a photo of a Entlebucher Sennenhund.', 'a photo of a construction crane.', 'a photo of a Egyptian Mau.', 'a photo of a bridegroom.', 'a photo of a guinea pig.', 'a photo of a hook.', 'a photo of a poncho.', 'a photo of a African bush elephant.', 'a photo of a European green lizard.', 'a photo of a school bus.', 'a photo of a lipstick.', 'a photo of a lampshade.', 'a photo of a Gila monster.', 'a photo of a red king crab.', 'a photo of a barn.', 'a photo of a shield.', 'a photo of a zucchini.', 'a photo of a tiger beetle.', 'a photo of a American dipper.', 'a photo of a mountain.', 'a photo of a banjo.', 'a photo of a sidewinder rattlesnake.', 'a photo of a stage.', 'a photo of a impala (antelope).', 'a photo of a lorikeet.', 'a photo of a china cabinet.', 'a photo of a cliff.', 'a photo of a trolleybus.', 'a photo of a chiffonier.', 'a photo of a green mamba.', 'a photo of a white stork.', 'a photo of a lion.', 'a photo of a dingo.', 'a photo of a sailboat.', 'a photo of a television.', 'a photo of a medicine cabinet.', 'a photo of a Clumber Spaniel.', 'a photo of a mountain bike.', 'a photo of a farm plow.', 'a photo of a wombat.', 'a photo of a grocery store.', 'a photo of a hair spray.', 'a photo of a fireboat.', 'a photo of a porcupine.', 'a photo of a bubble.', 'a photo of a Pickelhaube.', 'a photo of a English foxhound.']
Loading evaluator: Classification
Note that load_model() is skipped as no pretrained model is given (ignore this if it's done on purpose)
Evaluate on the *test* set
=> result
* total: 25,000
* correct: 18,773
* accuracy: 75.09%
* error: 24.91%
* macro_f1: 74.72%
