***************
** 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/1
resume: 
root: /mnt/hdd/DATA
seed: 1
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/1
RESUME: 
SEED: 1
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_1.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  641,778
# val      25,000
# test     25,000
---------  --------
Loading CLIP (backbone: ViT-B/16)
Prompts: ['a photo of a ambulance.', 'a photo of a Basenji.', 'a photo of a sarong.', 'a photo of a American lobster.', 'a photo of a sea slug.', 'a photo of a langur.', 'a photo of a gas pump.', 'a photo of a water jug.', 'a photo of a bobsleigh.', 'a photo of a Angora rabbit.', 'a photo of a electrical switch.', 'a photo of a hamper.', 'a photo of a Alaskan tundra wolf.', 'a photo of a lorikeet.', 'a photo of a tiger shark.', 'a photo of a beer bottle.', 'a photo of a rock crab.', 'a photo of a Gila monster.', 'a photo of a Pickelhaube.', 'a photo of a bell pepper.', 'a photo of a mountain bike.', 'a photo of a mousetrap.', 'a photo of a megalith.', 'a photo of a common redshank.', 'a photo of a stage.', 'a photo of a shower cap.', 'a photo of a baseball.', 'a photo of a whiskey jug.', 'a photo of a oboe.', 'a photo of a harvestman.', 'a photo of a cabbage.', 'a photo of a spatula.', 'a photo of a spotlight.', 'a photo of a shopping cart.', 'a photo of a pomegranate.', 'a photo of a tiger.', 'a photo of a Irish Setter.', 'a photo of a cottontail rabbit.', 'a photo of a viaduct.', 'a photo of a gymnastic horizontal bar.', 'a photo of a water tower.', 'a photo of a sundial.', 'a photo of a african grey parrot.', 'a photo of a microwave oven.', 'a photo of a wheelbarrow.', 'a photo of a Soft-coated Wheaten Terrier.', 'a photo of a sweatshirt.', 'a photo of a drumstick.', 'a photo of a butcher shop.', 'a photo of a bathtub.', 'a photo of a zucchini.', 'a photo of a red fox.', 'a photo of a library.', 'a photo of a tabby cat.', 'a photo of a ford model t.', 'a photo of a washing machine.', 'a photo of a plunger.', 'a photo of a swing.', 'a photo of a tailed frog.', 'a photo of a abacus.', 'a photo of a spoonbill.', 'a photo of a isopod.', 'a photo of a pig.', 'a photo of a beer glass.', 'a photo of a steam locomotive.', 'a photo of a rifle.', 'a photo of a black grouse.', 'a photo of a shield.', 'a photo of a pineapple.', 'a photo of a Maltese.', 'a photo of a spiral or coil.', 'a photo of a chain-link fence.', 'a photo of a butternut squash.', 'a photo of a common squirrel monkey.', 'a photo of a basketball.', 'a photo of a clothes iron.', 'a photo of a buckle.', 'a photo of a hippopotamus.', 'a photo of a television.', 'a photo of a revolver.', 'a photo of a broom.', 'a photo of a clogs.', 'a photo of a Basset Hound.', 'a photo of a drum.', 'a photo of a bulbul.', 'a photo of a envelope.', 'a photo of a corn cob.', 'a photo of a sandal.', 'a photo of a cocktail shaker.', 'a photo of a upright piano.', 'a photo of a bow tie.', 'a photo of a artichoke.', 'a photo of a lifeboat.', 'a photo of a fly.', 'a photo of a tank.', 'a photo of a barrel.', 'a photo of a Pembroke Welsh Corgi.', 'a photo of a armadillo.', 'a photo of a eastern hog-nosed snake.', 'a photo of a King Charles Spaniel.', 'a photo of a cauliflower.', 'a photo of a oscilloscope.', 'a photo of a banjo.', 'a photo of a crutch.', 'a photo of a radio.', 'a photo of a pufferfish.', 'a photo of a electric guitar.', 'a photo of a vending machine.', 'a photo of a ping-pong ball.', 'a photo of a cello.', 'a photo of a Chow Chow.', 'a photo of a lampshade.', 'a photo of a dome.', 'a photo of a cornet.', 'a photo of a dining table.', 'a photo of a Papillon.', 'a photo of a flute.', 'a photo of a coffeemaker.', 'a photo of a chimpanzee.', 'a photo of a mushroom.', 'a photo of a go-kart.', 'a photo of a mashed potatoes.', 'a photo of a tarantula.', 'a photo of a plectrum.', 'a photo of a measuring cup.', 'a photo of a space shuttle.', 'a photo of a pinwheel.', 'a photo of a Alaskan Malamute.', 'a photo of a American dipper.', 'a photo of a missile.', 'a photo of a Siberian Husky.', 'a photo of a hummingbird.', 'a photo of a platypus.', 'a photo of a wild boar.', 'a photo of a Dalmatian.', 'a photo of a split-rail fence.', 'a photo of a music speaker.', 'a photo of a Airedale Terrier.', 'a photo of a vespa.', 'a photo of a agama.', 'a photo of a valley.', 'a photo of a obelisk.', 'a photo of a toaster.', 'a photo of a corn.', 'a photo of a bee eater.', 'a photo of a brass memorial plaque.', 'a photo of a coucal.', 'a photo of a cheeseburger.', 'a photo of a poncho.', 'a photo of a siamang.', 'a photo of a Affenpinscher.', 'a photo of a West Highland White Terrier.', 'a photo of a cockroach.', 'a photo of a T-shirt.', 'a photo of a hay.', 'a photo of a Yorkshire Terrier.', 'a photo of a Toy Poodle.', 'a photo of a three-toed sloth.', 'a photo of a tripod.', 'a photo of a messenger bag.', 'a photo of a Sussex Spaniel.', 'a photo of a Newfoundland dog.', 'a photo of a slug.', 'a photo of a high-speed train.', 'a photo of a small white butterfly.', 'a photo of a sea snake.', 'a photo of a stinkhorn mushroom.', 'a photo of a amphibious vehicle.', 'a photo of a damselfly.', 'a photo of a academic gown.', 'a photo of a rose hip.', 'a photo of a starfish.', 'a photo of a window screen.', 'a photo of a brambling.', 'a photo of a newt.', 'a photo of a holster.', 'a photo of a pizza.', 'a photo of a English Setter.', 'a photo of a baguette.', 'a photo of a ringlet butterfly.', 'a photo of a chain.', 'a photo of a rain barrel.', 'a photo of a lab coat.', 'a photo of a safe.', 'a photo of a fire truck.', 'a photo of a racket.', 'a photo of a bikini.', 'a photo of a koala.', 'a photo of a pencil sharpener.', 'a photo of a St. Bernard.', 'a photo of a lakeshore.', 'a photo of a Polaroid camera.', 'a photo of a Dungeness crab.', 'a photo of a Black and Tan Coonhound.', 'a photo of a pot pie.', 'a photo of a French Bulldog.', 'a photo of a reflex camera.', 'a photo of a bolete.', 'a photo of a jeans.', 'a photo of a steel drum.', 'a photo of a snorkel.', 'a photo of a shovel.', 'a photo of a tile roof.', 'a photo of a jigsaw puzzle.', 'a photo of a European green lizard.', 'a photo of a vacuum cleaner.', 'a photo of a Persian cat.', 'a photo of a taxicab.', 'a photo of a promontory.', 'a photo of a mixing bowl.', 'a photo of a gong.', 'a photo of a ground beetle.', 'a photo of a stove.', 'a photo of a dingo.', 'a photo of a prairie grouse.', 'a photo of a grand piano.', 'a photo of a Old English Sheepdog.', 'a photo of a soap dispenser.', 'a photo of a hair clip.', 'a photo of a photocopier.', "a photo of a potter's wheel.", 'a photo of a wooden spoon.', 'a photo of a chambered nautilus.', 'a photo of a bighorn sheep.', 'a photo of a pier.', 'a photo of a sea lion.', 'a photo of a snoek fish.', 'a photo of a European garden spider.', 'a photo of a balance beam.', 'a photo of a Carolina anole.', 'a photo of a pirate ship.', 'a photo of a ocarina.', 'a photo of a slot machine.', 'a photo of a harmonica.', 'a photo of a mosque.', 'a photo of a cliff dwelling.', 'a photo of a ladle.', 'a photo of a minivan.', 'a photo of a mongoose.', 'a photo of a rocking chair.', 'a photo of a ram (adult male sheep).', 'a photo of a water buffalo.', 'a photo of a proboscis monkey.', 'a photo of a weighing scale.', 'a photo of a power drill.', 'a photo of a soup bowl.', 'a photo of a menu.', 'a photo of a dhole.', 'a photo of a carbonara.', 'a photo of a water bottle.', 'a photo of a Irish Wolfhound.', 'a photo of a Ibizan Hound.', 'a photo of a common gallinule.', 'a photo of a partridge.', 'a photo of a desert grassland whiptail lizard.', 'a photo of a Saharan horned viper.', 'a photo of a Appenzeller Sennenhund.', 'a photo of a electric fan.', 'a photo of a tea cup.', 'a photo of a electric locomotive.', 'a photo of a screw.', 'a photo of a football helmet.', "a photo of a yellow lady's slipper.", 'a photo of a red-breasted merganser.', 'a photo of a mountain.', 'a photo of a Welsh Springer Spaniel.', 'a photo of a military hat (bearskin or shako).', 'a photo of a bison.', 'a photo of a bustard.', 'a photo of a gondola.', 'a photo of a fox squirrel.', 'a photo of a spaghetti squash.', 'a photo of a Afghan Hound.', 'a photo of a purse.', 'a photo of a leopard.', 'a photo of a apron.', 'a photo of a lighter.', 'a photo of a hamster.', 'a photo of a volleyball.', 'a photo of a airplane wing.', 'a photo of a stethoscope.', 'a photo of a consomme.', 'a photo of a beach.', 'a photo of a notebook computer.', 'a photo of a wombat.', 'a photo of a coffee mug.', 'a photo of a Standard Schnauzer.', 'a photo of a red wine.', 'a photo of a sandbar.', 'a photo of a hammer.', 'a photo of a couch.', 'a photo of a tusker.', 'a photo of a llama.', 'a photo of a Flat-Coated Retriever.', 'a photo of a red panda.', 'a photo of a park bench.', 'a photo of a Crock Pot.', 'a photo of a mailbox.', 'a photo of a Otterhound.', 'a photo of a cannon.', 'a photo of a stopwatch.', 'a photo of a Kuvasz.', 'a photo of a hourglass.', 'a photo of a banded gecko.', 'a photo of a stupa.', 'a photo of a hen.', 'a photo of a bittern bird.', 'a photo of a Samoyed.', 'a photo of a gown.', 'a photo of a hot dog.', 'a photo of a Beagle.', 'a photo of a ruler measuring stick.', 'a photo of a leafhopper.', 'a photo of a tick.', 'a photo of a submarine.', 'a photo of a stick insect.', 'a photo of a Komodo dragon.', 'a photo of a English Springer Spaniel.', 'a photo of a black stork.', 'a photo of a dung beetle.', 'a photo of a night snake.', 'a photo of a traffic light.', 'a photo of a maze.', 'a photo of a syringe.', 'a photo of a sea urchin.', 'a photo of a hair dryer.', 'a photo of a oxygen mask.', 'a photo of a airliner.', 'a photo of a meatloaf.', 'a photo of a monitor.', 'a photo of a dock.', 'a photo of a Komondor.', 'a photo of a breastplate.', 'a photo of a rotary dial telephone.', 'a photo of a perfume.', 'a photo of a maypole.', 'a photo of a Malinois.', 'a photo of a flagpole.', 'a photo of a stretcher.', 'a photo of a paddle wheel.', 'a photo of a crash helmet.', 'a photo of a pillow.', 'a photo of a bubble.', 'a photo of a Bluetick Coonhound.', 'a photo of a pool table.', 'a photo of a terrapin.', 'a photo of a dugong.', 'a photo of a altar.', 'a photo of a magpie.', 'a photo of a Shetland Sheepdog.', 'a photo of a kite (bird of prey).', 'a photo of a bell tower.', 'a photo of a laptop computer.', 'a photo of a rapeseed.', 'a photo of a croquet ball.', 'a photo of a punching bag.', 'a photo of a Pomeranian.', 'a photo of a nematode.', 'a photo of a Cardigan Welsh Corgi.', 'a photo of a Cairn Terrier.', 'a photo of a bassinet.', 'a photo of a hartebeest.', 'a photo of a gar fish.', 'a photo of a dragonfly.', 'a photo of a great grey owl.', 'a photo of a chiton.', 'a photo of a CD player.', 'a photo of a computer keyboard.', 'a photo of a sea cucumber.', 'a photo of a waffle iron.', 'a photo of a bulletproof vest.', 'a photo of a ladybug.', 'a photo of a traffic or street sign.', 'a photo of a monastery.', 'a photo of a Arctic fox.', 'a photo of a tent.', 'a photo of a toilet seat.', 'a photo of a strawberry.', 'a photo of a sliding door.', 'a photo of a Bloodhound.', 'a photo of a Italian Greyhound.', 'a photo of a scorpion.', 'a photo of a sloth bear.', 'a photo of a teddy bear.', 'a photo of a Lakeland Terrier.', 'a photo of a one-piece bathing suit.', 'a photo of a conch.', 'a photo of a table lamp.', 'a photo of a Leonberger.', 'a photo of a lynx.', 'a photo of a salt shaker.', 'a photo of a safety pin.', 'a photo of a indigo bunting.', 'a photo of a bucket.', 'a photo of a jay.', 'a photo of a quail.', 'a photo of a yellow garden spider.', 'a photo of a overskirt.', 'a photo of a loggerhead sea turtle.', 'a photo of a gyromitra.', 'a photo of a four-poster bed.', 'a photo of a hornbill.', 'a photo of a Norfolk Terrier.', 'a photo of a home theater.', 'a photo of a fountain pen.', 'a photo of a Bedlington Terrier.', 'a photo of a microphone.', 'a photo of a German Shepherd Dog.', 'a photo of a spotted salamander.', 'a photo of a kimono.', 'a photo of a cauldron.', 'a photo of a common sorrel horse.', 'a photo of a limousine.', 'a photo of a soccer ball.', 'a photo of a tights.', 'a photo of a filing cabinet.', 'a photo of a face powder.', 'a photo of a goldfish.', 'a photo of a arabian camel.', 'a photo of a sink.', 'a photo of a orangutan.', 'a photo of a Christmas stocking.', 'a photo of a barber chair.', 'a photo of a cradle.', 'a photo of a threshing machine.', 'a photo of a frying pan.', 'a photo of a guillotine.', 'a photo of a macaque.', 'a photo of a storage chest.', 'a photo of a garter snake.', 'a photo of a station wagon.', 'a photo of a German Shorthaired Pointer.', 'a photo of a golf ball.', 'a photo of a balaclava ski mask.', 'a photo of a digital watch.', 'a photo of a great egret.', 'a photo of a strainer.', 'a photo of a cuirass.', 'a photo of a fishing casting reel.', 'a photo of a snowmobile.', 'a photo of a rock beauty fish.', 'a photo of a electric ray.', 'a photo of a hockey puck.', 'a photo of a letter opener.', 'a photo of a front curtain.', 'a photo of a moving van.', 'a photo of a African wild dog.', 'a photo of a broccoli.', 'a photo of a skunk.', 'a photo of a handkerchief.', 'a photo of a impala (antelope).', 'a photo of a desk.', 'a photo of a backpack.', 'a photo of a Miniature Pinscher.', 'a photo of a wall clock.', 'a photo of a feather boa.', 'a photo of a Lhasa Apso.', 'a photo of a shoji screen / room divider.', 'a photo of a house finch.', 'a photo of a Border Collie.', 'a photo of a cowboy boot.', 'a photo of a Whippet.', 'a photo of a Norwegian Elkhound.', 'a photo of a modem.', 'a photo of a aircraft carrier.', 'a photo of a hard disk drive.', 'a photo of a carved pumpkin.', 'a photo of a lipstick.', 'a photo of a tray.', 'a photo of a pill bottle.', 'a photo of a praying mantis.', 'a photo of a alligator lizard.', 'a photo of a automated teller machine.', 'a photo of a American bullfrog.', 'a photo of a quilt.', 'a photo of a cloak.', 'a photo of a howler monkey.', 'a photo of a window shade.', 'a photo of a tobacco shop.', 'a photo of a dishcloth.', 'a photo of a English foxhound.', 'a photo of a sleeping bag.', 'a photo of a flatworm.', 'a photo of a infant bed.', 'a photo of a necklace.', 'a photo of a boathouse.', 'a photo of a macaw.', 'a photo of a ring-tailed lemur.', 'a photo of a radio telescope.', 'a photo of a bullock cart.', 'a photo of a shower curtain.', 'a photo of a piggy bank.', 'a photo of a forklift.', 'a photo of a black-and-white colobus.', 'a photo of a Scottish Deerhound.', 'a photo of a baby pacifier.', 'a photo of a barbershop.', 'a photo of a assault rifle.', 'a photo of a spindle.', 'a photo of a remote control.']
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: 19,157
* accuracy: 76.63%
* error: 23.37%
* macro_f1: 76.16%
